m. tech in computer science and...

BANGALORE UNIVERSITY

Department of Computer Science and Engineering

UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING

K R Circle, Bengaluru-560 001.

Choice Based Credit System (CBCS)-2018

M. Tech in Computer Science and Engineering

CS1


VISION

―To strive for excellence in education for the realization of a vibrant and inclusive

society through knowledge creation and dissemination‖

MISSION

Impart quality education to meet national and global challenges

Blend theoretical knowledge with practical skills

Pursue academic excellence through high quality research and publications

Provide access to all sections of society to pursue higher education

Inculcate right values among students while encouraging competitiveness to

promote leadership qualities

Produce socially sensitive citizens

Hasten the process of creating a knowledge society

To contribute to nation building

CS2

Bangalore University UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING

K R Circle, Bengaluru – 560 001.

University Visvesvaraya College of Engineering (UVCE) was started as a School of Mechanical

Engineering by Bharat Ratna Sir. M. Visvesvaraya in the year 1913 to meet the needs of the State for

skilled workers with S V Setty as its Superintendent. Later, it was converted to a full-fledged

Engineering College in the year 1917 under the name Government Engineering College and was

affiliated to the University of Mysore. It is the fifth Engineering College to be established in the country.

After the formation of Bangalore University in 1964, UVCE became one of the Constituent

Colleges of Bangalore University. This is one of the oldest Institutions in the country imparting

technical education leading to B.E., M.E, B.Arch., M.Sc. (Engineering), M.Arch. and Ph.D. degrees in

various disciplines of Engineering and Architecture. The Institution currently offers 7 Undergraduate

(B.E. / B.Arch.) Full-time, three Undergraduate (B.E.) Part-time and 24 Postgraduate (M.E. / M.Arch.)

Programmes.

VISION

The vision of UVCE is to strive for excellence in advancing engineering education through path

breaking innovations across the frontiers of human knowledge to realize a vibrant, inclusive and humane

society.

MISSION

The mission of UVCE is to prepare human resource and global leaders to achieve the above vision

through discovery, invention and develop friendly technologies to promote scientific temper for a

healthy society. UVCE shapes engineers to respond competently and confidently to the economic, social

and organizational challenges arising from globally advancing technical needs.

CS3

Bangalore University Bengaluru

Department of Computer Science and Engineering, UVCE, Bengaluru

M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme - 2K18

Vision of the Department

Strive for Centre of Excellence in advancing Computer Science and Engineering education to produce

highly qualified human resources to meet local and global requirement.

Mission of the Department

CSM1. Implementing effectively, the outcome based education by imparting knowledge of basics and

advances in Computer Science and Engineering and other allied disciplines.

CSM2. Preparing and equipping human resources to become global leaders through innovation,

discovery, sustainable and environment friendly technology.

CSM3. Creating conducive environment for effective teaching and learning process through

interdisciplinary research, online courses, interaction with institutions of higher learning and industries, R

and D laboratories of national importance, alumni, employers and other internal & external stake holders.

CSM4. Imbibing awareness of entrepreneurship, ethics, honesty, credibility, social and environmental

consciousness and providing opportunity to the faculty and technical staff for continuous academic

improvement and to equip them with then latest trends in Software Engineering and thereby inculcate the

habit of continuous learning in faculty, staff and students.

Program Outcomes:

Computer Science and Engineering Graduates will be able to:

CSPO1: An ability to independently carry out research/investigate and development work to solve

practical problems

CSPO2: An ability to write and present a substantial technical report/document

CSPO3: Students should be able to demonstrate a degree of mastery over the area as per the

specialization of the problem. The mastery should be at a level higher than the requirements in the

appropriate bachelor degree

CS4

Program Educational Objectives (PEO)

M. Tech (Computer Science Engineering)

After successful completion of the program, the graduates will be

CSPEO 1: Able to apply concepts of mathematical foundation and computing to Computer Science

and Engineering

CSPEO 2: Able to design and develop interdisciplinary and innovative systems.

CSPEO 3: Able to inculcate effective communication skills, team work, ethics, leadership in

preparation for a successful career in industry and R&D organizations.

CS5

BANGLORE UNIVERSITY

SCHEME OF STUDIES AND EXAMINATION FOR 24MONTHS COURSE FOR THE AWARD OF

M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme – 2K18

Semester I Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits

Course Code Hrs/Week DPT Hrs/week Marks Marks

L T P S

1 18CS1C01 Mathematical Foundations of Computer Science 3 1 0 0 CSE 4 50 50 4

2 18CS1C02 Advances in Computer Networks 4 0 0 0 CSE 4 50 50 4

3 18CS1C03 Advanced Database Management Systems 4 0 0 0 CSE 4 50 50 4

18CS1E1A Cloud Computing 4 0 0 0 CSE

4 18CS1E1B Mobile Computing 4 0 0 0 CSE 4 50 50 4

18CS1E1C Wireless Networks 4 0 0 0 CSE

18CS1E2A Soft Computing 3 0 2 0 CSE

5 18CS1E2B Advances in Storage Area Networks 4 0 0 0 CSE 4 50 50 4

18CS1E2C Advanced Computer Architecture 4 0 0 0 CSE

6 18CS1L01 Network Programming Lab 0 0 4 0 CSE 4 50 50 2

7 18CS1M01 Research Methodology and IPR. 2 0 0 0 CSE 2 50 50 2

8 18CS1S01 Seminar -I 0 0 2 0 CSE 2 50 -- 1

9 18CS1M02 Audit Course-I (Technical Paper Writing) 2 0 0 0 English 2 50 -- 1

Total 30 450 350 26

*SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.

CS6

Semester II Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits


L T P S

1 18CS2C01 Advanced Data Structures and Algorithms 4 0 0 0 CSE 4 50 50 4

2 18CS2C02 Advanced Operating Systems 4 0 0 0 CSE 4 50 50 4

3 18CS2C03 Advances in Digital Image Processing 4 0 0 0 CSE 4 50 50 4

4

18CS2E1A Data Warehousing and Mining 4 0 0 0 CSE

4

50

50

4

18CS2E1B Stochastic Process and Queuing Theory 4 0 0 0

18CS2E1C Internet of Things 3 0 2 0

5

18CS2E2A Network Security 4 0 0 0

CSE 4 50 50 4 18CS2E2B Pattern Recognition 4 0 0 0

18CS2E2C Web Security 4 0 0 0

6 18CS2L01 Advanced Data Structures and Algorithms Lab 0 0 4 0 CSE 4 50 50 2

7 18CS2S01 Seminar -II 0 0 2 0 CSE 2 50 -- 1

8 18CS2M01 Audit Course-II (Pedagogy Studies) 2 0 0 0 CSE 2 50 -- 1

Total 28 400 300 24

Semester III Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits


L T P S

1 2

18CS3E1A Machine Learning 4 0 0 0 CSE

CSE

CSE

4

4

50

50

50

50

4

4

18CS3E1B Big Data Analytics 3 0 2 0

18CS3E1C High Performance Computing

4 0 0 0

Open Elective

3 18CS3S01 Seminar -III 0 0 2 0 CSE 2 50 1

4 18CS3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5

5 18CS3D01 Dissertation Phase -I 0 0 10 0 CSE 10 50 50 5

Total 30 250 200 19

CS7

Semester IV Sl. No Course Type/ Course Name Teaching Scheme Teaching Total CIE *SEE Credits


L T P S

1 18CS4S01 Seminar -IV 0 0 2 0 CSE 2 50 1

2 18CS4D01 Dissertation Phase -II 0 0 30 0 CSE 30 50 50 15

Total -- -- 32 -- 32 100 50 16

1 18CSMOOC MOOC Course 0 0 0 0 03

Grand Total of Credits 88

COURSE TYPE

CS: COMPUTER SCIENCE C: PROFESSIONAL CORE E: PROFESSIONAL ELECTIVE

P: OPEN ELECTIVE M: MANDATORY AUDIT L: LABORATORY

S: SEMINAR I: INTERNSHIP/ MINI PROJECT D: DISSERTATION

L – Theory lecture, T – Tutorial, P – Lab work, S – Self-study:

Numbers under teaching scheme indicates contact clock hours.

CS8

Open Elective

Sl. No Course Type /

Course Code Course Name

Teaching Scheme (No. of hrs per week)

Teaching

Dept.

Total hrs

/ week

CIE

Marks

*See

Marks Credits

L T P S

1

18CS3P1A Artificial Intelligence

4 0 0 0 CSE 4 50 50 4 18CS3P1B Business Analytics

18CS3P1C Modeling and Simulation

2

18CV3P1A Significance of National Building Codes

4 0 0 0 Civil 4 50 50 4

18CV3P1B Water Laws, Rights and Administration

18CV3P1C Waste to Energy

18CV3P1D Remote Sensing and Geographic Information

System

3 18ME3P1A Composite and Smart Materials

4 0 0 0 Mech 4 50 50 4 18ME3P1B Industrial Safety

4

18EE3P1A Real Time Embedded Systems

4 0 0 0 EEE 4 50 50 4 18EE3P1B Robotics and Automation

18EE3P1C Solar and Wind Energy

5

18EC3P1A Reliability and Engineering

4 0 0 0 ECE 4 50 50 4 18EC3P1B M-Commerce and Applications

18EC3P1C Optimization Techniques

CS9

Course Code 18CS1C01 M. Tech (Computer Science and Engineering)

Category Engineering Science Courses (Theory - Professional Core)

Course title MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE

Scheme and

Credits

No. of Hours/Week Semester – I

L T P S Credits

3 1 - - 4

CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs

Prerequisites (if any):

1. Basics of probability

2. Basics of graph theory

COURSE OBJECTIVES

The course will enable the students to:

1. Understand the concepts of number theory and solve related problems.

2. Apply the concepts of stochastic process and queuing theory required to devise

analytical models for the real problems of computer science.

3. Analyse the various concepts of arranging, selecting and combining objects from a

set.

4. Understand the concept of advanced graph theory that can be used to model any

network, physical or conceptual.

UNIT -I NUMBER THEORY: 10 Hours

The Division algorithm, the Greatest Common Divisor, the Euclidean algorithm. The basic

properties of Congruencies, Binary and decimal representation of integer, linear congruence,

Chinese-Reminder Theorem, Fermat‘s Little theorem, The sum and number of Divisors, The

Mobius inversion formula, The Greatest integer function (No theorem proofs).

UNIT -II PROBABILITY AND QUEUING THEORY: 10 Hours

Random Variables, Probability Distribution, Binomial Distribution, Poisson Distribution,

Geometric Distribution, Exponential Distribution, Normal Distribution, Uniform

Distribution. Two Dimensional Random Variables. Introduction to Stochastic Processes,

Markov process, Markov chain, one step and n-step Transition Probability, Chapman

Kolmogorov theorem (Statement only), Transition Probability Matrix, Classification of

States of a Markov chain. Introduction to Markovian queuing models, Single Server Model

with Infinite system capacity, Characteristics of the Model (M/M/1): (∞/FIFO), Single

Server Model with Finite System Capacity, Characteristics of the Model (M/M/1):

(K/FIFO).

UNIT -III COMBINATORICS: 10 Hours

Basics of Counting: Permutations, Permutations with Repetitions, Circular Permutations,

Restricted Permutations, Combinations: Restricted Combinations, Generating Functions of

Permutations and Combinations, Binomial and Multinomial Coefficients, Binomial and

Multinomial Theorems, The Principles of Inclusion Exclusion, Pigeonhole Principle and its

Application.

UNIT -IV RECURRENCE RELATIONS: 09 Hours Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of

Generating Functions, Recurrence Relations, Formulation as Recurrence Relations, Solving

Recurrence Relations by Substitution and Generating Functions, Method of Characteristic

Roots, Solving Inhomogeneous Recurrence Relations.

UNIT –V GRAPH THEORY: 09 Hours

Basic Concepts of Graphs, Sub graphs, Matrix Representation of Graphs: Adjacency

Matrices, Incidence Matrices, Isomorphic Graphs, Paths and Circuits, Eulerian and

Hamiltonian Graphs, Multi-graphs, Planar Graphs, Euler‗s Formula, Graph Colouring and

CS10

Covering, Chromatic Number, Spanning Trees, Algorithms for Spanning Trees (Concepts

and Problems Only, Theorems without Proofs).

UNIT-VI Recent advances and research being done in the topics mentioned above

units

REFERENCES

1. David M Burton, ―Elementary Number Theory‖, Allyn and Bacon, 1980.

2. K. S. Trivedi, ―Probability and Statistics with Reliability, Queuing for Computer

Science Applications‖, John Wiley and Sons, II Edition, 2008.

3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, ―Queuing Networks

and Markov Chains‖, John Wiley and Sons, II Edition, 2006.

4. Richard A Brualdi, Introductory Combinatorics 5th

Edition, Pearson 2009

5. J. A. Bondy and U. S. R. Murty, ―Graph Theory and Applications‖, Macmillan

Press, 1982.

COURSE OUTCOMES

On completion of the course, the student would be able to:

CO1. Solve problems related to number theory.

CO2: Design the analytical models using the concepts of probability and stochastic process.

CO3: Compare the various methods of counting using permutations and combinations.

CO4: Solve the problems of recurrence relations.

CO5: Apply the graph theory concepts in solving problems related to computer science.

Scheme of Examination

CIE -50

Marks

Test I (unit I,II, & III)-15

Test II (Unit IV & V) -15

Quiz/AAT=05 Marks

Unit-VI(AAT)=15 Marks

Total: Marks

50

SEE-

100

Marks

Unit which have 9 hours shall not have internal

choice

20*2=40

Marks Total:

Marks 100 Unit which have 10 hours shall have internal

choice

20*3=60

Marks

Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced

for 50 marks

Mapping of Course Outcomes (COS) to Program Outcomes (POs)

PO1 PO2 PO3

CO1 1 CO2 2 CO3 1 1 CO4 1 CO5 2

1: Low 2: Medium 3:High

CS11


Category Engineering Science Courses

Course title ADVANCES IN COMPUTER NETWORKS

Scheme and

Credits

No. of

Hours/Week

Semester – I

L T P SS Credits

4 0 - - 4

CIE Marks:

50

SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3

Hrs


COURSE OBJECTIVE

The course will enable the student to:

1. Understand the requirement of various high speed networks

2. Learn the effect of congestion and its control.

3. Understand Network Traffic Management for reliable delivery.

4. Understand integrated and differentiated architecture and services.

5. Learn the effect of traffic in the networks on various QoS parameters

UNIT I- INTRODUCTION 9 Hours

OSI and TCP/ IP Reference Model, RS-232C, RS-449, Devices used in Physical Layers,

Framing, Flow and Error Control, Error Detection and Correction, Checksum, Sliding

Window Protocols-ARQ.

UNIT II- DATA LINK LAYER 10 Hours Multiple Access Control Protocols(MAC), ALOHA, CSMA/CD (802.3), Data Link

Protocol- HDLC,PPP, Wired LAN‘s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,

Wireless LAN‘s(802.11), Broadband Wireless(802.16).

UNIT III- ROUTING AND CONGESTION CONTROL 10 Hours Design issues, Routing Algorithms, Distance Vector Routing, Link State Routing, Routing

in Mobile Host, Broadcast Routing, Reverse Path Forwarding, OSPF, IP Protocols (IPv4 -

ARP, RARP, IPv6), Queuing Analysis- Queuing Models – Single Server Queues –

Effects of Congestion – Congestion Control – Traffic Management – Congestion Control

in Packet Switching Networks.

UNIT IV – TRANSPORT LAYER AND INTEGRATED SERVICES 10 Hours

TCP Header, TCP Flow control – TCP Congestion Control – Retransmission – Timer

Management – Exponential RTO back-off – KARN‘s Algorithm – Window

management. Integrated Services Architecture – Approach, Components, Services-

Queuing Discipline, FQ, PS, BRFQ, GPS, WFQ – Random Early Detection,

Differentiated Services.

UNIT V - PROTOCOLS FOR QOS SUPPORT 09 Hours

RSVP – Goals & Characteristics, Data Flow, RSVP operations, Protocol

Mechanisms – Multiprotocol Label Switching – Operations, Label Stacking, Protocol

details – RTP – Protocol Architecture, Data Transfer Protocol, RTCP.

UNIT VI- To understand latest innovative networks such as Software Defined

Networks(SDN).

REFERENCES

1. Behrouz A Forouzan and Firouz Mosharraf, ―Computer Networks, A Top-Down

Approach‖, TMH, 2012.

2. Andrew S. Tanenbaum and David J. Wetherall, ―Computer Networks‖, Pearson Education, 5th

Edition,2011.

https://www.amazon.com/s/ref=rdr_ext_aut?_encoding=UTF8&index=books&field-author=Andrew%20S.%20Tanenbaum

https://www.amazon.com/s/ref=rdr_ext_aut?_encoding=UTF8&index=books&field-author=David%20J.%20Wetherall

https://www.amazon.com/dp/0132126958/ref=rdr_ext_tmb


CS12

3. William Stallings, ―High Speed Networks and Internet‖, , Second Edition, 2012.

4. Prakash C Guptha, ―Data Communication and Computer Networks‖, PHI , 6th

printing 2012.

5. Larry L. Peterson and Bruce S Davis , ―Computer Network A System

Approach‖, Elsevier, 5th

edition 2010.

COURSE OUTCOMES

On Completion of the course, the student will be able to:

CO1: Apply the networking principles to manage the network traffic.

CO2: Control the various anomalies in the network to improve the QoS.

CO3: Study the relation and effect of one QoS parameter on the other.

CO4: Apply the efficient techniques to achieve effective and reliable communication.

CO5: Develop new protocols to mitigate emerging problems.


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks

Unit which have 10 hours shall have internal

choice

20*3=60

Marks

Total:

Marks 100 Unit which have 09 hours shall not have

internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3 2 2

CO4 3 2

CO5 2 2 2

1. Low, 2. Medium, 3. High

CS13



Course title ADVANCED DATABASE MANAGEMENT SYSTEMS

Scheme and Credits No. of Hours/Week Semester – I

L T P S Credits

4 0 - - 4



COURSE OBJECTIVES:


1. Remembering the basics of database management systems.

2. Understanding the concepts of object relational databases and XML

3. Evaluate database security strategies.

4. Applying the concepts of Data Storage and Querying.

5. Understanding distributed, parallel databases and recent technologies

UNIT- I INTRODUCTION 09 Hours

Data models, schemas and instances, three schema architecture and data independence,

database languages and interfaces, database environment. ER model: entity types, entity sets,

attributes and keys, relationship types, relationship sets, roles and structural constraints, ER

Diagrams. SQL3 - Overview of the SQL Query Language, SQL Data Definition, Basic

Structure of SQL Queries, Additional Basic Operations, Set Operations, Null Values,

Aggregate Functions, Nested Subqueries.

UNIT-II OBJECT AND OBJECT RELATIONAL DATABASES 10 Hours Object oriented concepts, object identity, object structure and type constructors, encapsulation

of operations, methods and persistence, class hierarchies and inheritance, object model of

ODMG, object definition language, object query language.XML: Structured, Semi structured,

and Unstructured Data, Data Model, Documents, DTD, XML Schema, Storing and Extracting

XML Documents from Databases, XML Languages.

UNIT-III DATABASE SECURITY 09 Hours Issues, discretional access control and role base access control, SQL Injection, statistical

database security, public key infrastructure, privacy issues and preservation, Oracle Label-

Based Security

UNIT- IV INDEXING AND HASHING 10 Hours Basic Concepts, Ordered Indices, B + -Tree Index Files, B + -Tree Extensions, Multiple-Key

Access, Static Hashing, Dynamic Hashing, Comparison of Ordered Indexing and Hashing,

Bitmap Indices. Query Processing: Overview, Measures of Query Cost, Selection Operation,

Sorting, Join Operation, Evaluation of Expressions. Query Optimization: Overview,

Transformation of Relational Expressions, Estimating Statistics of Expression Results, Choice

of Evaluation Plans, Materialized Views.

UNIT-V PARALLEL AND DISTRIBUTED DATABASES 10 Hours Parallel Databases: Introduction, I/O Parallelism, Interquery Parallelism, Intraquery

Parallelism, Intraoperation Parallelism, Interoperation Parallelism, Query Optimization, Design

of Parallel Systems, Parallelism on Multicore Processors. Distributed Databases:

Homogeneous and Heterogeneous Databases, Distributed Data Storage, Distributed

Transactions, Commit Protocols, Concurrency Control in Distributed Databases, Availability,

Distributed Query Processing, Heterogeneous Distributed Databases, Cloud-Based Databases,

CS14

Directory Systems.

UNIT-VI RECENT TECHNOLOGIES

Latest technologies such as NoSQL, BigData, Multimedia Databases, Mobility and Personal

Databases

REFERENCES

1. Elmasri and Navathe, Fundamentals of Database Systems, 7th

edition, Pearson, 2016.

2. A. Silberschatz, H. F. Korth and S. Sudarshan, Database system concepts 6th ed. 2011

3. Raghu Ramakrishnan, Database Management System, McGraw Hill, 3rd

edition, 2003.

4. Ceri and Pelagatti, Distributed Databases: Principles and Systems, Tata McGraw Hill, 2008,

5. C.J.Date, A.Kannan and S.Swamynathan, An introduction to Database System, Pearson

Education, 8th

edition, 2009.

6. Dr. P.S. Deshpande, SQL and PL/SQL for Oracle log, Black Books Dreamtech Press.

COURSE OUTCOMES

Upon completion of the course, the students would be able to: CO1: State and identify the key concepts of database management systems

CO2: Design and implement object relational databases.

CO3: Determine the different strategies for database security and key issues.

CO4: Apply the concepts of query optimization and indexing.

CO5: Illustrate distributed and parallel database technologies.

SCHEME OF EXAMINATION

CIE –

50

marks

Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5

marks

Unit VI(AAT)=15

marks

Total:50

marks Test II (Unit IV & V) – 15 marks

SEE

– 100

marks

Answer FIVE full questions

Units which have 09 Hours shall not have

internal choice. 20* 2 = 40 Marks

Total:100

marks Units which have 10 Hours shall have

internal choice 20*3= 60 Marks

Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50

marks..


PO1 PO2 PO3

CO1 1

CO2 1

CO3 1

CO4 1 2

CO5 2


CS15

Course Code 18CS1E1A M. Tech (Computer Science and Engineering)

Category Engineering Science Courses (Theory - Professional Elective)

Course title CLOUD COMPUTING


L T P S Credits

4 0 - - 4



1. Operating systems

2. Basics of distributed computing

COURSE OBJECTIVES


1. Understand the various cloud service providers and cloud interoperability

2. Apply the cloud computing applications and paradigms

3. Analyse the concept of virtualization

4. Acquire the knowledge of cloud resource management and scheduling mechanism

5. Learn various security issues in cloud computing

UNIT-I CLOUD INFRASTRUCTURE 09 Hours

Cloud computing at Amazon, Cloud computing-the Google perspective, Microsoft Windows

Azure and Online services, Open-Source Software Platforms for Private Clouds Cloud Storage

Diversity and Vendor Lock-in, Cloud Computing Interoperability: The Intercloud, Service- and

Compliance-Level Agreements, Responsibility Sharing Between User and Cloud Service

Provider, User Experience, Software Licensing.

UNIT- II CLOUD COMPUTING: APPLICATIONS AND PARADIGMS 09 Hours Challenges for Cloud Computing, Existing Cloud Applications and New Application

Opportunities Architectural Styles for Cloud Applications, Workflows: Coordination of Multiple

Activities, Coordination Based on a State Machine Model: The ZooKeeper, The MapReduce

Programming Model, A Case Study: The GrepTheWeb Application, High-Performance

Computing on a Cloud.

UNIT-III CLOUD VIRTUALIZATION 10 Hours Virtualization, Layering and Virtualization, Virtual Machine Monitors, Virtual Machines,

Performance and Security Isolation, Full Virtualization and Paravirtualization, Hardware Support

for Virtualization, Case Study: Xen, a VMM Based on Paravirtualization, Optimization of

Network Virtualization in Xen 2.0, vBlades: Paravirtualization Targeting an x86-64 Itanium

Processor, A Performance Comparison of Virtual Machines.

UNIT-IV CLOUD RESOURCE MANAGEMENT AND SCHEDULING 10 Hours Policies and Mechanisms for Resource Management, Applications of Control Theory to Task

Scheduling on a Cloud, Stability of a Two-Level Resource Allocation Architecture, Feedback

Control Based on Dynamic Thresholds, Coordination of Specialized Autonomic Performance

Managers, A Utility-Based Model for Cloud-Based Web Services, Resource Bundling:

Combinatorial Auctions for Cloud Resources, Scheduling Algorithms for Computing Clouds,

Fair Queuing, Start-Time Fair Queuing, Borrowed Virtual Time Cloud Scheduling Subject to

Deadlines, Scheduling MapReduce Applications Subject to Deadlines, Resource Management

and Dynamic Application Scaling.

UNIT-V CLOUD SECURITY 10 Hours Cloud Security Risks, Security: The Top Concern for Cloud Users, Privacy and Privacy Impact

Assessment, Trust Operating System Security, Virtual Machine Security, Security of

Virtualization, Security Risks Posed by Shared Images, Security Risks Posed by a Management

OS.

CS16

UNIT-VI Recent developments and current research in multi cloud, cloud security, mobile

cloud computing.

REFERENCES

1. Dan C Marinescu, ―Cloud Computing: Theory and Practice‖, Morgan

Kaufmann/Elsevier. 2013.

2. George Reese, ―Cloud Application Architectures: Building Applications and

Infrastructure in the Cloud‖, O‘Reilly, 2009.

3. Rajkumar Buyya, James Broberg and Andrzej M. Goscinski, ―Cloud Computing:

Principles and Paradigms‖, Wiley, 2011.

4. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, ―Distributed and Cloud Computing: From

Parallel Processing to the Internet of Things‖, Morgan Kaufmann Publishers, 2012.

COURSE OUTCOMES

Upon completion of the course, the students would be able to:

CO1: Categorize the architectures, services and delivery models in cloud computing

CO2: Implement the concept of virtualization and its management in cloud computing

CO3: Design the extended functionalities of resource management and scheduling mechanisms

CO4: Analyse the security models in cloud environment

CO5: Investigate recent developments in multi cloud, cloud security and mobile cloud computing


CIE –

50

marks

Test I (Unit I, II &III) - 15 marks Quiz / AAT = 5

marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks



internal choice. 20* 2 = 40 Marks Total:100

marks

Units which have 10 Hours shall have



marks.


PO1 PO2 PO3

CO1 2 2

CO2 2

CO3 1 2

CO4 2 1

CO5 2 2


CS17

Course Code 18CS1E1B M. Tech (Computer Science and Engineering)


Course title MOBILE COMPUTING


L T P S Credits

4 0 - - 4



1. Computer Networks

2. Database Management Systems

3. Operating Systems

COURSE OBJECTIVES:


1. Understand the GSM architecture, services and protocols.

2. Understand the wireless MAC, mobile IP and transport layer functions and protocols.

3. Analyse the concepts of mobile databases, data dissemination, broadcasting systems and data

synchronization.

4. Review various mobile technologies including WLAN, WiFi, WAP, Bluetooth, Zigbee.

5. Understand mobile application languages and mobile operating systems

UNIT- I MOBILE COMPUTING ARCHITECTURE AND GSM 09 Hours

Mobile Computing Architecture: Types of Networks, Architecture for Mobile Computing, 3-tier

Architecture, Design Considerations for Mobile Computing. GSM: Services and System Architectures,

Radio Interfaces, Protocols, Localization, Calling, Handover, General Packet Radio Service.

UNIT-II WIRELESS MAC, IP and TRANSPORT LAYER 10 Hours

Medium Access Control, Introduction to CDMA based Systems, IP and Mobile IP Network Layers,

Packet Delivery and Handover Management, Location Management, Registration, Tunnelling and

Encapsulation, Route Optimization, Dynamic Host Configuration Protocol. Indirect TCP, Snooping

TCP, Mobile TCP, Other Methods of TCP.

UNIT-III DATABASES, DATA DISSEMINATION AND BROADCASTING SYSTEMS

10 Hours

Database Hoarding Techniques, Data Caching, Client – Server Computing and Adaptation,

Transactional Models, Query Processing, Data Recovery Process, Issues relating to Quality of Service.

Communication Asymmetry, Classification of Data – Delivery Mechanisms, Data Dissemination

Broadcast Models, Selective Tuning and Indexing Techniques, Digital Audio Broadcasting, Digital

video Broadcasting.

UNIT-IV DATA SYNCHRONIZATION IN MOBILE COMPUTING SYSTEMS 09 Hours

Synchronization, Synchronization Protocols, SyncML – Synchronization Language for Mobile

Computing, Synchronized Multimedia Markup Language (SMIL). –

UNIT-V MOBILE DEVICES, SERVER AND MANAGEMENT AND MOBILE APPLICATION

LANGUAGES 10 Hours

Wireless LAN, Mobile Internet Connectivity and Personal Area Network, Mobile agent, Application

Server, Gateways, Portals, Service Discovery, Device Management, Mobile File Systems. Wireless

LAN (Wi-Fi) Architecture and Protocol Layers, WAP 1.1 and WAP 2.0 Architectures, Bluetooth –

enabled Devices Network, Zigbee. XML, JAVA, J2ME and JAVACARD, Mobile Operating Systems:

Introduction, PalmOS, Windows CE, Symbian OS, Linux for Mobile Devices.

UNIT-VI Recent trends in wireless and mobile network security, mobile cloud computing.

CS18

REFERENCES

1. Raj Kamal, ―Mobile Computing‖, Oxford University Press, 2007.

2. Ashok Talukder, Ms Roopa Yavagal, and Mr. Hasan Ahmed, ―Mobile Computing,

Technology, Applications and Service Creation‖, II Edition, Tata McGraw Hill, 2010.

3. Jochen Schiller, ―Mobile Communications‖, Addison-Wesley. II Edition, 2004.

4. Hansmann, Merk, Nicklous, Stober, ―Principles of Mobile Computing‖, Springer, II Edition,

2003.

COURSE OUTCOMES

Upon completion of the course, the student would be able to:

CO1: Demonstrate the knowledge of GSM architecture, services and protocols.

CO2: Simulate a typical GSM network and demonstrate the performance analysis.

CO3: Extending the functionalities of mobile IP and transport layer protocols.

CO4: Apply the mobile application languages to design mobile applications.

CO5: Investigate recent developments in wireless, mobile network security and mobile cloud

computing.


CIE –

50

marks

Test I (Unit I, II &III) - 15 marks Quiz / AAT = 5

marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks



Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.


PO1 PO2 PO3

CO1 2

CO2 2

CO3 2

CO4 2 2

CO5 2 2


CS19

Course Code 18CS1E1C M. Tech (Computer Science and Engineering)


Course title WIRELESS NETWORKS

Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks: 50 SEE Marks:

50

Total Max. Marks: 100 Duration of SEE: 3 Hrs


Computer Networks

COURSE OBJECTIVES:


1. Get familiar with the wireless market and the future needs and challenges.

2. Learn the key concepts of wireless networks, standards, technologies and their

basic operations

3. Know various generations of cellular networks and learn cellular architecture

4. Understand the key concept of sensor networks

5. Analyse security techniques and vulnerabilities


Wireless Networking Trends, Key Wireless Physical Layer Concepts, Multiple Access

Technologies -CDMA, FDMA, TDMA, Spread Spectrum technologies, Frequency reuse,

Radio Propagation and Modelling, Challenges in Mobile Computing: Resource poorness,

Bandwidth, energy etc.

UNIT-II WIRELESS LOCAL AREA NETWORKS 10 Hours

IEEE 802.11 Wireless LANs Physical & MAC layer, 802.11 MAC Modes (DCF & PCF)

IEEE 802.11 standards, Architecture & protocols, Infrastructure vs. Adhoc Modes, Hidden

Node & Exposed Terminal Problem, Fading Effects in Indoor and outdoor WLANs,

WLAN Deployment issues.

UNIT- III WIRELESS CELLULAR NETWORKS 10 Hours

1G and 2G, 2.5G, 3G, and 4G, Mobile IPv4, Mobile IPv6, TCP over Wireless Networks,

Cellular architecture, Frequency reuse, Channel assignment strategies, Handoff strategies,

Interference and system capacity, Improving coverage and capacity in cellular systems

UNIT- IV WIRELESS SENSOR NETWORKS 10 Hours

Introduction, Application, Physical, MAC layer and Network Layer, Power Management,

Tiny OS Overview. Wireless Pans Bluetooth and Zigbee, Introduction to Wireless

Sensors networks, deployment, key design challenges, network deployment, Routing

protocols, routing challenges and design issues, routing strategies.

UNIT-V SECURITY 09 Hours

Security in wireless Networks, Vulnerabilities, Security techniques, Wi-Fi Security, DoS

in wireless communication.

UNIT-VI RECENT TRENDS Recent trends in Wireless networks, Vehicular Adhoc Networks.

CS20

REFERENCES

1. Schiller J., Mobile Communications, Addison Wesley 2000

2. Stallings W., Wireless Communications and Networks, Pearson Education 2005

3. Stojmenic Ivan, Handbook of Wireless Networks and Mobile Computing, John Wiley

and Sons Inc 2002

4. Yi Bing Lin and Imrich Chlamtac, Wireless and Mobile Network Architectures, John

Wiley and Sons Inc 2000

5. Pandya Raj, Mobile and Personal Communications Systems and Services, PHI 2000

6.Feng Zhao, leonidas Guibas, ―Wireless sensor Networks: An information processing

approach‖, Elsevier, 2004

COURSE OUTCOMES

Upon completion of the course, the students will be able to:

CO1: Demonstrate advanced knowledge of networking and wireless networking

CO2: Understand various types of wireless networks, standards, operations and use cases.

CO3: Be able to design and compare cellular based upon underlying propagation and

performance analysis.

CO4: Demonstrate knowledge of WPAN and sensor networks

CO5: Assess security measure and vulnerabilities.


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks



Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for

50 marks


PO1 PO2 PO3

CO1 2 3

CO2 2 3

CO3 2 3

CO4 3 3

CO5 1 3


CS21


Category Engineering Science Courses (Integrated - Professional

Elective)

Course title SOFT COMPUTNG

Scheme and

Credits


L T P S Credits

3 0 2 0 4



1. Basic knowledge of mathematics

COURSE OBJECTIVES:


1. Describe soft computing concepts and techniques and foster their abilities in

designing appropriate technique for a given scenario.

2. Choose Neural network algorithms for real – world problems.

3. Analyse and compare the different Optimization techniques.

4. Develop the applications of Genetic Algorithms in Machine Learning.

5. Provide a hands-on experience on MATLAB to implement various strategies

UNIT-I INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS

09 Hours

Evolution of Computing: Soft Computing Constituents, Conventional AI to Computational

Intelligence: Machine Learning Basics, Hard-Margin and Soft-Margin SVMs- Concepts of

Kernel and Feature Spaces, Basics of Optimization and Quadratic programming,

Introduction to Steganography and Applications of SVMs to Steganalysis

UNIT-II NEURAL NETWORKS 10 Hours Introduction

to ANN, Architectures, Learning methods, Bayesian Networks, Back Propagation network,

Perceptrons, Hopfield Networks, Kohonen Self Organizing Feature Maps, Chaos Theory

UNIT-III OPTIMIZATION TECHNIQUES 09 Hours Introduction, Elitism based Ant Colony Optimization, Min-Max based Ant Colony

Optimization, Particle Swarm Optimization, Artificial Bee Colony Optimization, Multi-

Swarm Optimization, Cuckoo Search, Whole Optimization, Firefly algorithm, Bat

Algorithm, Introduction to missing data-Imputation techniques, Principal Component

Analysis, Gradient Descent

UNIT-IV GENETIC ALGORITHMS and FUZZY LOGIC 10 Hours

Introduction to Genetic Algorithms (GA), Applications of GA in Machine Learning:

Machine Learning Approach to Knowledge Acquisition. Fuzzy Logic: Fuzzy Sets,

Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions: Fuzzy Rules and

Fuzzy Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision

Making, Defuzzification

UNIT-V Matlab Lib 10 Hours

Introduction to Matlab, Arrays and array operations, Functions and Files, Study of neural

network toolbox and fuzzy logic toolbox, Simple implementation of Artificial Neural

Network and Fuzzy Logic

UNIT-VI

Recent Trends in deep learning, various classifiers, neura1 networks and genetic algorithm.

Implementation of recently proposed soft computing techniques

CS22

UNIT-VII (Lab Programs)

1. a) Write a MATLAB Program for Hebb Net to classify two dimensional input

patterns in bipolar with given targets.

b) Generate XOR function and ANDNOT function using McCulloch-Pitts Neural

Network.

2. Classification of a 4-Class problem with a Perceptron using MATLAB.

3. Write a MATLAB program to apply Back Propagation network for pattern

recognition problem.

4. Develop a Kohonen Self Organizing feature map for image recognition problem.

5. Write a MATLAB program to implement Discrete Hopfield Network and test the

input pattern.

6. Write a MATLAB program for edge detection using Fuzzy logic.

7. Use a genetic algorithms approach for Travelling Salesman Problem.

8. Develop a simple Ant Colony Optimization problem with MATLAB to find the

optimum path.

9. Solve a feature selection problem using Artificial Bee Colony Optimization.

10. Implementation of minimum Spanning tree using Particle Swarm Optimization.

REFERENCES

1. S. N. Sivanandam and S. N. Deepa, ―Principles of Soft Computing‖, 2nd

Edition,

Wiley India, 2012.

2. Samir Roy, Udit Chakraborty, ―Introduction to Soft Computing- Neuro-Fuzzy and

Genetic Algorithms‖, First Edition, 2013.

3. David E Goldberg, ―Genetic Algorithms in Search Optimization and Machine

Learning‖, Addison Wesley, 1997.

4. MATLAB Toolkit Manual.

COURSE OUTCOMES


CO1: Explain the concepts and techniques of soft computing and their roles in building

intelligent machines

CO2: Apply fuzzy logic and reasoning to handle uncertainty and solve various

engineering problems.

CO3: Differentiate the various Optimization techniques.

CO4: Implement and evaluate the genetic algorithms in Machine learning.

CO5: Evaluate and compare solutions by various soft computing approaches for a given

Problem.

SCHEME OF EXAMINATION:

CIE -

Practical

Conduction of experiments, performance of student in

every week lab and completion of lab record=50#

Total: Marks

50

Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10

Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100

Unit which have 09 hours shall not have 20*2=40

CS23

internal choice Marks


for 50 marks

# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the

marks obtained shall be reduced for 25 marks

## = Lab test is to be conducted for 50 marks and the marks obtained shall be

reduced for 25 marks. Lab test shall be conducted by two examiners out of which

one examiner is the faculty taught the course. There is no SEE for the practical ‘s

portion of integrated course


PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


CS24



Course title ADVANCES IN STORAGE AREA NETWORKS


L T P S Credits

4 0 - - 4



1.Computer Networks

2.Database Management Systems

3.Operating Systems

COURSE OBJECTIVES

This course will enable the students to

1. Understand storage centric and server centric systems

2. Apply various metrics used for designing storage area networks

3. Analysis RAID concepts

4. Evaluate data maintains at data centres with the concepts of backup

5. Create techniques for data storage management at data centres

UNIT -I INTRODUCTION: 10 Hours

Server Centric IT Architecture and its Limitations; Storage – Centric IT Architecture and its

advantages. Case study: Replacing a server with Storage Networks The Data Storage and Data

Access problem; The Battle for size and access. Intelligent Disk Subsystems: Architecture of

Intelligent Disk Subsystems; Hard disks and Internal 8 Hours I/O Channels; JBOD, Storage

virtualization using RAID and different RAID levels; Caching: Acceleration of Hard Disk

Access; Intelligent disk subsystems, Availability of disk subsystems.

UNIT -II I/O TECHNIQUES: 10 Hours

The Physical I/O path from the CPU to the Storage System; SCSI; Fibre Channel Protocol

Stack; Fibre Channel SAN; IP Storage. Network Attached Storage: The NAS Architecture, The

NAS hardware Architecture, The NAS Software Architecture, Network connectivity, NAS as a

storage system. File System and NAS: Local File Systems; Network file Systems and file

servers; Shared Disk file systems; Comparison of fibre Channel and NAS.

UNIT -III STORAGE VIRTUALIZATION: 10 Hours

Definition of Storage virtualization; Implementation Considerations; Storage virtualization on

Block or file level; Storage virtualization on various levels of the storage Network; Symmetric

and Asymmetric storage virtualization in the Network.

UNIT- IV SAN ARCHITECTURE AND HARDWARE DEVICES: 09 Hours

Overview, Creating a Network for storage; SAN Hardware devices; The fibre channel switch;

Host Bus Adaptors; Putting the storage in SAN; Fabric operation from a Hardware perspective.

Software Components of SAN: The switch‘s Operating system; Device Drivers; Supporting the

switch‘s components; Configuration options for SANs.

UNIT–V MANAGEMENT OF STORAGE NETWORK: 09 Hours

System Management, Requirement of management System, Support by Management System,

Management Interface, Standardized Mechanisms, Property Mechanisms, In-band Management,

Use of SNMP, CIM and WBEM, Storage.

UNIT-VI Recent advances and research being done in the topics mentioned above units

CS25

REFERENCES

1. Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, Wiley

India 2013.

2. Robert Spalding: ―Storage Networks The Complete Reference‖, Tata McGraw-Hill, 2011.

3. Marc Farley: Storage Networking Fundamentals – An Introduction to Storage Devices,

Subsystems, Applications, Management, and File Systems, Cisco Press, 2005.

4. Richard Barker and Paul Massiglia: ―Storage Area Network Essentials A Complete Guide to

understanding and Implementing SANs‖, Wiley India, 2006.

COURSE OUTCOMES :

The students should be able to:

CO1: Distinguish storage centric and server centric systems

CO2: Determine the need for performance evaluation and the metrics used for it

CO3: Extrapolate RAID and different RAID levels

CO4: Validate data maintained at data centres

CO5: Develop techniques for storage management


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks

Unit which have 09 hours shall not have

internal choice

20*2=40

Marks

Total:


choice

20*3=60

Marks


for 50 marks


PO1 PO2 PO3

CO1 2 CO2 2 CO3 3 CO4 3 CO5 1 2


CS26



Course title ADVANCED COMPUTER ARCHITECTURE


L T P S Credits

4 0 - - 4



COURSE OBJECTIVES:

This course will enable students to

1. Understand the fundamentals of computer design

2. Apply various memory technologies and optimizations

3. Analyse different multiprocessor issues

4. Evaluate homogeneous and heterogeneous multi-core architectures

5. Create vector, SIMD and GPU architectures

UNIT-I FUNDAMENTALS OF COMPUTER DESIGN AND ILP 10 Hours Fundamentals of Computer Design – Measuring and Reporting Performance – Instruction Level

Parallelism and its Exploitation – Concepts and Challenges –Exposing ILP - Advanced Branch

Prediction - Dynamic Scheduling - Hardware-Based Speculation - Exploiting ILP - Instruction

Delivery and Speculation - Limitations of ILP - Multithreading

UNIT-II MEMORY HIERARCHY DESIGN 09 Hours

Introduction – Optimizations of Cache Performance – Memory Technology and Optimizations –

Protection: Virtual Memory and Virtual Machines – Design of Memory Hierarchies – Case Studies.

UNIT-III MULTIPROCESSOR ISSUES 10 Hours

Introduction- Centralized, Symmetric and Distributed Shared Memory Architectures –Cache

Coherence Issues – Performance Issues – Synchronization – Models of Memory Consistency –

Case Study-Interconnection Networks – Buses, Crossbar and Multi-stage Interconnection Networks

UNIT-IV MULTICORE ARCHITECTURES 10 Hours

Homogeneous and Heterogeneous Multi-core Architectures – Intel Multicore Architectures – SUN

CMP architecture – IBM Cell Architecture. Introduction to Warehouse-scale computers-

Architectures- Physical Infrastructure and Costs- Cloud Computing –Case Study- Google

Warehouse-Scale Computer.

UNIT-V VECTOR, SIMD AND GPU ARCHITECTURES 09 Hours

Introduction-Vector Architecture – SIMD Extensions for Multimedia – Graphics Processing Units –

Case Studies – GP GPU Computing – Detecting and Enhancing Loop Level Parallelism-Case

Studies.

UNIT-VI Recent trends in Multicore processors

REFERENCES

1. Darryl Gove, ―Multicore Application Programming: For Windows, Linux, and Oracle

Solaris‖, Pearson, 2011

2. David B. Kirk, Wen-mei W. Hwu, ―Programming Massively Parallel Processors‖, Morgan

Kauffman, 2010

3. David E. Culler, Jaswinder Pal Singh, ―Parallel computing architecture hardware/software

approach‖ , Morgan Kaufmann /Elsevier Publishers, 1999

CS27

4. John L. Hennessey and David A. Patterson, ―Computer Architecture – A Quantitative

Approach‖, Morgan Kaufmann / Elsevier, 5th edition, 2012.

5. Kai Hwang and Zhi.Wei Xu, ―Scalable Parallel Computing‖, Tata McGraw Hill,

NewDelhi, 2003

COURSE OUTCOMES

Upon completion of this course, the students should be able to:

CO1: Recognize the fundamentals of computer design

CO2: Illustrates the memory technologies, optimizations and cache performance

CO3: Compare the different multiprocessor issues, and multi-stage interconnection networks

CO4: Assess the homogeneous and heterogeneous multi-core architectures

CO5: Investigate vector, SIMD and GPU architectures and detecting, enhancing loop level

parallelism


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks





PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 2


CS28

Course Code 18CS1L01 M. Tech (Computer Science and Engineering)

Category Engineering Science Courses ( Practical )

Course title NETWORK PROGRAMMING LAB

Scheme and

Credits


L T P S Credits

- - 3 - 2




2. Programming in Java and C++

3. NS-3 Simulator

COURSE OBJECTIVES


1. Understand the implementation of various network protocols.

2. Understand programming the network protocols using Java.

3. Analyse the programming environment of NS-3 simulator.

4. Evaluate typical wired/wireless network using the NS-3 simulator.

5. Create a typical GSM network using NS-3

PART – A

Write a Java Program to design a :

1. TCP iterative Client-Server application to reverse the given input sequence.

2. TCP concurrent Client-Server application to reverse the given input sequence.

3. TCP Client-Server application to transfer a file.

4. UDP Client-Server application to transfer a file.

5. ARP/RARP protocol.

6. DHCP protocol.

7. Distance Vector Routing protocol.

8. Dijkstra‘s shortest path routing protocol.

PART – B

1. Write a C++ program to connect two nodes on NS-3 (for practise only).

2. Write a C++ program to connect three nodes considering one as a central node on

NS-3 (for practise only).

3. Write a C++ program to implement a star topology on NS-3.

4. Write a C++ program to implement a bus topology on NS-3.

5. Write a C++ program showing the connection of two nodes and four routers such that

the extreme nodes act as client and server on NS-3.

6. Implement and study the performance of a typical GSM network on NS-3 (using

MAC layer).

COURSE OUTCOMES


CO1: Design programs for any type of TCP and UDP based client-server applications using

Java and Analyze.

CO2: Implement a typical wired network using Java.

CO3: Extend the functionalities of a routing protocol using Java.

CO4: Implement and analyse the performance of a wireless/mobile network on NS-3.

CO5: Design a typical GSM network on NS-3.

CS29

SCHEME OF EXAMINATION The student has to write and implement two programs selecting ONE from each part

Continuous Internal

Evaluation (CIE) (Laboratory

– 50 Marks)

Marks Semester End Evaluation (SEE)

(Laboratory – 100 Marks) Marks

Performance of the Student in

the laboratory every week

20 Write up 10

Test at the end of the semester 20 Experiment-1 (Part - A) – 35 Marks

Experiment-2 (Part - B) – 35 Marks

70

Viva Voce 10 Viva Voce 20

Total 100

Total (CIE) 50 Total (SEE) 50*


PO1 PO2 PO3

CO1 1

CO2 2

CO3 2

CO4 2 2 3

CO5 2 2


CS30

Course Code 18CS1M01 M. Tech (Computer Science and Engineering)

Category Engineering Science Courses ( Mandatory Audit)

Course title RESEARCH METHODOLOGY AND IPR

Scheme and

Credits


L T P S Credits

2 0 0 0 2

CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3

Hrs


COURSE OBJECTIVES


1. Understand the formulation of research problem, scope and objectives of research

problem

2. Use methods for effective technical writing skills

3. Analyse Approaches of investigation of solutions for research problem

4. Evaluate the format of research proposal , intellectual property and patent

5. Create patent, research paper

UNIT -I RESEARCH PROBLEM: 03 Hours Meaning of research problem, Sources of research problem, Criteria Characteristics of a good

research problem, Errors in selecting a research problem, Scope and objectives of research

problem. Approaches of investigation of solutions for research problem, data collection,

analysis, interpretation, Necessary instrumentations

UNIT- II RESEARCH REQUIREMENTS: 03 Hours

Effective literature studies approaches, analysis Plagiarism, Research ethics,

UNIT- III EFFECTIVE TECHNICAL WRITING: 06 Hours Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of research

proposal, a presentation and assessment by a review committee

UNIT- IV NATURE OF INTELLECTUAL PROPERTY: 06 Hours Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological research,

innovation, patenting, development. International Scenario: International cooperation on Intellectual Property.

Procedure for grants of patents, Patenting under PCT.

UNIT- V PATENT RIGHTS: 06 Hours Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases. Geographical

Indications.

UNIT- VI NEW DEVELOPMENTS IN IPR: Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer Software

etc. Traditional knowledge Case Studies, IPR and IITs.

REFERENCES

1. Stuart Melville and Wayne Goddard, ―Research methodology: an introduction for

science & engineering students‘‖

2. Wayne Goddard and Stuart Melville, ―Research Methodology: An Introduction‖

3. Ranjit Kumar, 2nd Edition, ―Research Methodology: A Step by Step Guide for

beginners‖ Halbert, ―Resisting Intellectual Property‖, Taylor & Francis Ltd ,2007.

4. Mayall, ―Industrial Design‖, McGraw Hill, 1992.

5. Niebel, ―Product Design‖, McGraw Hill, 1974.

6. Asimov, ―Introduction to Design‖, Prentice Hall, 1962.

7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, ―Intellectual Property in New

CS31

Technological Age‖, 2016.

8. T. Ramappa, ―Intellectual Property Rights Under WTO‖, S. Chand, 2008

COURSE OUTCOMES

At the end of the course, the students will be able to:

CO1: Understand research problem formulation. Analyze research related information and

follow research ethics

CO2: Understanding that when IPR would take such important place in growth of

individuals and nation, it is needless to emphasis the need of information about

Intellectual Property Right to be promoted among students in general & engineering

in particular.

CO3: Understand that IPR protection provides an incentive to inventors for further research

work and investment in R & D, which leads to creation of new and better products,

and in turn brings about, economic growth and social benefits.

CO4: Analyze research related information

CO5: Follow research ethics


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3 CO2 3 CO3 3 CO4 CO5 3 3


CS32

Course Code 18CS1S01 M. Tech (Computer Science and Engineering)

Category Seminar Semester- I

Course title SEMINAR – I

Scheme and Credits

No. of Hours/Week

Total hours = 24 L T P S Credits

0 0 2 0 1

CIE Marks: 50 Total Max. Marks: 50

Prerequisites (if any): NIL

COURSE LEARNING OBJECTIVES:

The objectives of the SEMINAR-I is to prepare the students to learn to:

1. Carry out the literature review of general research area/current topic and analyse the same

effectively.

2. Prepare a technical report, reflecting his/her depth of understanding, on the selected

area/topic and prepare content rich presentation.

3. Acquire communication and time management skills for effective and impactful presentation.

Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,

responsiveness, and politeness in-group discussion. Overcome stage fear during the

presentation.

GUIDE LINES

1. Seminar preparation and presentation is an individual student activity.

2. Topic may be of general/ specific interest to program of engineering or electives not offered in

the semester and to be selected in consultation with the faculty/Guide.

3. Select one pertinent research paper for the seminar presentation.

4. Prepare and submit a detailed technical report of the seminar topic.

COURSE OUTCOMES:

Students shall be able to:

1. Carry out the literature survey of topic of seminar.

2. Prepare a technical report on the selected area/topic.

3. Make an effective presentation with seamless flow of content within the time allocated.

Overcome inhibition in interacting with peers and hence develop the spirit of team work.

Overcome stage fear during the presentation.

CS33


CIE – 50

marks

Phase -1 Marks =10 Seminar Report

Marks =20

Total:50

Marks Phase -2 Marks =20


PO1 PO2 PO3

CO1 2 2 3 CO2 2 3 3 CO3 2


Scheme of Continuous Internal Evaluation (CIE):

Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall

comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by

Chairman. The evaluation criteria shall be as per the rubrics given below:

Rubrics for Evaluation:

Topic - Technical Relevance, Sustainability and Societal Concerns : 15%

Review of Literature and Technical Content : 35%

Presentation Skills : 25%

Report : 25%

CS34


Category Engineering Science Courses ( Mandatory Audit )

Course title AUDIT COURSE-I ( TECHNICAL PAPER WRITING )

Scheme and

Credits


L T P S Credits

2 0 0 0 1



COURSE OBJECTIVES


1. Understand the planning section of research paper and preparation of paper writing

2. Apply key skill while writing research paper and know about what to write in each

section

3. Analyse literature, methods,

4. Evaluate research paper, paraphrasing paper

5. Create good research paper

UNIT-I PLANNING AND PREPARATION: 06 Hours Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs

and Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and

Vagueness

UNIT- II CLARIFYING: 03 Hours Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising,

Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction

UNIT- III REVIEW OF THE LITERATURE: 06 Hours Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.

UNIT- IV KEY SKILLS: 06 Hours Key skills are needed when writing a Title, key skills are needed when writing an Abstract,

key skills are needed when writing an Introduction, skills needed when writing a Review of

the Literature,

UNIT- V METHODS: 03 Hours

skills are needed when writing the Methods, skills needed when writing the Results, skills are

needed when writing the Discussion, skills are needed when writing the Conclusions.

UNIT- VI NEW DEVELOPMENTS IN RESEARCH PAPER WRITING: useful phrases, how to ensure paper is as good as it could possibly be the first- time

submission

REFERENCES

1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google

Books)

2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University

Press

3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.

Highman‘sbook.

4. Adrian Wallwork, English for Writing Research Papers, Springer New York

Dordrecht Heidelberg London, 2011

CS35

COURSE OUTCOMES


CO1: List of section of research paper and preparation of paper writing

CO2: Determine key skill while writing research paper

CO3: Analyse literature, methods

CO4: Assess research paper, do paraphrasing paper

CO5: Formulate research paper and results of simulation


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50


PO1 PO2 PO3

CO1 3 CO2 3 CO3 3 CO4 3 CO5 3


CS36


Category Engineering Science Courses ( Theory - Professional Core)

Course title ADVANCED DATA STRUCTURES AND ALGORITHMS

Scheme and

Credits

No. of Hours/Week Semester – II

L T P S Credits

4 0 - - 4



COURSE OBJECTIVE


1. Learn various data structures and its usage in designing algorithms.

2. Understand to the advanced methods of designing and analysing algorithms.

3. Learn various string matching and graph algorithms.

4. Acquire the knowledge of polynomial, non polynomial and approximation problems.

5. Understand the recent developments in the area of algorithmic design.

UNIT-1 REVIEW OF ANALYSIS TECHNIQUES 09 Hours

Growth of Functions: Asymptotic notations; Standard notations and common functions;

Recurrences -The substitution method, recursion-tree method, the master method,

Probabilistic Analysis and Randomized Algorithms.

UNIT- II BASIC DATA STRUCTURES 09 Hours Stacks and Queues, Vectors, Lists, and Sequences, Trees, Priority Queues and Heaps,

Dictionaries and Hash Tables, Search Trees and Skip lists- Ordered Dictionaries and

Binary Search Trees, AVL Trees, Bounded-Depth Search Trees, Splay Trees, Skip Lists.

UNIT -III DYNAMIC PROGRAMMING 10 Hours

Matrix-Chain multiplication, Elements of dynamic programming, longest common

subsequences. Graph algorithms: Bellman - Ford Algorithm; Single source shortest paths

in a DAG; Johnson‘s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson

method. .

UNIT- IV TRIES AND STRING MACHING ALGORITHMS 10 Hours

Quad trees and K-d trees. String-Matching Algorithms: Naïve string Matching; Rabin -

Karp algorithm; String matching with finite automata; KnuthMorris-Pratt algorithm.

UNIT- V NP-COMPLETENESS 10 Hours

: Polynomial time, Polynomial time verification, NP-Completeness and reducibility, NP-

Complete problems. Approximation Algorithms: vertex cover problem, the set – covering

problem, randomization and linear programming, the subset – sum problem.

UNIT VI

Recent Trends in problem solving paradigms applying recently proposed data

structures

REFERENCES

1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,‖

Introduction to Algorithms‖, Third Edition, Prentice-Hall, 2011.

2. M T Goodrich, Roberto Tamassia, ―Algorithm Design‖, John Wiley, 2002.

3. Mark Allen Weiss, ―Data Structures and Algorithm Analysis in C++‖, 4th

Edition,

Pearson, 2014.

4. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, ―Data Structures and

Algorithms‖, Pearson Education, Reprint 2006.

5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, ―Fundamentals of Data Structures in C‖,

Silicon Pr, 2007.

6. Aho, Hopcroft and Ullman, Data structures and algorithms, 1st edition, Pearson

CS37

Education, India, 2002, ISBN: 8177588265, 978817758826

COURSE OUTCOMES

On completion of the course, the student will be able to:

CO1: Develop and analyze algorithms for red-black trees, B-trees and Splay trees and for

text processing applications.

CO2: Identify suitable data structures and develop algorithms for solving a particular set of

problems

CO3: Analyze the complexity/ performance of different algorithms.

CO4: Categorize the different problems in various classes according to their complexity.

CO5: Use appropriate data structure and algorithms in real time applications.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 2

CO2 2 2

CO3 2 2

CO4 2

CO5 2 2


CS38



Course title ADVANCED OPERATING SYSTEMS

Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES


1. Understand the Design Approaches and Issues related to Advanced Operating Systems.

2. Apply the Knowledge on Distributed Operating Systems Concepts to develop Clocks,

Mutual Exclusion Algorithms.

3. Analyse the Distributed Deadlock Detection Algorithms and Agreement Protocols.

4. Evaluate the Algorithms for Distributed Scheduling, Examine the Commit Protocols

and review Concurrency Control Algorithms.

5. Create Advanced Operating Systems Applications with recent technologies

UNIT- I INTRODUCTION: 09 Hours

Concept of Batch system, Multiprogramming, Time Sharing, Parallel, Distributed and Real-

time System, Process Management: Concept of Process, Synchronization, CPU Scheduling,

IPC, Deadlock: Concept of Deadlock Prevention, Avoidance, Detection and its Recovery.

Memory Management: Contiguous allocation, Paging and Segmentation. Virtual memory:

Demand Paging, Page Replacement Algorithms. Distributed OS- Design Approaches and

Issues in DOS. Message Passing Model and RPC.

UNIT -II CLOCKS AND DISTRIBUTED MUTUAL EXCLUSION: 10 Hours

Concept of Lamport‘s Logical Clock and Vector Clocks, Termination Detection. A simple

solution to distributed mutual exclusion, Non Token based algorithms: Lamport‘s algorithm,

Ricart Agarwala‘s algorithm, Maekawa‘s algorithm, Token based algorithms: Suzuki Kasami‘s

broadcast algorithm, Raymond‘s tree based algorithm.

UNIT -III DISTRIBUTED DEADLOCK DETECTION: 10 Hours

Deadlock Handling, Strategies in Distributed Systems, Issues in Deadlock Detection And

Resolution, Control Organization for Distributed Deadlock Detection, Centralized Deadlock

Detection Algorithm: Ho-Ramamoorthy‘s Algorithm, Distributed Deadlock Detection

Algorithms: A Path- Pushing Algorithm and Edge Chasing Algorithm, Hierarchical Deadlock

Detection Algorithms: Menasce- Muntz Algorithm, Ho-Ramamoorthy‘s Algorithm.

Agreement Protocols: Byzantine Agreement Problem, Solution to The Byzantine Agreement

Problem- Lamport -Shostak- Pease Algorithm, Dolev et al.‘s Algorithm

UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE: 10 Hours Issues in Load Distribution, Components of a Load Distributing Algorithms, Load Distributing

Algorithms, Performance Comparison, Selecting Suitable Load Sharing Algorithms,

Requirements of Load Sharing Policies. Commit Protocols, Nonblocking Commit Protocols,

Voting Protocols, Dynamic Voting Protocols, The Majority Based Dynamic Voting Protocol,

Dynamic Vote Reassignment Protocols.

UNIT -V DATABASE OS & CONCURRENCY CONTROL 09 Hours

Requirement of Database OS, A Concurrency Control Model of a Database System, The

Problem of Concurrency Control, Serializability Theory, Concurrency Control Algorithms,

Basic Synchronization Primitives, Lock Based Algorithms, Timestamp Based Algorithms,

Optimistic Algorithms, Concurrency Control Algorithms for Data Replication.

CS39


REFERENCES

1. Mukesh Singhal and Niranjan G Shivaratri, Advanced Concepts in Operating Systems, Tata

Mcgraw Hill, 2002.

2. A Silberchatz and Peter Baer Galvin, Operating System Concepts, 10th Edition, John Wiley

and Sons, 2018.

3. William Stallings, Operating System: Internals and Design Principles, 9th Edition, Prentice

Hall India, 2017.

4. Andrew S Tennenbaum and Albert S Woodhull, Operating System Design and

Implementation, 3rd Edition, Pearson Education Inc., 2006.

5. Ceri S and Pelagorthi S, Distributed Databases: Principles and Systems, 1984, McGraw Hill.

COURSE OUTCOMES


CO1: Explain the Fundamentals of Advanced Operating Systems, Design issues.

CO2: Determine the various Clock Synchronization Principles and Implement Mutual

Exclusion Algorithms.

CO3: Differentiate the various Distributed Deadlock Detection Algorithms and Analyze the

Agreement Protocols.

CO4: Select the appropriate Distributed Scheduling Algorithms, Commit Protocols and

Concurrency Control Algorithms.

CO5: Integrate the Concepts of Advanced Operating Systems with recent trends and

technologies to Design and Develop Applications.


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


internal choice

20*2=40

Marks

Total:

Marks 100


choice

20*3=60

Marks


for 50 marks

Mapping of Course Outcomes (COS) to Program Outcomes (POs) PO1 PO2 PO3

CO1 1 - CO2 1 2 CO3 1 2 CO4 1 3 CO5 3 2 2


CS40

Course code 18CS2C03 M. Tech (Computer Science and Engineering)


Course title ADVANCES IN DIGITAL IMAGE PROCESSING

Scheme and Credits No. of Hours/Week Semester – II

L T P S Credits

4 0 - - 4



COURSE OBJECTIVES:


1. Learn Digital Image Fundamentals and mathematical transforms necessary for image

processing

2. Apply image enhancement techniques in Spatial and Frequency Domains

3. Investigate the Image Restoration/Degradation Process

4. Demonstrate the image segmentation and representation techniques.

5. Be Familiar With Image Compression Techniques.

UNIT-I DIGITAL IMAGE FUNDAMENTALS & IMAGE TRANSFORMS 10 Hours Digital Image Fundamentals, Components of an Image Processing System, Sampling and

Quantization, Relationship between Pixels

Image Transforms Discrete Fourier Transform, Discrete Cosine Transform, Hadamard

Transform - Haar Transform - Slant Transform - KL Transform -Properties And Examples.

UNIT-II IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN 10 Hours

Gray level transformations, histogram processing, Enhancement using Arithmetic/logical

operations, Basics of spatial filtering, smoothening and sharpening spatial filters.

Image Enhancement in the Frequency Domain: Filtering in Frequency Domain,

smoothening and sharpening frequency domain filters.

UNIT-III IMAGE RESTORATION 09 Hours

Degradation Model, Noise Models, Restoration in Presence of Noise Only- Spatial Filtering,

Periodic Noise Reduction by Frequency Domain Filtering, Estimation of Degradation

Function, Inverse Filtering.

UNIT-IV IMAGE SEGMENTATION AND REPRESENTATION 09 Hours Detection of Discontinuities, Edge Linking And Boundary Detection, Thresholding, Region

Oriented Segmentation.

Representation, Boundary Descriptors and Regional Descriptors

UNIT-VIMAGE COMPRESSION 10 Hours

Fundamentals, Image Compression Models, Error Free Compression, Lossy Compression,

Image Compression Standards

UNIT-VI APPLICATIONS

Character Recognition, Fingerprint Recognition, Remote Sensing. Applications using different

Imaging modalities such as acoustic Imaging, Medical imaging, electron microscopy etc.

CS41

REFERENCES

1. Digital Image Processing – Rafael C. Gonzalez, Richard E. Woods, 3rd Edition,

Pearson, 2008

2. Digital Image Processing- S Jayaraman, S Esakkirajan, T Veerakumar- TMH, 2015.

3. Digital Image Processing and Analysis-Human and Computer Vision Application with

using CVIP Tools – Scotte Umbaugh, 2nd Ed, CRC Press, 2011

4. Digital Image Processing using MATLAB — Rafael C. Gonzalez, Richard E Woods

and Steven L. Eddings, 2nd Edition, TMH, 2010.

5. Fundamentals of Digital Image Processing — A.K.Jain, PHI, 2015

COURSE OUTCOMES

Upon Completion of the course, the student would be able to:

CO1: Discuss Digital Image Fundamentals.

CO2: Apply Image Enhancement techniques in spatial and frequency domain.

CO3: Distinguish image Restoration and Degradation processes.

CO4: Design image analysis techniques in the form of image segmentation and to evaluate the

Methodologies for segmentation.

CO5: Use Image Compression Techniques.


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks




marks.


PO1 PO2 PO3

CO1 1

CO2 3 1

CO3 1 3

CO4 3 3

CO5 3 3


CS42

Course Code 18CS2E1A M. Tech ( Computer Science and Engineering)

Category Engineering Science Courses(Theory - Professional Elective)

Course title DATA WAREHOUSING AND MINING

Scheme and

Credits


L T P S Credits

4 0 - - 4



Course Objectives: The course will enable the students to:

1. Understand the principles of Data warehousing and data mining.

2. Perform classification and prediction of data.

3. Examine the types of data in cluster analysis with various clustering methods.

4. Illustrate the concepts of mining object, spatial, multimedia, text and web data.

5. Build a data warehouse and mapping the data warehouse to a multiprocessor

architecture.

UNIT I - INTRODUCTION TO DATA MINING: 9 Hours Data Mining Functionalities, Data Pre-processing, Data Cleaning, Data Integration and

Transformation, Data Reduction, Data Discretization and Concept Hierarchy Generation.

Association Rule Mining: Efficient and Scalable Frequent Item set Mining Methods,

Mining Various Kinds of Association Rules, Association Mining to Correlation Analysis,

Constraint-Based Association Mining, Handling categorical, Continuous Attributes,

Concept hierarchy, Sequential and Sub graph Patterns.

UNIT II - CLASSIFICATION AND PREDICTION: 10 Hours

Issues Regarding Classification and Prediction, Classification by Decision Tree

Introduction, Bayesian Classification, Rule Based Classification, Classification by Back

propagation, Support Vector Machines, Associative Classification, Lazy Learners, Other

Classification Methods, Prediction, Accuracy and Error Measures, Evaluating the

Accuracy of a Classifier or Predictor, Ensemble Methods, Model Section.

UNIT III - CLUSTER ANALYSIS: 10 Hours

Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods,

Partitioning Methods, Hierarchical methods, Density-Based Methods, Grid-Based

Methods, Model-Based Clustering Methods, Clustering High-Dimensional Data,

Constraint-Based Cluster Analysis, Outlier Analysis, Quality and validity of Cluster

Analysis.

UNIT IV - MINING OBJECT, SPATIAL, MULTIMEDIA, TEXT AND WEB DATA:

9 Hours

Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial

Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web,

Stream Data Mining, Social Network Analysis.

UNIT V – DATA WAREHOUSING AND BUSINESS ANALYSIS: 10 Hours

Data warehousing Components, Building a Data warehouse, Mapping the Data Warehouse

to a Multiprocessor Architecture, DBMS Schemas for Decision Support, Data Extraction,

Cleanup, and Transformation Tools, Metadata, reporting, Query tools and Applications,

Online Analytical Processing (OLAP), OLAP and Multidimensional Data Analysis.

CS43

UNIT VI - Recent Trends in Distributed warehousing and Data Mining, Class Imbalance

Problem, Graph mining, Social Network Analysis.

REFERENCES

1. Jiawei Han and Micheline Kamber ―Data Mining Concepts and Techniques‖,

Second Edition, Elsevier, 2011.

2. Vipin Kumar, Introduction to Data Mining - Pang-Ning Tan, Michael Steinbach,

Addison Wesley, 2006.

3. G Dong and J Pei, Sequence Data Mining, Springer, 2007.

4. Alex Berson and Stephen J. Smith ―Data Warehousing, Data Mining & OLAP‖, Tata

McGraw – Hill Edition, Tenth Reprint 2007.

5. K.P. Soman, Shyam Diwakar and V. Ajay ―Insight into Data Mining Theory and

Practice‖, Easter Economy Edition, Prentice Hall of India, 2006.

G. K. Gupta ―Introduction to Data Mining with Case Studies‖, Easter Economy Edition,

Prentice Hall of India, 2006.

COURSE OUTCOMES


CO1. Demonstrate the concept of data mining principles, data warehousing Architecture

and its

Implementation

CO2. Apply the association rules, design and deploy appropriate classification techniques

for

mining the data

CO3. Cluster the high dimensional data for better organization of the data

CO4. Describe stream mining, Time-Series and sequence data in high dimensional system

CO5. Acquire the concept of Mining Object, Spatial, Multimedia, Text, and Web Data


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.

CS44


PO1 PO2 PO3

CO1 3

CO2 2

CO3 3

CO4 2

CO5 3


CS45



Course title STOCHASTIC PROCESS AND QUEUING THEORY


L T P S Credits

4 0 - - 4


Prerequisites (if any)

1. Probability Theory

COURSE OBJECTIVES:


1. Understand the concepts of stochastic processes, and Markov chains.

2. Understand Markov processes with discrete and continuous state spaces.

3. Understand the concepts of queuing theory and different queues.

4. Understand open and closed queuing networks.

5. Analyse single and multi-server queuing models.

UNIT-I INTRODUCTION TO STOCHASTIC PROCESSES AND MARKOV CHAINS 09 Hours Introduction, Specifications, Classification of Stochastic Processes, Stationary Process, Poisson Processes,

Renewal Processes, Markov Chains: Transition Probabilities, Classification of States and Chains, Reducible

Chains, Statistical Inference of Markov Chains, Markov Chains with Continuous State Space, Non-

homogenous Chains.

UNIT-II MARKOV PROCESSES WITH DISCRETE AND CONTINUOUS STATE SPACE 09 Hours

Poisson Process and its Related Distributions, Generalization of Poisson Processes, Birth and Death Process,

Markov Process with Discrete State Space (Continuous Time Markov Chains), Brownian Motion, Wiener

Process, Differential Equations for Wiener Process, Kolmogorav Equations, First Passage Time Distribution

for Wiener Process.

UNIT-III QUEUING THEORY AND MARKOVIAN QUEUING MODELS 10 Hours

Introduction, Characteristics Notations, Birth and Death Processes, Single-Server Queues (M|M|1), Multi-

Server Queues (M|M|c), Choosing the Number of Servers, Queues with Truncation (M|M|c|K), Erlang‘s Loss

Formula (M|M|c|c), Queues with Unlimited Service, Finite Source Queues, State-Dependent Service, Queues

with Impatience, Transient Behaviour, Busy-Period Analysis, Bulk Input and Bulk Service.

UNIT-IV NETWORKS, SERIES, AND CYCLIC QUEUES 10 Hours

Series Queues, Open Jackson Networks, Closed Jackson Networks, Cyclic Queues, Extensions of Jackson

Networks, Non-Jackson Networks.

UNIT-V GENERAL ARRIVAL OR SERVICE PATTERNS 10 Hours General Service, Single Server (M|G|1), General Service, Multi-server (M|G|c|∙, M|G|∞), General Input

(G|M|1, G|M|c).

UNIT-VI Performance analysis of data networks.

REFERENCES

1. Jyothiprasad Medhi, ―Stochastic Processes‖, New Age International Publishers, II Edition, 2002.

2. Kishore S. Trivedi, ―Probability and Statistics with Reliability, Queuing and Computer Science

Applications‖, John Wiley and Sons, II Edition, 2008.

3. Donald Gross, John F. Shortle, James M. Thomson, and Carl M. Harris, ―Fundamentals of Queuing

Theory‖, John Wiley and Sons, IV Edition, 2008.

4. Oliver Knill, ―Probability Theory and Stochastic Processes with Applications‖, Overseas Press, 2009.

CS46

COURSE OUTCOMES


CO1: Solve problems on stochastic process and Markov chains.

CO2: Analyse Markov Process for Discrete and Continuous State Spaces.

CO3: Model the Behaviour of Various Computer Networks and Distributed Systems using Queuing Models.

CO4: Analyse the Arrival and Service Patterns of any System and Solve Problems in Computer Networks

and Distributed Systems.

CO5: Investigate the performance analysis of data networks


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks


Units which have 09 Hours shall not

have internal choice. 20* 2 = 40 Marks

Total:100

marks





PO1 PO2 PO3

CO1 1

CO2 2

CO3 2

CO4 2

CO5 1


CS47


Category Engineering Science Courses ( Integrated - Professional

Elective )

Course title INTERNET OF THINGS

Scheme and

Credits


L T P S Credits

3 0 2 - 4

CIE Marks:

50

SEE Marks: 50 Total Max. Marks:

100

Duration of SEE: 3 Hrs



COURSE OBJECTIVES


1. Understand the IoT architecture and its enabling technologies.

2. Realize the various applications of IoT, understand the IoT system management

using NETCONF-YANG.

3. Understand the design of IoT, Python programming language, packages for IoT

and Raspberry Pi.

4. Create the various IoT protocols and their support in the implementation of

services.

5. Create a typical IoT input using the standard IT protocols.

UNIT I – INTRODUCTION TO INTERNET OF THINGS (IoT) 09 Hours

Definition and Characteristics of IoT, Physical Design of IoT, Logical Design of IoT, IoT

Enabling Technologies, IoT Levels and Deployment Templates.

UNIT II – DOMAIN SPECIFIC IoT, M2M and IoT System Management 09 Hours

Home Automation, Cities, Environment, Energy, Retail, Logistics, Agriculture, Industry,

Health and Lifestyle, M2M, Difference between IoT and M2M, SDN and NFV for IoT,

Need for IoT Systems Management, Simple Network Management Protocol, Network

Operator Requirements, IoT System Management with NETCONF-YANG.

UNIT III – DEVELOPING IoT USING PYTHON 10 Hours

IoT Design Methodology, IoT Systems – Logical Design using Python, Python Data

Types and Data Structures, Control Flow, Functions, Modules, Packages, File Handling,

Data/Time Operations. Classes, Python Packages for IoT: JSON, XML, HTTPLib and

URLLib, SMTPLib.

UNIT IV – IoT DEVICES AND PROTOCOLS 09 Hours

Basic Building Blocks of an IoT Device, Raspberry Pi, Programming Raspberry Pi using

Python, Basics of IoT Protocols: HTTP, UPnP, MQTT, CoAP and XMPP.

UNIT V – IoT PROTOCOLS 10 Hours

HTTP: Adding HTTP Support to Sensor, Adding HTTP Support to Actuator, Adding

HTTP Support to Controller. UPnP Protocol: Creating a Device Description Document,

Creating a Service Description Document, Providing a Web Interface, Creating an UPnP

Interface, Implementing the Still Image Service using Camera. CoAP Protocol: Making

HTTP Binary, Adding CoAP to Sensor, Adding CoAP to Actuator. MQTT Protocol:

Adding MQTT Support to Sensor, Adding MQTT Support to Actuator, Adding MQTT

Support to Controller. XMPP Protocol: Adding XMPP Support to a Thing, Adding

XMPP Support to Actuator, Adding XMPP Support to Camera, Adding XMPP Support

to Controller, Connecting All Together.

UNIT VI – Recent Trends in Industrial Internet of Things and Social Internet of Things.

CS48

UNIT VII- ( Lab Programs)

1. Study and Install Python in Eclipse and WAP for data types in python.

2. Write a Program for arithmetic operation in Python.

3. Write a Program for looping statement in Python.

4. Study and Install IDE of Arduino and different types of Arduino.

5. Write program using Arduino IDE for Blink LED.

6. Write Program for RGB LED using Arduino.

7. Study the Temperature sensor and Write Program foe monitor temperature using

Arduino.

8. Study and Implement RFID, NFC using Arduino.

9. Study and implement MQTT protocol using Arduino.

10. Study and Configure Raspberry Pi.

11. WAP for LED blink using Raspberry Pi.

12. Study and Implement Zigbee Protocol using Arduino / Raspberry Pi.

REFERENCES

1. Arshdeep Bahga and Vijay Madisetti, ―Internet of Things: A Hands-on

Approach‖, University Press, 2015.

2. Peter Waher, ―Learning Internet of Things‖, PACKT Publishing, 2015.

3. Adrian McEwen and Hakim Cassimally, ―Designing Internet of Things‖, John

Wiley and Sons, 2014.

COURSE OUTCOMES


CO1: Demonstrate the knowledge of IoT architecture and design.

CO2: Manage the IoT system with NETCONF-YANG.

CO3: Program the Raspberry Pi using Python.

CO4: Develop an IoT application using the IoT protocol.

CO5: Investigate the standard IoT protocol.


CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks

CS49


for 50 marks

# = CIE Marks for laboratory performance is to be evaluated for 50 marks and

the marks obtained shall be reduced for 25 marks






PO1 PO2 PO3

CO1 1 2 CO2 1 CO3 3 CO4 1 CO5 2


CS50



Course title NETWORK SECURITY

Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



COURSE OBJECTIVES


1: Learn the basics of security and various types of security issues.

2: Understand cryptography techniques available and various security attacks.

3: Explore network security and how they are implemented in real world.

4: Analyse various issues of wireless security techniques.

5: Effectively design secured wireless sensor network

UNIT I- INTRODUCTION TO SECURITY 09 Hours

Need for security, Security approaches, Principles of security, Types of attacks.

Encryption Techniques: Plaintext, Cipher text, Substitution & Transposition techniques,

Encryption & Decryption, Types of attacks, Key range & Size. Symmetric &

Asymmetric Key Cryptography: Algorithm types & Modes, DES, AES, RSA, ECC;

UNIT II- SECURED HASH ALGORITHMS 09 Hours

Message Digest, Key- Distribution Algorithms, Digital signatures, User Authentication

Mechanisms, Key Management, Certificates, Kerberos.

UNIT III - DISTRIBUTED SYSTEM SECURITY 10 Hours Firewalls, Proxy-Servers, Network intrusion detection. Transport security: Mechanisms

of TLS, SSL, IPSec. Network -level solutions, Secure socket layer, IP Security, DoS

Counter measures, DNS Solutions.

UNIT IV - WIRELESS SECURITY 10 Hours

Security in wireless Networks Vulnerabilities, Security techniques, Wi-Fi Security, DoS


UNIT V - WIRELESS SENSOR NETWORKS SECURITY 10 Hours

Security in Wireless Sensor Networks, Possible attacks, countermeasures, SPINS, Static

and dynamic key Management

UNIT VI Recent trends in IOT security, IDS – 04 Hours

REFERENCES

1. W. Stallings. Cryptography and Network Security. Prentice Hall, 7th

Edition - 2017

2. W. R. Cheswick and S. M. Bellovin. Firewalls and Internet Security. Addison Wesley,

2007.

3. B. Schneier. Applied Cryptography. Wiley, 2006.


5. KazemSohraby, Daniel Minoli and TaiebZnati, ―wireless sensor networks -

Technology,

Protocols, and Applications‖, Wiley Interscience 2007

6. Takahiro Hara,Vladimir I. Zadorozhny, and Erik Buchmann, ―Wireless Sensor

NetworkTechnologies for the Information Explosion Era‖, springer 2010

CS51

COURSE OUTCOMES


CO1: Analyse various security issues related to computer networks.

CO2: Implement various network security algorithms.

CO3: Design and implement various security algorithms for distributed environment.

CO4: Analyse the security issues and apply the relevant algorithm to mitigate the same.

CO5: Analyse various security attacks in WSN.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 2 2 CO2 2 CO3 2 2 CO4 3 2 CO5 2 2


CS52



Course title PATTERN RECOGNITION


L T P S Credits

4 0 - - 4



Course Objectives


1. Understand the various image processing and pattern recognition techniques.

2. Apply the mathematical morphology necessary for pattern recognition.

3. Analyse image representation, description and feature extraction.

4. Evaluate the principles of decision trees and clustering in pattern recognition

5. Create clustering large data sets and applications


Definition of PR, Applications, Datasets for PR, Different paradigms for PR, Introduction to

probability, events, random variables, Joint distributions and densities, moments. Estimation

minimum risk estimators, problems

UNIT- II REPRESENTATION 09Hours

Data structures for PR, Representation of clusters, proximity measures, size of patterns,

Abstraction of Data set, Feature extraction, Feature selection, Evaluation

UNIT- III NEAREST NEIGHBOUR BASED CLASSIFIERS & BAYES CLASSIFIER 10

Hours

Nearest neighbour algorithm, variants of NN algorithms, use of NN for transaction databases,

efficient algorithms, Data reduction, prototype selection, Bayes theorem, minimum error rate

classifier, estimation of probabilities, estimation of probabilities, comparison with NNC, Naive

bayes classifier, Bayesian belief network

UNIT- IV NAIVE BAYES CLASSIFIER 10 Hours

Bayessian belief network,Decision Trees: Introduction, DT for PR, Construction of DT,

Splitting at the nodes, Over fitting & Pruning, Examples, Hidden Markov models: Markov

models for classification, Hidden Markov models and classification using HMM

UNIT- V CLUSTERING 10 Hours

Hierarchical (Agglomerative, single/complete/average linkage, wards, Partitional (Forgy‘s, k-

means, Isodata), clustering large data sets, examples, An application: Handwritten Digit

recognition

UNIT-VI Recent trends in pattern analysis

REFERENCES

1. Pattern Recognition ( An Introduction) , V Susheela Devi, M Narsimha Murthy, 2011

Universities Press, ISBN 978-81-7371-725-3

2. Pattern Recognition & Image Analysis, Earl Gose, Richard Johnsonbaugh, Steve Jost.

PH ISBN-81-203-1484-0, 1996.

3. Duda R. O., P.E. Hart, D.G. Stork., Pattern Classification, John Wiley and sons, 2000.

CS53

COURSE OUTCOMES


CO1: Explain the various image processing and pattern recognition techniques.

CO2: Solve the mathematical morphology necessary for pattern recognition.

CO3: Review image representation and feature extraction.

CO4: Assess the principles of decision trees and clustering in pattern recognition

CO5: Design the clustering large data sets and applications


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks




marks.


PO1 PO2 PO3

CO1 1

CO2 1

CO3 2

CO4 2

CO5 2


CS54


Category Engineering Science Courses(Theory - Professional Elective)

Course title WEB SECURITY

Scheme and

Credits


L T P S Credits

4 0 - - 4



COURSE OBJECTIVES


1. Understand web application‘s vulnerability and malicious attacks.

2. Understand basic web technologies used for web application development.

3. Analyse basic concepts of Mapping the application

4. Illustrate different attacking illustrations.

5. Emphasis various basic concepts of Attacking Data Stores.

.

UNIT I: WEB APPLICATION SECURITY 09 Hours

The Evolution of Web Applications, Common Web Application Functions, Benefits of

Web Applications, Web Application Security.

Core Defense Mechanisms: Handling User Access Authentication, Session Management,

Access Control, Handling User Input, Varieties of Input Approaches to Input Handling,

Boundary Validation.

Multistep Validation and Canonicalization: Handling Attackers, Handling Errors,

Maintaining Audit Logs, Alerting Administrators, Reacting to Attacks.

UNIT II: WEB APPLICATION TECHNOLOGIES 09 Hours The HTTP Protocol, HTTP Requests, HTTP Responses, HTTP Methods, URLs, REST,

HTTP Headers, Cookies, Status Codes, HTTPS, HTTP Proxies, HTTP Authentication,

Web Functionality, Server-Side Functionality, Client-Side Functionality, State and

Sessions, Encoding Schemes, URL Encoding, Unicode Encoding, HTML Encoding,

Base64 Encoding, Hex Encoding, Remoting and Serialization Frameworks.

UNIT III: MAPPING THE APPLICATION 10 Hours Enumerating Content and Functionality, Web Spidering, User-Directed Spidering,

Discovering Hidden Content, Application Pages Versus Functional Paths, Discovering

Hidden Parameters, Analyzing the Application, Identifying Entry Points for User Input,

Identifying Server-Side Technologies, Identifying Server-Side Functionality, Mapping the

Attack Surface.

UNIT IV: ATTACKING AUTHENTICATION 10 Hours

Authentication Technologies, Design Flaws in Authentication Mechanisms, Bad

Passwords, Brute-Forcible Login, Verbose Failure Messages, Vulnerable Transmission of

Credentials, Password Change, Functionality, Forgotten Password Functionality, User

Impersonation, Functionality Incomplete, Validation of Credentials, Nonunique

Usernames, Predictable Usernames, Predictable Initial Passwords, Insecure Distribution of

Credentials. Attacking Access Controls.

UNIT V - ATTACKING DATA STORES 10 Hours

Injecting into Interpreted Contexts, Bypassing a Login, Injecting into SQL, Exploiting a

Basic Vulnerability Injecting into Different Statement Types, Finding SQL Injection Bugs,

Fingerprinting the Database, The UNION Operator, Extracting Useful Data, Extracting

CS55

Data with UNION, Bypassing Filters, Second-Order SQL Injection, Advanced Exploitation

Beyond SQL Injection: Escalating the Database Attack, Using SQL Exploitation Tools,

SQL Syntax and Error Reference, Preventing SQL Injection.

UNIT VI

Recent trends in Web Applications and its Security

REFERENCES

1. Defydd Stuttard, Marcus Pinto, The Web Application Hacker's Handbook: Finding and

Exploiting Security, Wiley Publishing, Second Edition.

2.Andres Andreu, Professional Pen Testing for Web application, Wrox Press.

3. Carlos Serrao, Vicente Aguilera, Fabio Cerullo, ―Web Application Security‖ Springer;

1st Edition

4. Joel Scambray, Vincent Liu, Caleb Sima ,―Hacking exposed‖, McGraw-Hill; 3rd

Edition, (October, 2010).

5. OReilly Web Security Privacy and Commerce 2nd Edition 2011.

6. Software Security Theory Programming and Practice, Richard sinn, Cengage Learning.

COURSE OUTCOMES


CO1: Achieve Knowledge of web application‘s vulnerability and malicious attacks.

CO2:Understand the basic web technologies used for web application development

CO3: Understands the basic concepts of Mapping the application.

CO4:Able to illustrate different attacking illustrations

C05:Investigate technique of attacking Data Stores


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 1 3

CO2 2 1 3

CO3 1 3

CO4 3 1 3

CO5 1 3


CS56

Course Code 18CS2L01 M. Tech (Computer Science and Engineering)


Course title ADVANCED DATS STRUCTURES AND ALGORITHMS LAB


L T P S Credits

0 0 4 0 4



1. Data structures and Algorithm

2. Java Programming


1. Acquire the knowledge of using advanced data structures

2. Acquire the knowledge of sorting and balancing the tree structure

3. Understand the usage of graph structures and string matching.

4. Understand the implementation of various string matching algorithms.

5. learn to solve the various NP complete problems

Each student has to work individually on assigned lab exercises. Lab sessions could be

scheduled as one contiguous four-hour session per week. It is recommended that all

implementations are carried out in Java. Exercises should be designed to cover the following

topics:

1. Doubly Circular Linked List

2. AVL Tree

3. Efficiency of Heap Sort & Quick Sort

4. Travelling Salesman Problem (Dynamic Programming)

5. N Queens Problem (Backtracking/ Branch & Bound)

6. Bellman-Ford algorithm

7. Shortest paths in a DAG

8. Ford-Fulkerson algorithm

9. Robin-Karp algorithm

10. Knuth-Morris-Pratt algorithms

11. String matching with Finite Automata

12. Vertex Cover problem

13. The Set Covering problem

14. The Subset-Sum problem

15. Maximum Bipartite algorithm

COURSE OUTCOMES


CO1: Design and implement basic and advanced data structures extensively.

CO2: Design and apply graph structures for various applications.

CO3: Design and develop efficient algorithms with minimum complexity using design

techniques.

CO4: Design and develop advanced string matching and NP Complete problems.

CO5: Achieve proficiency in Java programming.


Continuous Internal

Evaluation (CIE) (Laboratory Marks

Semester End Evaluation (SEE)


CS57

– 50 Marks)



20 Write up 10



70


Total 100



PO1 PO2 PO3

CO1 2 2 CO2 2 CO3 2 CO4 2 CO5 2


CS58


The objectives of the SEMINAR-II is to prepare the students to learn to:

1.Carry out the literature review of general research area/current topic and analyse the same

effectively.

2.Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic

and prepare content rich presentation.

3.Acquire communication and time management skills for effective and impactful presentation.


responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.

GUIDE LINES

1.Seminar preparation and presentation is an individual student activity.

2.Topic may be of general/ specific interest to program of engineering or electives not offered in


3.Select one pertinent research paper for the seminar presentation.

4.Prepare and submit a detailed technical report of the seminar topic.

COURSE OUTCOMES:


1.Carry out the literature survey of topic of seminar.

2.Prepare a technical report on the selected area/topic.

3.Make an effective presentation with seamless flow of content within the time allocated. Overcome

inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear

during the presentation.


CIE – 50

marks


Marks =20

Total:50



Category Seminar Semester: II

Course title SEMINAR - II

Scheme and Credits

No. of Hours/Week


0 0 2 0 1



CS59


PO1 PO2 PO3

CO1 2 2 3 CO2 2 3 3 CO3 2










Report : 25%

CS60



Course title PEDAGOGY STUDIES


L T P S Credits

2 0 0 0 1



COURSE OBJECTIVES

SThis course will enable students to

1. Understand the Thematic Overview and Pedagogical practices

2. Apply professional classroom practices , curriculum and assessment

3. Analyse methodology for quality assessment of school curriculum teacher

4. Evaluate pedagogic theory and pedagogical approaches

5. Create contexts pedagogy, new curriculum and assessment metrics for future

UNIT- I INTRODUCTION AND METHODOLOGY: 06 Hours Aims and rationale, Policy background, Conceptual framework and terminology Theories of

learning, Curriculum, Teacher education. Conceptual framework, Research questions.

Overview of methodology and Searching.

UNIT- II THEMATIC OVERVIEW: 03 Hours Pedagogical practices are being used by teachers in formal and informal classrooms in

developing countries. Curriculum, Teacher education

UNIT- III PEDAGOGICAL PRACTICES: 06 Hours Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage:

quality assessment of included studies. How can teacher education (curriculum and

practicum) and the school curriculum and guidance materials best support effective

pedagogy? Theory of change. Strength and nature of the body of evidence for effective

pedagogical practices. Pedagogic theory and pedagogical approaches. Teachers‘ attitudes

and beliefs and Pedagogic strategies.

UNIT- IV PROFESSIONAL DEVELOPMENT: 06 Hours

Professional development: alignment with classroom practices and follow-up support Peer

Support Support from the head teacher and the community. Curriculum and assessment

Barriers to learning: limited resources and large class sizes

UNIT- V RESEARCH GAPS AND FUTURE DIRECTIONS: 03 Hours

Research design Contexts Pedagogy Teacher education Curriculum and assessment

Dissemination and research impact.

UNIT- VI NEW DEVELOPMENTS IN PEDAGOGY:

REFERENCES

1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools,

Compare, 31 (2): 245-261.

2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation,

Journal of Curriculum Studies, 36 (3): 361-379.

3. Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher

education research project (MUSTER) country report 1. London: DFID.

4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and

learning of basic maths and reading in Africa: Does teacher preparation count?

International Journal Educational Development, 33 (3): 272–282.

CS61

5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary

education. Oxford and Boston: Blackwell.

6. Chavan M (2003) Read India: A mass scale, rapid, ‗learning to read‘ campaign

7. www.pratham.org/images/resource%20working%20paper%202.pdf.

COURSE OUTCOMES


CO1: What pedagogical practices are being used by teachers in formal and informal

classrooms in developing countries?

CO2: What is the evidence on the effectiveness of these pedagogical practices, in

what conditions, and with what population of learners?

CO3: How can teacher education (curriculum and practicum) and the school

curriculum and guidance materials best support effective pedagogy

CO4: Assess pedagogic theory and pedagogical approaches

CO5: Design new curriculum and assessment metrics for future


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50


PO1 PO2 PO3

CO1 3 CO2 3 CO3 3 CO4 3 CO5 3


CS62


Category Engineering Science Courses (Theory)

Course title MACHINE LEARNING(Theory - Professional Elective)

Scheme and Credits No. of Hours/Week Semester – III

L T P S Credits

4 0 - - 4



Data Mining (Preferable)


1. Understand the concept of how to extract patterns.

2. Design and analyse various machine learning algorithms and techniques with a modern

outlook, focusing on recent advances.

3. Develop supervised and unsupervised learning paradigms of machine learning.

4. Assess Deep learning techniques and various feature extraction strategies.

5. Evaluate the machine learning algorithms.

UNIT I - SUPERVISED LEARNING (REGRESSION/CLASSIFICATION) 09 Hours

Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes

Linear models: Linear Regression, Logistic Regression, Generalized Linear Models, Support

Vector Machines, Nonlinearity and Kernel Methods, Beyond Binary Classification: Multi-Class

/ Structured Outputs, Ranking

UNIT II - UNSUPERVISED LEARNING 10 Hours

Clustering: K-means / Kernel K-means, Dimensionality Reduction: PCA and kernel PCA,

Matrix Factorization and Matrix Completion, Generative Models (mixture models and latent

factor models)

UNIT III - MACHINE LEARNING ALGORITHMS 09 Hours

Evaluating Machine Learning algorithms and Model Selection, Introduction to Statistical

Learning Theory, Ensemble Methods (Boosting, Bagging, Random Forests)

UNIT IV 10 Hours

Sparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep Learning and

Feature Representation Learning

UNIT V 10 Hours

Scalable Machine Learning (Online and Distributed Learning) A selection from other advanced

topics, e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning, Inference in

Graphical Models, Introduction to Bayesian Learning and Inference

UNIT VI

Recent trends in various learning techniques of machine learning and classification methods for

IOT applications. Various models for IOT applications

REFERENCES

1. Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012

2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning,

Springer 2009

3. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007

COURSE OUTCOMES


CO1. Extract features that can be used for a particular machine learning approach in

various IOT applications.

CO2. Compare and contrast pros and cons of various machine learning techniques.

CS63

CO3. Get an insight of when to apply a particular machine learning approach.

CO4. Mathematically analyse various machine learning approaches and paradigms.

CO5. Design and formulate Supervised and Unsupervised learning paradigms of machine

learning


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




marks.

Mapping of Course Outcomes (COS) to Program Outcomes

PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


CS64



Elective)

Course title BIG DATA ANALYTICS

Scheme and

Credits

No. of Hours/Week Semester – III

L T P S Credits

3 - 2 - 4



Data Structures, Computer Architecture and Organization


1. Understand big data for business intelligence.

2. Illustrate business case studies for big data analytics.

3. Discuss NoSQL big data management.

4. Demonstrate map-reduce analytics using Hadoop.

5. Compare Hadoop related tools such as HBase, Pig, Cassandra and Hive for big data

analytics.

UNIT I – INTRODUCTION TO BIG DATA 9 Hours Need for big data, convergence of key trends, unstructured data, industry examples of big

data, web analytics, big data and marketing, fraud and big data, risk and big data, credit

risk management, big data and algorithmic trading, big data and healthcare, big data in

medicine, advertising and big data, big data technologies, introduction to Hadoop, open

source technologies, cloud and big data, mobile business intelligence, Crowd sourcing

analytics, inter and trans firewall analytics.

UNIT II - INTRODUCTION TO NoSQL 10 Hours Aggregate data models, aggregates, key-value and document data models, relationships,

graph databases, schemaless databases, materialized views, distribution models, sharding,

master-slave replication, peer peer replication, sharding and replication, consistency,

relaxing consistency, version stamps, map-reduce, partitioning and combining, composing

map-reduce calculations.

UNIT III – HADOOP 10 Hours

Data format, analyzing data with Hadoop, scaling out, Hadoop streaming, Hadoop pipes,

design of Hadoop distributed file system (HDFS), HDFS concepts, Java interface, data

flow, Hadoop I/O, data integrity, compression, serialization, Avro, file-based data

structures

UNIT IV – MAPREDUCE 10 Hours MapReduce workflows, unit tests with MRUnit, test data and local tests, anatomy of

MapReduce job run, classic Map-reduce, YARN, failures in classic Map-reduce and

YARN, job scheduling, shuffle and sort, task execution, MapReduce types, input formats,

output formats.

UNIT V – Hbase 9 Hours

Hbase, data model and implementations, Hbase clients, Hbase examples, praxis.

Cassandra, Cassandra data model, Cassandra examples, Cassandra clients, Hadoop

integration, Pig, Grunt, pig data model, Pig Latin, developing and testing Pig Latin scripts.

Hive, data types and file formats, HiveQL data definition, HiveQL data manipulation,

HiveQL queries.

UNIT VI -

Recent advances in Data Analytics

CS65

UNIT –VII (Lab Programs)

1. (a) Perform setting up and Installing Hadoop in its two operating modes:

o Pseudo distributed,

o Fully distributed.

(b) Use web based tools to monitor your Hadoop setup.

2. (a) Implement the following file management tasks in Hadoop:

o Adding files and directories

o Retrieving files

o Deleting files

(b) Benchmark and stress test an Apache Hadoop cluster

3. Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm.

(a) Find the number of occurrence of each word appearing in the input file(s)

(b) Performing a MapReduce Job for word search count (look for specific

keywords in a file)

4. Stop word elimination problem:

Input:

o A large textual file containing one sentence per line

o A small file containing a set of stop words (One stop word per line)

Output:

o A textual file containing the same sentences of the large input file without the

words appearing in the small file.

5. Write a Map Reduce program that mines weather data. Weather sensors collecting data

every hour at many locations across the globe gather large volume of log data, which is a

good candidate for analysis with MapReduce, since it is semi structured and record-

oriented.

Data available at: https://github.com/tomwhite/hadoopbook/tree/master/input/ncdc/all.

(a) Find average, max and min temperature for each year in NCDC data set?

(b) Filter the readings of a set based on value of the measurement, Output the line

of input files associated with a temperature value greater than 30.0 and store it

in a separate file.

6. Purchases.txt Dataset

(a) Instead of breaking the sales down by store, give us a sales breakdown by

product category across all of our stores

(b)What is the value of total sales for the following categories?

(i) Toys

(ii) Consumer Electronics

(c) Find the monetary value for the highest individual sale for each separate store

(d) What are the values for the following stores?

(i) Reno

(ii) Toledo

(iii)Chandler

(e) Find the total sales value across all the stores, and the total number of sales.

7. Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter

your data.

8. Write a Pig Latin scripts for finding TF-IDF value for book dataset (A corpus of eBooks

available at: Project Gutenberg)

9. Install and Run Hive then use Hive to create, alter, and drop databases, tables, views,

functions, and indexes.

10. Install, Deploy & configure Apache Spark Cluster. Run apache spark applications using

Scala.

CS66

REFERENCES

1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big

Analytics: Emerging Business Intelligence and Analytic Trends for Today's

Businesses", Wiley, 2013.

2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging

World of

Polyglot Persistence", Addison-Wesley Professional, 2012.

3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.

4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.

5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.

6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.

7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.

8. Alan Gates, "Programming Pig", O'Reilley, 2011.

COURSE OUTCOMES


CO1. Describe big data and use cases from selected business domains.

CO2. Discuss the business case studies for big data analytics.

CO3. Explain NoSQL big data management.

CO4. Perform map-reduce analytics using Hadoop.

CO5. Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data

analytics.


CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks







CS67


PO1 PO2 PO3

CO1 3 1

CO2 2 2

CO3 3 2

CO4 1 2

CO5 3


CS68



Course title HIGH PERFORMANCE COMPUTING

Scheme and

Credits


L T P S Credits

4 0 - - 4



1. Computer Architecture


COURSE OBJECTIVES


1. Understand the modern processors, their architectures and several case studies.

2. Understand the need of parallelism and types of parallelism.

3. Analyse shared and distributed based memory parallel programming using OpenMP

and MPI.

4. Evaluate hybrid parallel programming using MPI and OpenMPI.

5. Review of recent trends in efficiency MPI programming and scalable parallel

processing.

UNIT-I MODERN PROCESSORS 10 Hours

Stored-Program Computer Architecture, General-Purpose Cache-Based Microprocessor

Architecture, Memory, Multi-Core Processors, Multithreaded Processors, Vector Processors.

Basic Optimization Techniques For Serial Code: Scalar Profiling, Common Sense

Optimizations, Simple Measures, Large Impact, The Role of Compilers, C++ Optimizations.

Data Access Optimization: Balance Analysis and Light Speed Estimates, Storage Order, Case

Study: The Jacobi Algorithm, Case Study: Dense Matrix Transpose, Algorithm Classification

and Access Optimizations, Case Study: Sparse Matrix-Vector Multiply.

UNIT-II PARALLEL COMPUTERS 09 Hours

Taxonomy of Parallel Computing Paradigms, Shared-Memory Computers, Distributed-

Memory Computers, Hierarchical (Hybrid) Systems, Networks, Basics of Parallelization:

Why Parallelize? Data and Functional Parallelism, Parallel Scalability.

UNIT-III SHARED-MEMORY PARALLEL PROGRAMMING WITH OpenMP

09 Hours

Introduction to OpenMP, Case Study: OpenMP-Parallel Jacobi Algorithm. Efficient OpenMP

programming: Profiling OpenMP Programs Performance Pitfalls, Case Study: Parallel Sparse

Matrix-Vector Multiply.

UNIT-IV DISTRIBUTED-MEMORY PARALLEL PROGRAMMING WITH MPI

10 Hours

Message Passing, Introduction to MPI, Example: MPI Parallelization of a Jacobi Solver.

Efficient MPI Programming: MPI Performance Tools, Communication Parameters,

Synchronization, Serialization, Contention, Reducing Communication Overhead,

Understanding Intra-Node Point-To-Point Communication.

UNIT-V HYBRID PARALLELIZATION WITH MPI AND OpenMP 10 Hours

Basic MPI/OpenMP Programming Models, MPI Taxonomy of Thread Interoperability,

Hybrid Decomposition and Mapping, Potential Benefits and Drawbacks of Hybrid

Programming.

UNIT VI – Recent trends in efficient MPI programming and scalable parallel processing.

CS69

REFERENCES

1. Georg Hager and Gerhard Wellein, ―Introduction to High Performance Computing for

Scientists and Engineers‖, CRC Press, 2011.

2. Victor Eijkhout with Edmond Chow, Robert van de Geijn, ―Introduction to High

Performance Scientific Computing‖. II Edition, 2015.

3. Charles Severance Kevin Dowd, ―High Performance Computing‖, Oreilly Media, II

Edition, 1998

COURSE OUTCOMES


CO1: Discuss various modern processes, along with their architectures.

CO2: Categorize and compare different types of parallelism.

CO3: Asses shared and distributed based memory parallel programming using OpenMPI and

MPI.

CO4: Investigate hybrid parallel programming using MPI and OpenMP

CO5: Design an efficient HPC system using MPI and OpenMP programming.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




marks.


PO1 PO2 PO3

CO1 2

CO2 1 1

CO3 1 2

CO4 1

CO5 1 1 1


CS70

Course Code 18CS3P1A M. Tech (Computer Science and Engineering)

Category Engineering Science Courses (Theory – Open Elective)

Course title ARITIFICIAL INTELLIGENCE

Scheme and

Credits


L T P S Credits

4 0 0 0 4



COURSE OBJECTIVES


1. Understand the various characteristics of Intelligent agents

2. Understand the different search strategies in AI

3. Learn to represent knowledge in solving AI problems

4. Analyse the different ways of designing software agents

5. Evaluate the various reasoning techniques for AI.

UNIT-I INTRODUCTION: 9 Hours Introduction Definition Future of Characteristics and Problem Solving Approach to Typical

AI problems. State Space Search and Heuristic Search Techniques Defining problems as

State Space search, Production systems and characteristics, Hill Climbing, Breadth first and

depth first search, Best first search.

UNIT-II KNOWLEDGE REPRESENTATION ISSUES: 9 Hours Representations and Mappings, Approaches to knowledge representation, Using Predicate

Logic and Representing Knowledge as Rules , Representing simple facts in logic,

Computable functions and predicates, Procedural vs Declarative knowledge, Logic

Programming, Forward vs backward reasoning.

UNIT-III SOFTWARE AGENTS: 10 Hours

Architecture for Intelligent Agents Agent communication Negotiation and Bargaining

Argumentation among Agents Trust and Reputation in Multi-agent systems.

UNIT-IV REASONING I: 10 Hours Symbolic Logic under Uncertainty, Non-monotonic Reasoning, Logics for non-monotonic

reasoning, Statistical Reasoning.

UNIT-V METHODS: 10 Hours

Probability and Bayes Theorem, Certainty factors, Probabilistic Graphical Models, Bayesian

Networks, Markov Networks, Fuzzy Logic.


units

REFERENCES:

1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach", Prentice

Hall, Third Edition, 2009.

2. Rich and Knight, Artificial Intelligence, 2nd Edition, 2013

3. I. Bratko, "Prolog: Programming for Artificial Intelligence", Fourth edition,

Addison-Wesley Educational Publishers Inc., 2011.

4. M. Tim Jones, "Artificial Intelligence: A Systems Approach(Computer Science),

Jones and Bartlett Publishers, Inc.; First Edition, 2008

5. Nils J. Nilsson, "The Quest for Artificial Intelligence", Cambridge University

Press, 2009.

6. William F. Clocksin and Christopher S. Mellish," Programming Using

CS71

COURSE OUTCOMES


CO1: Define and identify various AI concepts

CO2: illustrate different AI strategies

CO3: Sketch various knowledge representation for AI problems

CO4: Analyse agents usage for AI

CO5: Design AI inference techniques


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


internal choice

20*2=40

Marks

Total:


choice

20*3=60

Marks


for 50 marks


PO1 PO2 PO3

CO1 2 CO2 2 CO3 2 CO4 2 CO5 2 2


CS72

Course Code 18CS3P1B M. Tech (Computer Science and Engineering)

Category Engineering Science Courses (Theory – Open Elective)

Course title BUSINESS ANALYTICS

Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100




1. Understand the role of business analytics within an organization.

2. Analyze data using statistical and data mining techniques.

3. Distinguish relationships between the underlying business processes of an

organization.

4. Gain an understanding of how managers use business analytics to formulate and solve

business problems and to support managerial decision making.

5. Discuss the uses of decision-making tools and Operations research techniques.

UNIT I – BUSINESS ANALYTICS 10 Hours Overview of Business analytics, Scope of Business analytics, Business Analytics Process,

Relationship of Business Analytics Process and organisation, competitive advantages of

Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical

methods, Review of probability distribution and data modelling, sampling and estimation

methods overview –

UNIT II - TRENDINESS AND REGRESSION ANALYSIS: 9 Hours

Modelling Relationships and Trends in Data, simple Linear Regression. Important

Resources, Business Analytics Personnel, Data and models for Business analytics,

problem solving, Visualizing and Exploring Data, Business Analytics Technology

UNIT III - ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS:

10 Hours Team management, Management Issues, Designing Information Policy, Outsourcing,

Ensuring Data Quality, Measuring contribution of Business analytics, Managing

Changes. Descriptive Analytics, predictive analytics, predicative Modelling, Predictive

analytics analysis, Data Mining, Data Mining Methodologies, Prescriptive analytics and

its step in the business analytics Process, Prescriptive Modelling, nonlinear Optimization.

UNIT IV – FORECASTING TECHNIQUES: 10 Hours

Qualitative and Judgmental Forecasting, Statistical Forecasting Models, Forecasting

Models for Stationary Time Series, Forecasting Models for Time Series with a Linear

Trend, Forecasting Time Series with Seasonality, Regression Forecasting with Casual

Variables, Selecting Appropriate Forecasting Models. Monte Carlo Simulation and Risk

Analysis: Monte Carle Simulation Using Analytic Solver Platform, New-Product

Development Model, Newsvendor Model, Overbooking Model, Cash Budget Model.

UNIT V – DECISION ANALYSIS 9 Hours Formulating Decision Problems, Decision Strategies with the without Outcome

Probabilities, Decision Trees, The Value of Information, Utility and Decision Making

UNIT VI -

Recent Trends in Embedded and collaborative business intelligence, Visual

data recovery, Data Storytelling and Data journalism.

CS73

REFERENCES

1. Business analytics Principles, Concepts, and Applications by Marc J. Schniederjans,

Dara G. Schniederjans, Christopher M. Starkey, Pearson FT Press, First edition,

2014

2. Business Analytics by James Evans, Pearson Education, First Edition, 2017.

COURSE OUTCOMES


CO1. Develop the knowledge of data analytics.

CO2. Demonstrate the ability of think critically in making decisions based

on data and deep analytics

CO3. Discuss the uses of technical skills in predicative and prescriptive

modeling to support business decision-making

CO4. Demonstrate the ability to translate data into clear and actionable insights.

CO5. Evaluate and assess the forecasting techniques.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


CS74

Course Code 18CS3P1C M. Tech (Computer Science and Engineering)

Category Engineering Science Courses ( Theory – Open Elective)

Course title MODELING AND SIMULATION

Scheme and

Credits


L T P S Credits

3 1 - - 4



COURSE OBJECTIVES:


1. Understand the system, specify systems using natural models of computation, modelling

techniques

2. Apply natural models of computation, modelling techniques to

understand behaviour of system , and analyse the simulation data

3. Analyse simulation data, simulation tools for simulation, Terminating Simulations –

Steady state simulations.

4. Evaluate the existing simulation models for verification, calibration and validation

5. Design validation, calibration model and decision support

UNIT – I INTRODUCTION TO SIMULATION 09 Hours

Introduction Simulation Terminologies- Application areas – Model Classification Types of

Simulation- Steps in a Simulation study- Concepts in Discrete Event Simulation Example.

UNIT-II MATHEMATICAL MODELS 10 Hours

Statistical Models - Concepts – Discrete Distribution- Continuous Distribution – Poisson

Process- Empirical Distributions- Queueing Models – Characteristics- Notation Queueing

Systems – Markovian Models- Properties of random numbers- Generation of Pseudo Random

numbers- Techniques for generating random numbers-Testing random number generators

Generating Random-Variates- Inverse Transform technique Acceptance- Rejection technique –

Composition & Convolution Method.

UNIT-III ANALYSIS OF SIMULATION DATA 10 Hours

Input Modelling - Data collection - Assessing sample independence – Hypothesizing

distribution family with data - Parameter Estimation - Goodness-of-fit tests – Selecting input

models in absence of data- Output analysis for a Single system – Terminating Simulations –


UNIT -IV VERIFICATION AND VALIDATION 09 Hours

Building – Verification of Simulation Models – Calibration and Validation of Models –

Validation of Model Assumptions – Validating Input – Output Transformations

UNIT-V SIMULATION OF COMPUT ER SYSTEMS 10 Hours

Simulation Tools – Model Input – High level computer system simulation – CPU – Memory

Simulation – Comparison of systems via simulation – Simulation Programming techniques -

Development of Simulation models.


REFERENCES

1. Jerry Banks and John Carson, ―Discrete Event System Simulation‖, Fourth Edition, PHI,

2005.

2. Geoffrey Gordon, ―System Simulation‖, Second Edition, PHI, 2006.

3. Frank L. Severance, ―System Modelling and Simulation‖, Wiley, 2001.

4. Averill M. Law and W. David Kelton, ―Simulation Modelling and Analysis, Third

Edition, McGraw Hill, 2006.

CS75

5. Jerry Banks, ―Handbook of Simulation: Principles, Methodology, Advances,

Applications and Practice‖, Wiley-Inter science, 1 edition, 1998.

COURSE OUTCOMES


CO1: Explain natural models of computation, modelling techniques

CO2: Determine suitable models of computation, modelling techniques to

understand behaviour of system.

CO3: Distinguish simulation models for verification, calibration and validation

CO4: Assess the performance of different simulation models, statistical models, queuing

Systems and Markovian Models for given problem

CO5: Design goodness-of-fit tests and input models in absence of data


CIE –

50

marks

Test I (Unit I, II &III)- 20 marks Two Quizzes /

AAT = 10 marks

Total:50


SEE

– 100

marks




Total:100




marks.


PO1 PO2 PO3

CO1 2 CO2 3 CO3 3 CO4 3 CO5 3 2


CS76


The objectives of the SEMINAR-III is to prepare the students to learn to:


effectively.






GUIDE LINES


2. Topic may be of general/ specific interest to program of engineering or electives not offered

in the semester and to be selected in consultation with the faculty/Guide.



COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50



Category Seminar Semester: III

Course title SEMINAR – III

Scheme and Credits

No. of Hours/Week


0 0 2 0 1



CS77


PO1 PO2 PO3

CO1 2 2 3 CO2 2 3 3 CO3 2










Report : 25%

CS78

INTERNSHIP


Objectives of the internship

1. Provide an opportunity to see how classroom and textbook learning applies to the real world,

and to expose the students to the relevant work experience.

2. Pay close attention to all the steps that go onto completing a job, thereby, help students to

become workforce ready before entering the job market as a graduate. Provide an opportunity

to select the topic of dissertation work by evaluating the requirement of organisation.

3. Prepare and present a technical report of internship.

GUIDELINES

1. Student has to approach the concerned heads of various Industries/organization, which are

related to the field of specialization of the M. Tech program.

2. If any student gets internship, he/she has to submit the internship offer letter duly signed by the

concerned authority of the company to the Chairperson of the Department.

3. The internship on full time basis will be after the examination of II semester and during III

semester for a period of 8 weeks without affects regular class work.

4. The progress has to be reported periodically to the faculty or to the Guide assigned by the

Chairperson as per the format acceptable to the respective industry /organizations and to the

Institution.

5. At the end of the internship the student has to prepare a detailed report and submit.

6. Students are advised to use ICT tools such as Skype to report their progress and submission of

periodic progress reports to the faculty in charge or guide.

7. Duly signed report from internal supervisor (faculty incharge or guide) and external supervisor

from the organization where internship is offered has to be submitted to the Chairperson of the

Department for his/her signature and further processing for evaluation.

The broad format of the internship final report shall contain Cover Page, Certificate from College,

Certificate from Industry / Organization of internship, Acknowledgement, Synopsis, Table of

Contents, chapters of Profile of the Organization - Organizational structure, Products, Services,

Business Partners, Financials, Manpower, Societal Concerns, Professional Practices, Activities of

the Department where internship is done, Tasks Performed and summary of the tasks performed.

specific technical and soft skills that student has acquired during internship, References &

Annexure.

Course Code 18CS3I01 M. Tech (Computer Science and Engineering)

Category Internship/ Mini Project Semester: III

Course title INTERNSHIP / MINI PROJECT

Scheme and Credits

No. of Hours/Week


--- --- 10 --- 5

CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs for a

batch of 6 students


CS79

COURSE OUTCOMES:

The student will be able to:

1. Apply the gained experience along with the theoretical knowledge to solve the real world

problems what engineers ready do.

2. Get equipped with experience required before entering the job market. Explore the possibility of

formulating the dissertation problem.

3. Prepare a technical report and make a presentation of details of internship.


CIE 1.Marks awarded by guide (Internal Examiner) = 50 marks

2.Marks awarded by the department internship monitoring committee = 50 marks

50*

Marks

SEE Presentation of internship in the presence of Guide (Internal Examiner) and external

examiner = 100 marks

50**

Marks

Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.

**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.


PO1 PO2 PO3

CO1 3 2

CO2 2 2

CO3 3


Rubrics for CIE:

1. Topic of Internship = 10%

2. Objectives of Internship = 10%

3. Specific Skills Acquired = 20%

4. Document = 40%

5. Presentation = 20%

Rubrics for SEE:

1. Topic of Internship = 10%

2. Objectives of Internship = 10%

3. Specific Skills Acquired = 20%

4. Document = 40%


CS80

MINI PROJECT

COURSE LEARNING OBJECTIVE:

1. Understand the method of applying engineering knowledge/use application software to solve

specific problems after carrying out literature survey.

2. Apply engineering and management principles while executing the project.

3. Demonstrate the skills for good technical report writing and presentation.

COURSE CONTENT/GUIDELINES

Student shall take up small problems in the field of domain of program as mini project. It can be

related to a solution to an engineering problem, verification and analysis of experimental data

available, conducting experiments on various engineering subjects, material characterisation, studying

a software tool for solution to an engineering problem, etc.

The mini project must be carried out preferably using the resources available in the department/college

and it can be of interdisciplinary also.

COURSE OUTCOMES:

The students shall be able to:

1. Conduct experiments / use the capabilities of relevant application software/ simulation tools

individually to generate data/ solve problems.

2. Assess the available engineering resources available in the institution.

3. Prepare and Present the technical document of mini project.

Rubrics for CI shall be done with weightage/distribution of marks as follows:

Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.

Sl.

no

Particulars Weightage Marks Total marks

of CIE

1 Selection of the topic & formulation of objectives 10% 10

50*

2 Modelling and simulation/algorithm

development/experiment setup

25% 25

3 Conducting experiments/implementation/testing 25% 25

4 Demonstration & Presentation 15% 15

5 Report writing 25% 25

Total 100% 100

CS81



50*

Marks



50**

Marks

Rubrics for SEE:

The SEE shall be done by two examiners out of which one examiner is the guide of mini project.

The following weightage would be given for the examination. Evaluation shall be done in batches, not

exceeding 6 students.

Sl.

no


of CIE

1 Brief write-up about the project 05% 05

50**

2 Presentation/demonstration of the project 20% 20

3 Methodology and Experimental Results and

Discussion

30% 30

4 Report 25% 25

5 Viva Voce 20% 20

Total 100% 100

Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50

marks.


PO1 PO2 PO3

CO1 3 3

CO2 2 3

CO3 3


CS82


1. Choose a problem applying relevant knowledge and skills acquired during the course. Formulate

the specifications of the project work, identify the set of feasible solutions, prepare, and execute

project plan considering professional, cultural and societal factors. Identify the problem-solving

methodology using literature survey and present the same.

2. Develop experimental planning and select appropriate techniques and tools to conduct

experiments to Evaluate and critically examine the outcomes followed by concluding the results

and identifying relevant applications. Preparation of synopsis, preliminary report for approval of

topic selected along with literature survey, objectives and methodology.

3. Develop oral and written communication skills to effectively convey the technical content.

GUIDELINES

The Dissertation work will start in III semester and should be a problem with research potential

and should involve scientific research, design, generation/collection and analysis of data,

determining solution and must preferably bring out the individual contribution.

The Dissertation work will have to be done by only one student and the topic of dissertation

must be decided by the guide and the student. The dissertation work shall be carried out, on-

campus or in an industry or in an organisation with prior approval from the Chairperson of the

Department. The student has to be in regular contact with the guide atleast once in a week.

The report of Dissertation work phase I shall contain cover page, certificate from

College/Industry/Organisation, Acknowledgement, List of Figures and Tables Contents,

Nomenclature, Chapters of Introduction including motivation to choose topic, Literature survey,

Conclusion of literature survey, Objectives and Scope of Dissertation, Methodology to be

followed, Experimental requirements, References and Annexure.

The preliminary results (if available) of the problem of Dissertation work may also be

discussed in the report.

Course Code 18CS3D01 M. Tech (Computer Science and Engineering)

Category Dissertation Work Semester: III

Course title DISSERTATION WORK PHASE –I

Scheme and Credits

No. of Hours/Week


0 0 10 0 5

CIE Marks: 50 SEE Marks:50 Total Max. Marks: 100 Duration of SEE: 1Hour


CS83

COURSE OUTCOME:

The students will be able to:

1. Self learn various topics relevant to Dissertation work. Survey the literature such as books,

National/International reference journals, articles and contact resource persons for selected topics

of Dissertation.

2. Write and prepare a typical technical report.

3. Present and defend the contents of Dissertation work phase I in front of technically qualified

audience effectively.



2.Marks awarded by the department dissertation monitoring committee = 50 marks

50*

Marks

SEE Presentation of Dissertation work Phase-I in the presence of Guide (Internal

Examiner) and external examiner = 100 marks

50**

Marks



Rubrics for CIE: Weightage

1. Introduction and Justification of Topic = 10%

2. Literature Survey and Conclusion = 30%

3. Objectives and Scope of Dissertation Work = 30%

4. Methodology to be Adopted = 20%

5. Presentation of Contents of Dissertation Work Phase-I = 10%

Rubrics for SEE:

1. Introduction and Justification of topic = 10%

2. Literature Survey and Conclusion = 30%

3. Objectives and Scope of Dissertation Work = 30%

4. Methodology, Experimental/Software = 20%

5. Presentation of Dissertation Phase-I = 10%

Mapping of Course Outcomes (Cos) to Program Outcomes (POs)

PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3


CS84


The objectives of the SEMINAR-IV is to prepare the students to learn to:


effectively.






GUIDE LINES






COURSE OUTCOMES:





inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage

fear during the presentation.


CIE – 50

marks


Marks =20

Total:50



Category Seminar Semester: IV

Course title SEMINAR – IV

Scheme and Credits

No. of Hours/Week


0 0 2 0 1



CS85


PO1 PO2 PO3

CO1 2 2 3 CO2 2 3 3 CO3 2








Review of Literature : 35%


Report : 25%

CS86


1. Apply/Use different experimental techniques, equipments, software/ Computational/ Analytical

/Modelling and Simulation tools required for conducting tests and generate other relevant data.

Students will also be able to design and develop an experimental setup/test rig.

2. Analyse the results of the experiments conducted/models developed.

3. Create a detailed technical document as per format based on the outcome of dissertation work

phase I and II.

GUIDELINES

Dissertation work phase II is the continuation of project work started in III semester. The report of

Dissertation work that includes the details of Dissertation work phase I and phase II should be

presented in a standard format. The candidate shall prepare a detailed report of dissertation that

includes Cover Paper, Certificate from College/Industry/Organisation, Acknowledgement,

Abstract, Table of contents, List of Figures and Table, Nomenclature, Chapter of Introduction,

Literature survey, Conclusion of literature survey, Objectives and Scope of dissertation work,

Methodology, Experimentation, Results, Discussion, Conclusion, Scope for future work,

References, Annexure and full text of the publication (submitted or published).

COURSE OUTCOMES:


1. Conduct experiments/ implement the capabilities of different Software /Computational /

Analytical/Modelling and simulation tools individually and generate data for validation of

hypothesis.

2. Investigate and assess the results obtained within the scope of experiments conducted followed by

conclusions.

3. Prepare a detailed technical document, Present and defend the contents of Dissertation work in

presence of technically qualified audience effectively.

Course Code 18CS4D01 M. Tech (Computer Science and Engineering)

Category Dissertation Work Semester: IV

Course title DISSERTATION WORK PHASE –II

Scheme and Credits

No. of Hours/Week


--- --- 30 --- 15

CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100


CS87


CIE

1. Marks awarded by guide = 50 marks

2. Marks awarded by the department dissertation monitoring committee

(Guide + Two faculty members )= 50 marks

100

marks

50*

marks

SEE

1. Dissertation evaluation by guide (Internal examiner) = 100 marks

2. Dissertation Evaluation by External Examiner = 100 marks

3. Viva- Voce examination by guide and external examiner who evaluated the

dissertation work =100 marks

300

marks

50**

marks


** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for 50

marks.


PO1 PO2 PO3

CO1 3 2 3

CO2 2 2 3

CO3 3 3 3


Rubrics for CIE:

1. Presentation of Background of Dissertation Work = 10%

2. Literature survey, Problem Formulation and Objectives = 30%

3. Presentation of Methodology and Experimentation = 30%

4. Results and Discussion = 20%

5. Questions and Answers = 10%

Rubrics for SEE:

1. Originality = 05%

2. Literature Survey = 15%

3. Problem Formulation, Objectives and Scope of Work = 10%

4. Methodology, Experimentation/Theoretical Modelling = 10%

5. Results, Discussion and Conclusion = 20%


7. Submission/Publication of Technical Paper for Publication/ Presentation in

Journals/Conference = 20%


PO1 PO2 PO3

CO1 3 2 3

CO2 2 2 3

CO3 3 3 3







M. Tech in Computer Science and Engineering

Specialization: Information Technology

IT1


VISION

―To strive for excellence in education for the realization of a vibrant and inclusive

society through knowledge creation and dissemination‖

MISSION



Pursue academic excellence through high quality research and publications


Inculcate right values among students while encouraging competitiveness to





IT2

Bangalore University UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING












Programmes.

VISION



society.

MISSION





IT3



M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme - 2K18

Specialization: Information Technology





CSM1. Implementing effectively, the outcome based education by imparting knowledge of basics and

advances in Computer Science and Engineering and other allied disciplines.



CSM3. Creatingconducive environment for effective teaching and learning process through

interdisciplinary research, online courses, interaction with institutions of higher learning and industries, R

and D laboratories of national importance, alumni, employers and other internal & external stake holders.

CSM4. Imbibing awareness of entrepreneurship, ethics, honesty, credibility, social and environmental

consciousness and providing opportunity to the faculty and technical staff for continuous academic

improvement and to equip them with then latest trends in Software Engineering and thereby inculcate the

habit of continuous learning in faculty, staff and students.

Program Outcomes:

Computer Science and Engineering Graduates will be able to:

CSPO1: An ability to independently carry out research/investigate and development work to solve

practical problems

CSPO2: An ability to write and present a substantial technical report/document

IT4

CSPO3: Students should be able to demonstrate a degree of mastery over the area as per the

specialization of the problem. The mastery should be at a level higher than the requirements in the

appropriate bachelor degree


M. Tech (Information Technology)

After successful completion of the program, the graduates will be

ITPEO 1: Able to apply concepts of mathematical foundation and computing to Information

Technology

ITPEO 2: Able to design and develop interdisciplinary and innovative systems.

ITPEO 3: Able to inculcate effective communication skills, team work, ethics, leadership in

preparation for a successful career in industry and R&D organizations.

IT5

BANGLORE UNIVERSITY


M. Tech. DEGREE IN INFORMATION TECHNOLOGY under CBCS Scheme – 2K18



L T P S



3 18CS1C03 Advanced Database Management Systems 4 0 0 0 CSE 4 50 50 4

18CS1E1A Cloud Computing 4 0 0 0 CSE

4 18CS1E1B Mobile Computing 4 0 0 0 CSE 4 50 50 4

18CS1E1C Wireless Networks 4 0 0 0 CSE


5 18CS1E2B Advances in Storage Area Networks 4 0 0 0 CSE 4 50 50 4

18IT1E2C Web Engineering 4 0 0 0 CSE



8 18IT1S01 Seminar -I 0 0 2 0 CSE 2 50 -- 1


Total 30 450 350 26

*SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.

IT6



L T P S



3 18CS2C03 Advances in Digital Image Processing 4 0 0 0 CSE 4 50 50 4

4

18CS2E1A Data Warehousing and Mining 4 0 0 0 CSE

4

50

50

4

18CS2E1B Stochastic Process and Queuing Theory 4 0 0 0

18CS2E1C Internet of Things 3 0 2 0

5

18CS2E2A Network Security 4 0 0 0

CSE 4 50 50 4 18IT2E2B Cyber Security 4 0 0 0

18CS2E2C Web Security 4 0 0 0


7 18IT2S01 Seminar -II 0 0 2 0 CSE 2 50 -- 1


Total 28 400 300 24



L T P S

1

2

18IT3E1A Social Network 4 0 0 0 CSE

CSE

CSE

4

4

50

50

50

50

4

4

18CS3E1B Big Data Analytics 3 0 2 0

18IT3E1C Information Retrieval Systems

4 0 0 0

Open Elective

3 18IT3S01 Seminar -III 0 0 2 0 CSE 2 50 1

4 18IT3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5

5 18IT3D01 Dissertation Phase -I 0 0 10 0 CSE 10 50 50 5

Total 30 250 200 19

IT7

Semester IV Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits


L T P S

1 18IT4S01 Seminar -IV 0 0 2 0 CSE 2 50 1

2 18IT4D01 Dissertation Phase -II 0 0 30 0 CSE 30 50 50 15

Total -- -- 32 -- 32 100 50 16

1 18ITMOOC MOOC Course 0 0 0 0 03


COURSE TYPE

CS: COMPUTER SCIENCE C: PROFESSIONAL CORE E: PROFESSIONAL ELECTIVE

P: OPEN ELECTIVE M: MANDATORY AUDIT L: LAB


L – Theory lecture, T – Tutorial, P – Lab work, S – Self-study:


IT8

Open Elective




Teaching

Dept.

Total hrs

/ week

CIE

Marks

*See

Marks Credits

L T P S

1




2


4 0 0 0 Civil 4 50 50 4

18CV3P1B Water Laws, Rights and Administration


18CV3P1D Remote Sensing and Geographic Information

System



4




5




IT9

Course Code 18CS1C01 M. Tech (Information Technology)

Category Engineering Science Courses (Theory- Professional Core )


Scheme and

Credits


L T P S Credits

3 1 - - 4





COURSE OBJECTIVES





3. Analyze the various concepts of arranging, selecting and combining objects from a

set.







mobius inversion formula, The Greatest integer function (No theorem proofs).








with Infinite system capacity, Characteristics of the Model (M/M/1) : (∞/FIFO), Single

Server Model with Finite System Capacity, Characteristics of the Model (M/M/1) :

(K/FIFO).






Application.









IT10




units

REFERENCES









Press, 1982.

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


internal choice

20*2=40

Marks

Total:


choice

20*3=60

Marks


for 50 marks


PO1 PO2 PO3

CO1 1 CO2 2 CO3 1 1 CO4 1 CO5 2


IT11




Scheme and

Credits

No. of

Hours/Week

Semester – I

L T P SS Credits

4 0 - - 4

CIE Marks:

50


Hrs


COURSE OBJECTIVE






























Networks(SDN).

REFERENCES



2. Andrew S. Tanenbaum and David J. Wetherall, ―Computer Networks‖, Pearson Education, 5th

Edition,2011. 3. William Stallings, ―High Speed Networks and Internet‖, , Second Edition, 2012.





IT12


printing 2012.



edition 2010.

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3 2 2

CO4 3 2

CO5 2 2 2


IT13



Course title ADVANCED DATABASE MANAGEMENT SYSTEMS


L T P S Credits

4 0 - - 4



COURSE OBJECTIVES:


1. Remembering the basics of database management systems.

2. Understanding the concepts of object relational databases and XML

3. Evaluate database security strategies.

4. Applying the concepts of Data Storage and Querying.

5. Understanding distributed, parallel databases and recent technologies


Data models, schemas and instances, three schema architecture and data independence,

database languages and interfaces, database environment. ER model: entity types, entity sets,

attributes and keys, relationship types, relationship sets, roles and structural constraints, ER

Diagrams. SQL3 - Overview of the SQL Query Language, SQL Data Definition, Basic

Structure of SQL Queries, Additional Basic Operations, Set Operations, Null Values,

Aggregate Functions, Nested Subqueries.

UNIT-II OBJECT AND OBJECT RELATIONAL DATABASES 10 Hours Object oriented concepts, object identity, object structure and type constructors, encapsulation

of operations, methods and persistence, class hierarchies and inheritance, object model of

ODMG, object definition language, object query language.XML: Structured, Semi structured,

and Unstructured Data, Data Model, Documents, DTD, XML Schema, Storing and Extracting

XML Documents from Databases, XML Languages.

UNIT-III DATABASE SECURITY 09 Hours Issues, discretional access control and role base access control, SQL Injection, statistical

database security, public key infrastructure, privacy issues and preservation, Oracle Label-

Based Security

UNIT- IV INDEXING AND HASHING 10 Hours Basic Concepts, Ordered Indices, B + -Tree Index Files, B + -Tree Extensions, Multiple-Key

Access, Static Hashing, Dynamic Hashing, Comparison of Ordered Indexing and Hashing,

Bitmap Indices. Query Processing: Overview, Measures of Query Cost, Selection Operation,

Sorting, Join Operation, Evaluation of Expressions. Query Optimization: Overview,

Transformation of Relational Expressions, Estimating Statistics of Expression Results, Choice

of Evaluation Plans, Materialized Views.

UNIT-V PARALLEL AND DISTRIBUTED DATABASES 10 Hours Parallel Databases: Introduction, I/O Parallelism, Interquery Parallelism, Intraquery

Parallelism, Intraoperation Parallelism, Interoperation Parallelism, Query Optimization, Design

of Parallel Systems, Parallelism on Multicore Processors. Distributed Databases:

Homogeneous and Heterogeneous Databases, Distributed Data Storage, Distributed

Transactions, Commit Protocols, Concurrency Control in Distributed Databases, Availability,

Distributed Query Processing, Heterogeneous Distributed Databases, Cloud-Based Databases,

IT14

Directory Systems.

UNIT-VI RECENT TECHNOLOGIES

Latest technologies such as NoSQL, BigData, Multimedia Databases, Mobility and Personal

Databases

REFERENCES

1. Elmasri and Navathe, Fundamentals of Database Systems, 7th

edition, Pearson, 2016.

2. A. Silberschatz, H. F. Korth and S. Sudarshan, Database system concepts 6th ed. 2011

3. Raghu Ramakrishnan, Database Management System, McGraw Hill, 3rd

edition, 2003.

4. Ceri and Pelagatti, Distributed Databases: Principles and Systems, Tata McGraw Hill, 2008,

5. C.J.Date, A.Kannan and S.Swamynathan, An introduction to Database System, Pearson

Education, 8th

edition, 2009.

6. Dr. P.S. Deshpande, SQL and PL/SQL for Oracle log, Black Books Dreamtech Press.

COURSE OUTCOMES

Upon completion of the course, the students would be able to: CO1: State and identify the key concepts of database management systems

CO2: Design and implement object relational databases.

CO3: Determine the different strategies for database security and key issues.

CO4: Apply the concepts of query optimization and indexing.

CO5: Illustrate distributed and parallel database technologies.


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks



have internal choice. 20* 2 = 40 Marks Total:100

marks




marks..


PO1 PO2 PO3

CO1 1

CO2 1

CO3 1

CO4 1 2

CO5 2


IT15

Course Code 18CS1E1A M. Tech (Information Technology)

Category Engineering Science Courses (Theory- Professional Elective )



L T P S Credits

4 0 - - 4





COURSE OBJECTIVES





4. Acquire the knowledge of the cloud resource management and scheduling mechanism

5. Learn various security issues in cloud computing







UNIT- II CLOUD COMPUTING: APPLICATIONS AND PARADIGMS 09 Hours Challenges for Cloud Computing, Existing Cloud Applications and New Application





UNIT-III CLOUD VIRTUALIZATION 10 Hours Virtualization, Layering and Virtualization, Virtual Machine Monitors, Virtual Machines,





UNIT-IV CLOUD RESOURCE MANAGEMENT AND SCHEDULING 10 Hours Policies and Mechanisms for Resource Management, Applications of Control Theory to Task








UNIT-V CLOUD SECURITY 10 Hours Cloud Security Risks, Security: The Top Concern for Cloud Users, Privacy and Privacy Impact



OS.

IT16


cloud computing.

REFERENCES





3. Rajkumar Buyya, James Broberg and Andrzej M. Goscinski , ―Cloud Computing:




COURSE OUTCOMES








CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks




marks.


PO1 PO2 PO3

CO1 2 2

CO2 2

CO3 1 2

CO4 2 1

CO5 2 2


IT17

Course Code 18CS1E1B M. Tech (Information Technology)




L T P S Credits

4 0 - - 4






COURSE OBJECTIVES:





synchronization.












UNIT-III DATABASES, DATA DISSEMINATION AND BROADCASTING SYSTEMS 10

Hours





video Broadcasting.





LANGUAGES 10 Hours






UNIT-VI Recent trends in wireless and mobile network security, mobile cloud computing.

IT18

REFERENCES

1. Raj Kamal, ―Mobile Computing‖, Oxford University Press, 2007.

2. Ashok Talukder, Ms Roopa Yavagal, and Mr. Hasan Ahmed, ―Mobile Computing,

Technology, Applications and Service Creation‖, II Edition, Tata McGraw Hill, 2010.

3. Jochen Schiller, ―Mobile Communications‖, Addison-Wesley. II Edition, 2004.

4. Hansmann, Merk, Nicklous, Stober, ―Principles of Mobile Computing‖, Springer, II Edition,

2003.

COURSE OUTCOMES







computing.


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks





PO1 PO2 PO3

CO1 2

CO2 2

CO3 2

CO4 2 2

CO5 2 2


IT19

Course Code 18CS1E1C M. Tech (Information Technology)



Scheme and

Credits


L T P S Credits

4 0 - - 4


50



Computer Networks

COURSE OBJECTIVES:




basic operations


























UNIT-VI RECENT TRENDS Recent trends in Wireless networks, Vehicular Adhoc Networks.

IT20

REFERENCES




and Sons Inc 2002




6.Feng Zhao, leonidas Guibas, ―Wireless sensor Networks: An information processing

approach‖, Elsevier, 2004

COURSE OUTCOMES









CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks




50 marks


PO1 PO2 PO3

CO1 2 3

CO2 2 3

CO3 2 3

CO4 3 3

CO5 1 3


IT21


Category Engineering Science Courses (Integrated- Professional Elective)


Scheme and

Credits


L T P S Credits

3 0 2 0 4




COURSE OBJECTIVES:









09 Hours

Evolution of Computing: Soft Computing Constituents, Conventional AI to Computational

Intelligence: Machine Learning Basics, Hard-Margin and Soft-Margin SVMs- Concepts of

Kernel and Feature Spaces, Basics of Optimization and Quadratic programming,

Introduction to Steganography and Applications of SVMs to Steganalysis

UNIT-II NEURAL NETWORKS 10 Hours Introduction to

ANN, Architectures, Learning methods, Bayesian Networks, Back Propagation network,

Perceptrons, Hopfield Networks, Kohonen Self Organizing Feature Maps, Chaos Theory









Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions: Fuzzy Rules and Fuzzy

Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision Making,

Defuzzification





UNIT-VI

Recent Trends in deep learning, various classifiers, neural networks and genetic algorithm.


UNIT-VII (Lab Programs)


IT22



Network.






input pattern.




optimum path.



REFERENCES

1. S. N. Sivanandam and S. N. Deepa, ―Principles of Soft Computing‖, 2nd

Edition,

Wiley India, 2012.

2. Samir Roy, Udit Chakraborty, ―Introduction to Soft Computing- Neuro-Fuzzy and

Genetic Algorithms‖, First Edition, 2013.

3. David E Goldberg, ―Genetic Algorithms in Search Optimization and Machine

Learning‖, Addison Wesley, 1997.


COURSE OUTCOMES









Problem.


CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks

IT23


for 50 marks








PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


IT24





L T P S Credits

4 0 - - 4



1.Computer Networks


3.Operating Systems

COURSE OBJECTIVES
























UNIT- IV SAN ARCHITECTURE AND HARDWARE DEVICES: 09

Hours



Software Components of SAN: The switch‘s Operating system; Device Drivers; Supporting the

switch‘s components; Configuration options for SANs.

UNIT–V MANAGEMENT OF STORAGE NETWORK: 09

Hours



IT25



REFERENCES


India 2013.

2. Robert Spalding: ―Storage Networks The Complete Reference‖, Tata McGraw-Hill, 2011.



4. Richard Barker and Paul Massiglia: ―Storage Area Network Essentials A Complete Guide to

understanding and Implementing SANs‖, Wiley India, 2006.

COURSE OUTCOMES :








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


internal choice

20*2=40

Marks

Total:

Marks 100


choice

20*3=60

Marks


for 50 marks


PO1 PO2 PO3

CO1 2 CO2 2 CO3 3 CO4 3 CO5 1 2


IT26

Course Code 18IT1E2C M. Tech (Information Technology)

Category Engineering Science Courses (Integrated - Professional Elective )

Course title WEB ENGINEERING


L T P S Credits

3 - 2 - 4




1. Understand the concepts of Web engineering and requirement engineering.

2. Apply the architecture and models for Web applications.

3. Verify and analyse the Web applications.

4. Provide the knowledge on CGI Programming to implement various Web applications.

5. Design Embedded Web applications using PHP.

UNIT I - INTRODUCTION TO WEB ENGINEERING AND REQUIREMENTS

ENGINEERING: 10 Hours The need for Web engineering, Categories of Web Applications, Characteristics of Web

Applications. Evolution of Web Engineering, Requirement Engineering and modeling in web

engineering: RE specifics in Web Engineering, principles, modeling requirements. Methods

and Tools for modeling in Web Engineering, Designing a Web application.

UNIT II - WEB APPLICATION ARCHITECTURES AND MODELING WEB

APPLICATIONS: 10 Hours Introduction- Categorizing Architectures, Specifics of Web Application Architectures,

Components of a Generic Web Application Architecture, Layered Architectures: 2-Layer and

N-Layer Architectures, Data-aspect Architectures, Database-centric Architectures,

Architectures for Web Document Management, Architectures for Multimedia Data. Web

application design, Model based web application development: OOHDM method, W2000

method

UNIT III - TESTING WEB APPLICATIONS: 09 Hours Introduction, Fundamentals, Test approaches, Test methods and techniques, Test driven

development, Test Automation, Test tools.

UNIT IV - CGI PROGRAMMING: 10 Hours Structural- Apache web server, Apache configuration, MySQL- introduction, Database

independent interface, Loading and Dumping a Database. CGI Programming: Dynamic-

Introduction CGI.pm, Information received by the CGI Program, Form widget Methods, CGI

security considerations.

UNIT V – EMBEDDED WEB APPLICATION 09 Hours Introduction, Security considerations, PHP-introduction, Embedding PHP into HTML,

Configuration, Quick examples, Built-in PHP functions.

UNIT VI

Recent Trends in Web engineering and Web application tools

UNIT VII (Lab Programs)

1. Write a Perl script to read in a string from the console and print:

(a) The length and reverse of the string

(b) The upper and lower case version of the string

IT27

2. a) Write a Perl program to extract Log file information using regular expression.

b) Write a perl script to compute the nth

power of a given number.

3. a) Write a Perl program to display various Server Information like Server Name, Server

Software, Server protocol, CGI Revision etc.

b) Write a Perl program to accept UNIX command from a HTML form and to display

the output of the command executed.

4. a) Write a Perl Program to check whether the given number is Armstrong number or

not.

b) Write a Perl program to insert name and age information entered by the user into a

table created using MySQL and to display the current contents of this table.

5. Write a Perl program to accept the User Name and display a greeting message

randomly chosen from a list of 4 greeting messages.

6. Write a Perl program to keep track of the number of visitors visiting the web page and

to display this count of visitors, with proper headings.

7. Write a Perl program to display a digital clock which displays the current time of the

server.

8. Write a PHP program to store current date-time in a COOKIE and display the ‗Last

visited on‘ date-time on the web page upon reopening of the same page.

9. Write a PHP program to store page views count in SESSION, to increment the count on

each refresh, and to show the count on web page.

10. Using PHP and MySQL develop a program to accept book information viz. Accession

number, title, authors, edition and publisher from a web page and store the information

in a database and to search for a book with the title specified by the user and to display

the search results with proper headings.

REFERENCES

1. Web Engineering: The Discipline of Systematic Development of Web Applications by

Kappel et al., John Wiley, 2006

2. Web Engineering by Emilia Mendes and Nile Mosley, 1st Edition, Springer, 2006

3. Open Source Web Development with LAMP-using Linux, Apache, MySQL, perl and

PHP by James Lee and Brent Ware, Addison Wesley/Pearson Inc 2003.

COURSE OUTCOMES


CO1. Discuss Web engineering and requirement engineering concepts.

CO2. Make use of the Architecture and various modeling techniques for web applications.

CO2. Discuss design issues involved in web application development.

CO3. Validate and use testing process specific to Web applications.

CO4. Develop the Web applications using CGI Programming.

IT28


CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks








PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


IT29

Course Code 18CS1L01 M. Tech (Information Technology)



Scheme and

Credits


L T P S Credits

- - 3 - 2





3. NS-3 simulator

COURSE OBJECTIVES







PART – A







6. DHCP protocol.


8. Dijkstra‘s shortest path routing protocol.

PART – B









MAC layer).

COURSE OUTCOMES



Java and analysed

CO2: Implement a typical wired network using Java.




IT30


Continuous Internal


– 50 Marks)





20 Write up 10



70


Total 100



PO1 PO2 PO3

CO1 1

CO2 2

CO3 2

CO4 2 2 3

CO5 2 2


IT31

Course Code 18CS1M01 M. Tech (Information Technology)

Category Engineering Science Courses ( Mandatory Audit)


Scheme and

Credits


L T P S Credits

2 0 0 0 2


Hrs


COURSE OBJECTIVES



problem

















Indications.



REFERENCES

1. Stuart Melville and Wayne Goddard, ―Research methodology: an introduction for

science & engineering students‘‖


3. Ranjit Kumar, 2nd Edition, ―Research Methodology: A Step by Step Guide for

beginners‖ Halbert, ―Resisting Intellectual Property‖, Taylor & Francis Ltd ,2007.




7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, ― Intellectual Property in New


IT32


COURSE OUTCOMES







in particular.







CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


internal choice

20*2=40

Marks

Total:

Marks 100


choice

20*3=60

Marks


for 50 marks


PO1 PO2 PO3

CO1 3 CO2 3 CO3 3 CO4 CO5 3 3


IT33

Course Code 18IT1S01 M. Tech (Information Technology)

Category Seminar Semester- I

Course title SEMINAR – I

Scheme and Credits

No. of Hours/Week


0 0 2 0 1






effectively.





responsiveness, and politeness in-group discussion. Overcome stage fear during the

presentation.

GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50


IT34


PO1 PO2 PO3

CO1 2 2 3 CO2 2 3 3 CO3 2










Report : 25%

IT35



Course title AUDIT COURSE-I ( TECHNICAL PAPER WRITING )

Scheme and

Credits


L T P S Credits

2 0 0 0 1



COURSE OBJECTIVES




section




UNIT-I PLANNING AND PREPARATION: 06 Hours Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs


Vagueness

UNIT- II CLARIFYING: 03 Hours Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising,

Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction

UNIT- III REVIEW OF THE LITERATURE: 06 Hours Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.

UNIT- IV KEY SKILLS: 06 Hours Key skills are needed when writing a Title, key skills are needed when writing an Abstract,


the Literature,




UNIT- VI NEW DEVELOPMENTS IN RESEARCH PAPER WRITING: useful phrases, how to ensure paper is as good as it could possibly be the first- time

submission

REFERENCES


Books)

2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University

Press


Highman‘sbook.

4. Adrian Wallwork, English for Writing Research Papers, Springer New York

Dordrecht Heidelberg London, 2011

IT36

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50


PO1 PO2 PO3

CO1 3 CO2 3 CO3 3 CO4 3 CO5 3


IT37




Scheme and

Credits


L T P S Credits

4 0 - - 4



COURSE OBJECTIVE


















method. .








UNIT VI


structures

REFERENCES





Edition,

Pearson, 2014.




Silicon Pr, 2007.


IT38


COURSE OUTCOMES





problems





CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 2

CO2 2 2

CO3 2 2

CO4 2

CO5 2 2


IT39




Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES





3. Analyse the Distributed Deadlock Detection Algorithms and Agreement Protocols.


































IT40


REFERENCES


Mcgraw Hill, 2002.


and Sons, 2018.


Hall India, 2017.




COURSE OUTCOMES












CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


internal choice

20*2=40

Marks

Total:

Marks 100


choice

20*3=60

Marks


for 50 marks


CO1 1 - CO2 1 2 CO3 1 2 CO4 1 3 CO5 3 2 2


IT41

Course code 18CS2C03 M. Tech (Information Technology)


Course title ADVANCES IN DIGITAL IMAGE PROCESSING


L T P S Credits

4 0 - - 4



COURSE OBJECTIVES:


1. Learn Digital Image Fundamentals and mathematical transforms necessary for image

processing

2. Apply image enhancement techniques in Spatial and Frequency Domains

3. Investigate the Image Restoration/Degradation Process

4. Demonstrate the image segmentation and representation techniques.

5. Be Familiar With Image Compression Techniques.

UNIT-I DIGITAL IMAGE FUNDAMENTALS & IMAGE TRANSFORMS 10 Hours Digital Image Fundamentals, Components of an Image Processing System, Sampling and

Quantization, Relationship between Pixels

Image Transforms Discrete Fourier Transform, Discrete Cosine Transform, Hadamard

Transform - Haar Transform - Slant Transform - KL Transform -Properties And Examples.

UNIT-II IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN 10 Hours

Gray level transformations, histogram processing, Enhancement using Arithmetic/logical

operations, Basics of spatial filtering, smoothening and sharpening spatial filters.

Image Enhancement in the Frequency Domain: Filtering in Frequency Domain,

smoothening and sharpening frequency domain filters.

UNIT-III IMAGE RESTORATION 09 Hours

Degradation Model, Noise Models, Restoration in Presence of Noise Only- Spatial Filtering,

Periodic Noise Reduction by Frequency Domain Filtering, Estimation of Degradation

Function, Inverse Filtering.

UNIT-IV IMAGE SEGMENTATION AND REPRESENTATION 09 Hours Detection of Discontinuities, Edge Linking And Boundary Detection, Thresholding, Region

Oriented Segmentation.

Representation, Boundary Descriptors and Regional Descriptors

UNIT-VIMAGE COMPRESSION 10 Hours

Fundamentals, Image Compression Models, Error Free Compression, Lossy Compression,

Image Compression Standards

UNIT-VI APPLICATIONS

Character Recognition, Fingerprint Recognition, Remote Sensing. Applications using different

Imaging modalities such as acoustic Imaging, Medical imaging, electron microscopy etc.

IT42

REFERENCES

1. Digital Image Processing – Rafael C. Gonzalez, Richard E. Woods, 3rd Edition,

Pearson, 2008

2. Digital Image Processing- S Jayaraman, S Esakkirajan, T Veerakumar- TMH, 2015.

3. Digital Image Processing and Analysis-Human and Computer Vision Application with

using CVIP Tools – Scotte Umbaugh, 2nd Ed, CRC Press, 2011

4. Digital Image Processing using MATLAB — Rafael C. Gonzalez, Richard E Woods

and Steven L. Eddings, 2nd Edition, TMH, 2010.

5. Fundamentals of Digital Image Processing — A.K.Jain, PHI, 2015

COURSE OUTCOMES

Upon Completion of the course, the student would be able to:

CO1: Discuss Digital Image Fundamentals.

CO2:Apply Image Enhancement techniques in spatial and frequency domain.

CO3:Distinquish image Restoration and Degradation processes.

CO4: Design image analysis techniques in the form of image segmentation and to evaluate the

Methodologies for segmentation.

CO5: Use Image Compression Techniques.


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks




marks.


PO1 PO2 PO3

CO1 1

CO2 3 1

CO3 1 3

CO4 3 3

CO5 3 3


IT43

Course Code 18CS2E1A M. Tech ( Computer Science and Engineering)

Category Engineering Science Courses(Theory- Professional Elective )


Scheme and

Credits


L T P S Credits

4 0 - - 4









architecture.

UNIT I - INTRODUCTION TO DATA MINING: 9

Hours Data Mining Functionalities, Data Pre-processing, Data Cleaning, Data Integration and


Association Rule Mining: Efficient and Scalable Frequent Item set Mining Methods,

Mining Various Kinds of Association Rules, Association Mining to Correlation Analysis,

Constraint-Based Association Mining, Handling categorical, Continuous Attributes,

Concept hierarchy, Sequential and Sub graph Patterns.

UNIT II - CLASSIFICATION AND PREDICTION: 10

Hours

Issues Regarding Classification and Prediction, Classification by Decision Tree

Introduction, Bayesian Classification, Rule Based Classification, Classification by Back

propagation, Support Vector Machines, Associative Classification, Lazy Learners, Other

Classification Methods, Prediction, Accuracy and Error Measures, Evaluating the

Accuracy of a Classifier or Predictor, Ensemble Methods, Model Section.

UNIT III - CLUSTER ANALYSIS: 10

Hours Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods,

Partitioning Methods, Hierarchical methods, Density-Based Methods, Grid-Based

Methods, Model-Based Clustering Methods, Clustering High-Dimensional Data,

Constraint-Based Cluster Analysis, Outlier Analysis, Quality and validity of Cluster

Analysis.

UNIT IV - MINING OBJECT, SPATIAL, MULTIMEDIA, TEXT AND WEB DATA:

9 Hours

Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial

Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web,

Stream Data Mining, Social Network Analysis.

UNIT V – DATA WAREHOUSING AND BUSINESS ANALYSIS: 10

Hours

Data warehousing Components, Building a Data warehouse, Mapping the Data Warehouse

IT44

to a Multiprocessor Architecture, DBMS Schemas for Decision Support, Data Extraction,

Cleanup, and Transformation Tools, Metadata, reporting, Query tools and Applications,

Online Analytical Processing (OLAP), OLAP and Multidimensional Data Analysis.

UNIT VI - Recent Trends in Distributed warehousing and Data Mining, Class Imbalance

Problem, Graph mining, Social Network Analysis.

REFERENCES

1. Jiawei Han and Micheline Kamber ―Data Mining Concepts and Techniques‖,

Second Edition, Elsevier, 2011.

2. Vipin Kumar, Introduction to Data Mining - Pang-Ning Tan, Michael Steinbach,

Addison Wesley, 2006.


4. Alex Berson and Stephen J. Smith ―Data Warehousing, Data Mining & OLAP‖, Tata

McGraw – Hill Edition, Tenth Reprint 2007.

5. K.P. Soman, Shyam Diwakar and V. Ajay ―Insight into Data Mining Theory and

Practice‖, Easter Economy Edition, Prentice Hall of India, 2006.

G. K. Gupta ―Introduction to Data Mining with Case Studies‖, Easter Economy Edition,

Prentice Hall of India, 2006.

COURSE OUTCOMES


CO1. Demonstrate the concept of data mining principles, data warehousing Architecture

and its

Implementation

CO2. Apply the association rules, design and deploy appropriate classification techniques

for

mining the data





CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.

IT45


PO1 PO2 PO3

CO1 3

CO2 2

CO3 3

CO4 2

CO5 3


IT46


Category Engineering Science Courses Theory- Professional l Elective )



L T P S Credits

4 0 - - 4




COURSE OBJECTIVES:







UNIT-I INTRODUCTION TO STOCHASTIC PROCESSES AND MARKOV CHAINS 09 Hours Introduction, Specifications, Classification of Stochastic Processes, Stationary Process, Poisson Processes,



homogenous Chains.





for Wiener Process.



Server Queues (M|M|c), Choosing the Number of Servers, Queues with Truncation (M|M|c|K), Erlang‘s Loss






UNIT-V GENERAL ARRIVAL OR SERVICE PATTERNS 10 Hours General Service, Single Server (M|G|1), General Service, Multi-server (M|G|c|∙, M|G|∞), General Input

(G|M|1, G|M|c).


REFERENCES

1. Jyothiprasad Medhi, ―Stochastic Processes‖, New Age International Publishers, II Edition, 2002.

2. Kishore S. Trivedi, ―Probability and Statistics with Reliability, Queuing and Computer Science

Applications‖, John Wiley and Sons, II Edition, 2008.

3. Donald Gross, John F. Shortle, James M. Thomson, and Carl M. Harris, ―Fundamentals of Queuing

Theory‖, John Wiley and Sons, IV Edition, 2008.

4. Oliver Knill, ―Probability Theory and Stochastic Processes with Applications‖, Overseas Press, 2009.

IT47

COURSE OUTCOMES







CO5:Investigate the performance analysis of data networks


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




marks





PO1 PO2 PO3

CO1 1

CO2 2

CO3 2

CO4 2

CO5 1


IT48


Category Engineering Science Courses ( Integrated-- Professional

Elective ) )


Scheme and

Credits


L T P S Credits

3 0 2 - 4

CIE Marks:

50


100




COURSE OBJECTIVES




using NETCONF-YANG.


and Raspberry Pi.


services.














URLLib, SMTPLib.















IT49

UNIT VII- ( Lab Programs)








Arduino.




11. WAP for LED blink using Raspberry Pi.

12. Study and Implement Zigbee Protocol using Arduino / Raspberry Pi.

REFERENCES

1. Arshdeep Bahga and Vijay Madisetti, ―Internet of Things: A Hands-on

Approach‖, University Press, 2015.

2. Peter Waher, ―Learning Internet of Things‖, PACKT Publishing, 2015.

3. Adrian McEwen and Hakim Cassimally, ―Designing Internet of Things‖, John


COURSE OUTCOMES








CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


IT50

for 50 marks








PO1 PO2 PO3

CO1 1 2 CO2 1 CO3 3 CO4 1 CO5 2


IT51




Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



COURSE OBJECTIVES





4: Analyze various issues of wireless security techniques.




















REFERENCES


Edition - 2017


2007.



5. KazemSohraby, Daniel Minoli and TaiebZnati, ―wireless sensor networks -

Technology,

Protocols, and Applications‖, Wiley Interscience 2007

6. Takahiro Hara,Vladimir I. Zadorozhny, and Erik Buchmann, ―Wireless Sensor

IT52

NetworkTechnologies for the Information Explosion Era‖, springer 2010

COURSE OUTCOMES


CO1:analized various security issues related to computer networks

CO2: Implements various networks security algorithms

CO3: Design and implements various security algorithms for distributed environments

CO4: Analyses security issues and apply the relevant algorithms to mitigate the same

CO5: Analyses various security attracts


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 2 2 CO2 2 CO3 2 2 CO4 3 2 CO5 2 2


IT53

Course Code 18IT2E2B M. Tech (Information Technology)


Course title CYBER SECURITY

Scheme and

Credits


L T P S Credits

4 0 - - 4



COURSE OBJECTIVES


1. Memorise basis of security concepts and security techniques.

2. Understand the cybercrime and law.

3. Identify and determine the motive and remedial measures for cybercrime, detection

and handling.

4. Analyse areas affected by cybercrime and identify Legal Perspectives in cyber

security.

5. Effectively design a secure cyber system.

UNIT I - INTRODUCTION TO SECURITY 09 Hours Introduction to Security: Need for security, Security approaches, Principles of security,

Types of attacks. Encryption Techniques: Plaintext, Cipher text, Substitution &

Transposition techniques, Encryption & Decryption, Types of attacks, Key range & Size.

Symmetric & Asymmetric Key Cryptography: DES,RSA

UNIT II- INTRODUCTION TO CYBERCRIME 09 Hours Cybercrime: Definition and Origins of the Word, Cybercrime and Information Security,

Cybercriminals, Classifications of Cybercrimes, Cybercrime: The Legal Perspectives,

Cybercrimes: An Indian Perspective, Cybercrime and the Indian ITA 2000, A Global

Perspective on Cybercrimes, Cybercrime Era: Survival Mantra for the Netizens.

Cyberoffenses: Criminals Plan: Attacks, Social Engineering, Cyberstalking, Cybercafe and

Cybercrimes, Botnets: The Fuel for Cybercrime, Attack Vector, Cloud Computing.

UNIT III CYBERCRIME: MOBILE AND WIRELESS DEVICES 10 Hours Introduction, Proliferation of Mobile and Wireless Devices, Trends in Mobility, Credit

Card Frauds in Mobile and Wireless Computing Era, Security Challenges Posed by Mobile

Devices, Registry Settings for Mobile Devices, Authentication Service Security, Attacks

on Mobile/Cell Phones, Mobile Devices: Security Implications for organizations,

Organizational Measures for Handling Mobile, Organizational Security Policies and

Measures in Mobile Computing Era, Laptops.

UNIT IV- TOOLS AND METHODS USED IN CYBERCRIME 10 Hours Introduction, Proxy Servers and Anonymizers, Phishing, Password Cracking, Keyloggers

and Spywares, Virus and Worms, Trojan Horses and Backdoors, Steganography, DoS and

DDoS Attacks, SQL Injection, Buffer Overflow, Attacks on Wireless Networks. Phishing

and Identity Theft : Introduction, Phishing, Identity Theft (ID Theft).

UNIT V- INTRODUCTION TO SECURITY POLICIES AND CYBER LAWS

10 Hours

Need for An Information Security Policy, Information Security Standards – ISO,

Introducing Various Security Policies and Their Review Process, Introduction to Indian

Cyber Law, Objective and Scope of the it Act, 2000, Intellectual Property Issues, Overview

IT54

of Intellectual - Property - Related Legislation in India, Patent, Copyright, Law Related to

Semiconductor Layout and Design, Software License.

UNIT VI - Recent developments in Security Policies and Cyber Laws

REFERENCES


Edition - 2017

2. Sunit Belapure and Nina Godbole, ―Cyber Security: Understanding Cyber Crimes,

Computer Forensics And Legal Perspectives‖, Wiley India Pvt Ltd, ISBN: 978-81-

265-21791, 2013.

3. Dr. Surya PrakashTripathi, RitendraGoyal, Praveen Kumar Shukla, KLSI.

―Introduction to information security and cyber laws‖. Dreamtech Press. ISBN:

9789351194736, 2015.

4. Thomas J. Mowbray, ―Cybersecurity: Managing Systems, Conducting Testing, and

Investigating Intrusions‖, Copyright © 2014 by John Wiley & Sons, Inc, ISBN: 978

-1-11884965 -1

5. I. A. Dhotre , ―Cyber Forensics , Technical Publications; 1st Edition edition (2016),

ISBN- 13:978-9333211475

COURSE OUTCOMES At the end of the course, the students will be able to:

CO1: Interpret the basic concepts of cyber security, cyber law and their roles.

CO2: Articulate evidence collection and legal challenges

CO3: Discuss tools support for detection of various attacks.

CO4: Analyse various cyber risks.

CO5: Validate different cyber techniques in cyber system.


CIE –

50

mark

s


marks

Unit-VI = 15 marks

Total:

50


SEE

– 100

mark

s



internal choice. 20* 2 = 40 Marks Total:

100




50 marks.


PO1 PO2 PO3

CO1 3 CO2 2 CO3 2 CO4 2 CO5 2


IT55


Category Engineering Science Courses(Theory- Professional Elective )


Scheme and

Credits


L T P S Credits

4 0 - - 4



COURSE OBJECTIVES







.




Core Defense Mechanisms: Handling User Access Authentication, Session Management,

Access Control, Handling User Input, Varieties of Input Approaches to Input Handling,

Boundary Validation.



UNIT II: WEB APPLICATION TECHNOLOGIES 09 Hours The HTTP Protocol, HTTP Requests, HTTP Responses, HTTP Methods, URLs, REST,





UNIT III: MAPPING THE APPLICATION 10 Hours Enumerating Content and Functionality, Web Spidering, User-Directed Spidering,



Identifying Server-Side Technologies, Identifying Server-Side Functionality, Mapping the

Attack Surface.






Usernames, Predictable Usernames, Predictable Initial Passwords, Insecure Distribution of

Credentials. Attacking Access Controls.



Basic Vulnerability Injecting into Different Statement Types, Finding SQL Injection Bugs,

Fingerprinting the Database, The UNION Operator, Extracting Useful Data, Extracting

IT56

Data with UNION, Bypassing Filters, Second-Order SQL Injection, Advanced Exploitation

Beyond SQL Injection: Escalating the Database Attack, Using SQL Exploitation Tools,

SQL Syntax and Error Reference, Preventing SQL Injection.

UNIT VI


REFERENCES

1. Defydd Stuttard, Marcus Pinto , The Web Application Hacker's Handbook: Finding And

Exploiting Security, Wiley Publishing, Second Edition.



1st Edition





COURSE OUTCOMES


CO1:Achieve Knowledge of web application‘s vulnerability and malicious attacks.


CO3:Understands the basic concepts of Mapping the application.




CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 1 3

CO2 2 1 3

CO3 1 3

CO4 3 1 3

CO5 1 3


IT57

Course Code 18CS2L01 M. Tech (Information Technology)


Course title ADVANCED DATS STRUCTURES AND ALGORITHMS LAB


L T P S Credits

0 0 4 0 4




2. Java Programming









implementations are carried out in Java. Exercises should be designed to cover the following

topics:


2. AVL Tree














COURSE OUTCOMES





techniques.




Continuous Internal Marks Semester End Evaluation (SEE) Marks

IT58


– 50 Marks)

(Laboratory – 100 Marks)



20 Write up 10



70


Total 100



PO1 PO2 PO3

CO1 2 2 CO2 2 CO3 2 CO4 2 CO5 2


IT59




effectively.






GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50





Scheme and Credits

No. of Hours/Week


0 0 2 0 1



IT60


PO1 PO2 PO3

CO1 2 2 3 CO2 2 3 3 CO3 2








Review of literature and Technical Content : 35%


Report : 25%

IT61





L T P S Credits

2 0 0 0 1



COURSE OBJECTIVES










UNIT- II THEMATIC OVERVIEW: 03 Hours Pedagogical practices are being used by teachers in formal and informal classrooms in


UNIT- III PEDAGOGICAL PRACTICES: 06 Hours Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage:




pedagogical practices. Pedagogic theory and pedagogical approaches. Teachers‘ attitudes


UNIT- IV PROFESSIONAL DEVELOPMENT: 06 Hours Professional development: alignment with classroom practices and follow-up support Peer

support Support from the head teacher and the community. Curriculum and assessment


UNIT- V RESEARCH GAPS AND FUTURE DIRECTIONS: 03 Hours Research design Contexts Pedagogy Teacher education Curriculum and assessment



REFERENCES


Compare, 31 (2): 245-261.








IT62





COURSE OUTCOMES




CO2: What is the evidence on the effectiveness of these pedagogical practices, in

what conditions, and with what population of learners?

CO3: How can teacher education (curriculum and practicum) and the school

curriculum and guidance materials best support effective pedagogy




CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50


PO1 PO2 PO3

CO1 3 CO2 3 CO3 3 CO4 3 CO5 3


IT63

Course Code 18IT3E1A M. Tech (Information Technology)


Course title SOCIAL NETWORK

Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES This course will enable students to

1. Understand the concept of semantic web and related applications.

2. Construct social network using various representation

3. Understand social web and related communities

4. Build sentiment analysis of social

UNIT-I INTRODUCTION: 9 Hours Introduction to Web - Limitations of current Web – Development of Semantic Web –

Emergence of the Social Web, Evolution in Social Networks , Statistical Properties of

Social Networks -Network analysis - Development of Social Network Analysis - Key

concepts and measures in network analysis - Discussion networks - Blogs and online

communities - Web-based networks

UNIT- II MODELING AND VISUALIZATION: 10 Hours Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph

Representation -Centrality- Clustering - Node-Edge Diagrams - Visualizing Social

Networks with Matrix Based Representations- Node-Link Diagrams - Hybrid

Representations - Modelling and aggregating social network data – Random Walks and

their Applications - Ontological representation of social individuals and relationships

UNIT- III SOCIAL NETWORK ANALYSIS TECHNIQUES: 10 Hours Framework - Tracing Smoothly Evolving Communities - Models and Algorithms for

Social Influence Analysis - Influence Related Statistics - Social Similarity and Influence -

Influence Maximization in Viral Marketing - Algorithms and Systems for Expert Location

in Social Networks - Expert Location without Graph Constraints - with Score Propagation

– Expert Team Formation - Link Prediction in Social Networks -Feature based Link

Prediction - Bayesian Probabilistic Models - Probabilistic Relational Models

UNIT -IV MINING COMMUNITIES: 9 Hours

Aggregating and reasoning with social network data, Advanced Representations -

Extracting evolution of Web Community from a Series of Web Archive - Detecting

Communities in Social Networks - Evaluating Communities – Core Methods for

Community Detection & Mining - Applications of Community Mining Algorithms - Node

Classification in Social Networks.

UNIT- V TEXT AND OPINION MINING: 10 Hours Text Mining in Social Networks -Opinion extraction – Sentiment classification and

clustering - Temporal sentiment analysis - Irony detection in opinion mining - Wish

analysis - Product review mining – Review Classification – Tracking sentiments towards

topics over time


units

IT64

REFERENCES

1. Charu C. Aggarwal, ―Social Network Data Analytics‖, Springer; 2011

2. Peter Mika, ―Social Networks and the Semantic Web‖, Springer, 1st edition, 2007.

3. Borko Furht, ―Handbook of Social Network Technologies and Applications‖,

Springer, 1st edition, 2010.

4. Guandong Xu , Yanchun Zhang and Lin Li, ―Web Mining and Social Networking –

Techniques and applications‖, Springer, 1st edition, 2011.

5. Giles, Mark Smith, John Yen, ―Advances in Social Network Mining and Analysis‖,

Springer, 2010.

6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, ―Computational Social

Network Analysis: Trends, Tools and Research Advances‖, Springer, 2009.

7. Toby Segaran, ―Programming Collective Intelligence‖, O‘Reilly, 2012

COURSE OUTCOMES


CO1 Develop semantic web related applications.

CO2: Represent knowledge using ontology

CO3: Analysis of models in social network.

CO4: Predict social web and related communities.

CO5: Visualize and sentiment analysis of social networks


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


internal choice

20*2=40

Marks

Total:

Marks 100

Unit which have 10 hours shall t have internal

choice

20*3=60

Marks


for 50 marks


PO1 PO2 PO3

CO1 2 3 CO2 2 CO3 1 3 CO4 1 3 CO5 1 1 3


IT65



Elective)


Scheme and

Credits


L T P S Credits

3 - 2 - 4










analytics.


data, web analytics, big data and marketing, fraud and big data, risk and big data, credit

risk management, big data and algorithmic trading, big data and healthcare, big data in

medicine, advertising and big data, big data technologies, introduction to Hadoop, open

source technologies, cloud and big data, mobile business intelligence, Crowd sourcing

analytics, inter and trans firewall analytics.








design of Hadoop distributed file system (HDFS), HDFS concepts, Java interface, data

flow, Hadoop I/O, data integrity, compression, serialization, Avro, file-based data

structures


MapReduce job run, classic Map-reduce, YARN, failures in classic Map-reduce and

YARN, job scheduling, shuffle and sort, task execution, MapReduce types, input formats,

output formats.


Hbase, data model and implementations, Hbase clients, Hbase examples, praxis.

Cassandra, Cassandra data model, Cassandra examples, Cassandra clients, Hadoop

integration, Pig, Grunt, pig data model, Pig Latin, developing and testing Pig Latin scripts.

Hive, data types and file formats, HiveQL data definition, HiveQL data manipulation,

HiveQL queries.

UNIT VI -

Recent advances in Data Analytics

IT66








o Retrieving files

o Deleting files



(a) Find the number of occurrence of each word appearing in the input

file(s)

(b) Performing a MapReduce Job for word search count (look for specific

keywords in a file)


Input:



Output:





good candidate for analysis with MapReduce, since it is semi structured and record

oriented.

Data available at https://github.com/tomwhite/hadoopbook/tree/master/input/ncdc/all.

o Find average, max and min temperature for each year in NCDC data set?

o Filter the readings of a set based on value of the measurement, Output the line

of input files associated with a temperature value greater than 30.0 and store it

in a separate file.




(b) What is the value of total sales for the following categories?

o Toys.

o Consumer Electronics

(c) Find the monetary value for the highest individual sale for each separate

store


Reno

Toledo

Chandler

(e) Find the total sales value across all the stores, and the total number of

sales.

7. Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter

your data.


IT67





Scala.

REFERENCES

1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big

Analytics: Emerging Business Intelligence and Analytic Trends for Today's

Businesses", Wiley, 2013.


World of

Polyglot Persistence", Addison-Wesley Professional, 2012.







COURSE OUTCOMES







analytics.


CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks







IT68


PO1 PO2 PO3

CO1 3 1

CO2 2

CO3 3 2

CO4 1 2

CO5 3


IT69

Course Code 18IT3E1C M. Tech (Information Technology)


Course title INFORMATION RETRIEVAL SYSTEMS

Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



Course Objectives


1. Understand the taxonomy and models of Information retrieval system.

2. Discuss the retrieval evaluation methods.

3. Acquire learning techniques for text classification and clustering.

4. Design the search engine

5. Experiment web content structure searching in search engine.

UNIT I-INTRODUCTION 10 Hours Motivation, Basic concepts, Past, present, and future, The retrieval process. Modelling:

Introduction, A taxonomy of information retrieval models, Retrieval: Adhoc and filtering,

A formal characterization of IR models, Classic information retrieval, Alternative set

theoretic models, Alternative algebraic models, Alternative probabilistic models,

Structured text retrieval models, Models for browsing.

UNIT II- RETRIEVAL EVALUATION 10 Hours Introduction, Retrieval performance evaluation, Reference collections. Query

Languages: Introduction, keyword-based querying, Pattern matching, Structural

queries, Query protocols. Query Operations: Introduction, User relevance feedback,

Automatic local analysis, Automatic global analysis.

UNIT III - TEXT AND MULTIMEDIA LANGUAGES AND PROPERTIES

09 Hours Introduction, Metadata, Text, Markup languages, Multimedia. Text Operations:

Introduction, Document pre-processing, Document clustering, Text compression,

Comparing text compression techniques

UNIT IV – USER INTERFACES AND VISUALIZATION 10 Hours Introduction, Human-Computer interaction, The information access process, Starting

pints, Query specification, Context, Using relevance judgments, Interface support for

the search process. Searching the Web: Introduction, Challenges, Characterizing the

web, Search engines, Browsing, Meta searchers, Finding the needle in the haystack,

Searching using hyperlinks.

UNIT V - INDEXING AND SEARCHING 09 Hours Introduction; Inverted Files; Other indices for text;Boolean queries; Sequential searching;

Pattern matching; Structural queries;Compression. Parallel and Distributed IR:

Introduction, Parallel IR, Distributed IR.

UNIT VI -

Recent trends in information retrieval systems

IT70

REFERENCES

1. Ricardo Baeza-Yates, Berthier Ribeiro-Neto: Modern Information Retrieval, Pearson,

1999.

2. David A. Grossman, Ophir Frieder: Information Retrieval Algorithms and Heuristics,

2nd

Edition, Springer, 2004

COURSE OUTCOMES

Upon completion of this course, the students should be able to: CO1: Summerize taxonomy and models of information retrieval system.

CO2: Design the various components of an information retrieval system

CO3: Design text classification and clustering applying machine learning technique.

CO4: Demonstrate the functions of search engine.

CO5: Analyse web content structure for efficient information retrieval.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 2

CO2 3

CO3 2

CO4 3

CO5 2 3


IT71

Course Code 18CS3P1A M. Tech (Information Technology)

Category Engineering Science Courses (Theory- Open Elective )


Scheme and

Credits


L T P S Credits

4 0 0 0 4



COURSE OBJECTIVES







UNIT-I INTRODUCTION: 9 Hours Introduction Definition Future of Characteristics and Problem Solving Approach to Typical

AI problems. State Space Search and Heuristic Search Techniques Defining problems as

State Space search, Production systems and characteristics, Hill Climbing, Breadth first and

depth first search, Best first search.








UNIT-IV REASONING I: 10 Hours Symbolic Logic under Uncertainty , Non-monotonic Reasoning, Logics for non-monotonic






units

REFERENCES:









Press, 2009.


IT72

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


internal choice

20*2=40

Marks

Total:

Marks 100


choice

20*3=60

Marks


for 50 marks


PO1 PO2 PO3

CO1 2 CO2 2 CO3 2 CO4 2 CO5 2 2


IT73

Course Code 18CS3P1B M. Tech (Information Technology)

Category Engineering Science Courses (Theory-Open Elective )


Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100







organization.

4. Gain an understanding of how managers use business analytics to formulate and solve

business problems and to support managerial decision making.


UNIT I – BUSINESS ANALYTICS 10 Hours Overview of Business analytics, Scope of Business analytics, Business Analytics Process,


Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical

methods, Review of probability distribution and data modelling, sampling and estimation

methods overview –

UNIT II - TRENDINESS AND REGRESSION ANALYSIS: 9 Hours


Resources, Business Analytics Personnel, Data and models for Business analytics,

problem solving, Visualizing and Exploring Data, Business Analytics Technology

UNIT III - ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS:

10 Hours Team management, Management Issues, Designing Information Policy, Outsourcing,

Ensuring Data Quality, Measuring contribution of Business analytics, Managing

Changes. Descriptive Analytics, predictive analytics, predicative Modelling, Predictive

analytics analysis, Data Mining, Data Mining Methodologies, Prescriptive analytics and

its step in the business analytics Process, Prescriptive Modelling, nonlinear Optimization.

UNIT IV – FORECASTING TECHNIQUES: 10 Hours






Development Model, Newsvendor Model, Overbooking Model, Cash Budget Model.

UNIT V – DECISION ANALYSIS 9 Hours Formulating Decision Problems, Decision Strategies with the without Outcome


UNIT VI -

Recent Trends in Embedded and collaborative business intelligence, Visual

data recovery, Data Storytelling and Data journalism.

IT74

REFERENCES


Dara G. Schniederjans, Christopher M. Starkey, Pearson FT Press, First edition,

2014

2. Business Analytics by James Evans, Pearson Education, First Edition, 2017.

COURSE OUTCOMES










CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


IT75

Course Code 18CS3P1C M. Tech (Information Technology)

Category Engineering Science Courses (Theory-Open Elective)


Scheme and

Credits


L T P S Credits

3 1 - - 4



COURSE OBJECTIVES:



techniques






























REFERENCES


2005.




IT76




COURSE OUTCOMES










CIE –

50

marks

Test I (Unit I, II &III)- 20 marks Two Quizzes / AAT

= 10 marks

Total:50


SEE

– 100

marks

Answer FIVE full questions Total:100 marks






marks.


PO1 PO2 PO3

CO1 2 CO2 3 CO3 3 CO4 3 CO5 3 2


IT77




effectively.






GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50





Course title SEMINAR - III

Scheme and Credits

No. of Hours/Week


0 0 2 0 1



IT78

PO1 PO2 PO3

CO1 2 2 3 CO2 2 3 3 CO3 2










Report : 25%

IT79

INTERNSHIP



1. Provide an opportunity to see how classroom and textbook learning applies to the real world,

and to expose the students to the relevant work experience.


become workforce ready before entering the job market as a graduate. Provide an opportunity

to select the topic of dissertation work by evaluating the requirement of organisation.


GUIDELINES

1. Student has to approach the concerned heads of various Industries/organization, which are

related to the field of specialization of the M. Tech program.







Institution.










Business Partners, Financials, Manpower, Societal Concerns, Professional Practices, Activities of

the Department where internship is done, Tasks Performed and summary of the tasks

performed. specific technical and soft skills that student has acquired during internship,

References & Annexure.

Course Code 18IT3I01 M. Tech (Information Technology)



Scheme and Credits

No. of Hours/Week


--- --- 10 --- 5


batch 6 students


IT80

COURSE OUTCOMES:









2.Marks awarded by the department internship monitoring committee = 50 Marks

50*

Marks


examiner =100 Marks

50**

Marks




PO1 PO2 PO3

CO1 3 2

CO2 2 2

CO3 3


Rubrics for CIE:

1. Topic of internship = 10%

2. Objectives of internship = 10%

3. Specific skills acquired = 20%

4. Document = 40%


Rubrics for SEE:




4. Document = 40%

5. presentation = 20%

IT81

MINI PROJECT



specific

problems after carrying out literature survey.






available, conducting experiments on various engineering subjects, material characterisation, studying

a software tool for solution to an engineering problem, etc.

The mini project must be carried out preferably using the resources available in the department/college

and it can be of interdisciplinary also.

COURSE OUTCOMES:






Rubrics for CIE shall be done with weightage/distribution of marks as follows:


Sl.

no


of CIE


50*



25% 25




Total 100% 100

IT82


2.Marks awarded by the department internship monitoring committee = 50 Marks

50*

Marks


examiner =100 Marks

50**

Marks

Rubrics for SEE:


The following weightage would be given for the examination. Evaluation shall be done in batches, not

exceeding 6 students.

Sl.

no


of CIE


50**


3 Methodology and Experimental Results &

Discussion

30% 30

4 Report 25% 25

5 Viva Voce 20% 20

Total 100% 100

Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50

marks.


PO1 PO2 PO3

CO1 3 3

CO2 2 3

CO3 3


IT83


1. Choose a problem applying relevant knowledge and skills acquired during the course. Formulate

the specifications of the project work, identify the set of feasible solutions, prepare, and execute

project plan considering professional, cultural and societal factors. Identify the problem-solving



experiments to Evaluate and critically examine the outcomes followed by concluding the results

and identifying relevant applications. Preparation of synopsis, preliminary report for approval of

topic selected along with literature survey, objectives and methodology.


GUIDELINES


and should involve scientific research, design, generation/collection and analysis of data,

determining solution and must preferably bring out the individual contribution.

The Dissertation work will have to be done by only one student and the topic of dissertation

must be decided by the guide and the student. The dissertation work shall be carried out, on-

campus or in an industry or in an organisation with prior approval from the Chairperson of the

Department. The student has to be in regular contact with the guide atleast once in a week.




Conclusion of literature survey, Objectives and Scope of Dissertation, Methodology to be

followed, Experimental requirements, References and Annexure.



Course Code 18IT3D01 M. Tech (Information Technology)


Course title DISSERTATION WORK PHASE -I

Scheme and Credits

No. of Hours/Week


0 0 10 0 5



IT84

COURSE OUTCOME:




of Dissertation.





CIE 1.Marks awarded by guide (Internal examiner) = 50 Marks


50*

Marks


examiner) and external examiner =100 Marks

50**

Marks





2. Literature survey and Conclusion = 30%

3. Objectives and Scope of Dissertation work = 30%

4. Methodology to be adopted = 20%

5. Presentation of contents of Dissertation work Phase-I = 10%

Rubrics for SEE:




4. Methodology, Experimental /Software = 20%



PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

1. Low, 2.Medium, 3. High

IT85




effectively.






GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50




Course title SEMINAR - IV

Scheme and Credits

No. of Hours/Week


0 0 2 0 1



IT86


PO1 PO2 PO3

CO1 2 2 3 CO2 2 3 3 CO3 2








Review of literature : 35%


Report : 25%

IT87







phase I and II.

GUIDELINES









COURSE OUTCOMES:




hypothesis.

2. Investigate and assess the results obtained within the scope of experiments conducted followed

by conclusions.

Course Code 18IT4D01 M. Tech (Information Technology)


Course title DISSERTATION WORK PHASE -II

Scheme and Credits

No. of Hours/Week


--- --- 30 --- 15



IT88

3. Prepare a detailed technical document, Present and defend the contents of Dissertation work

in presence of technically qualified audience effectively.


CIE




100

marks

50*

marks

SEE


2. Dissertation evaluation by External examiner =100 marks



300

marks

50**

marks


** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for 50

marks.

Rubrics for CIE:

1.Presentation of background of dissertation work = 10%

2.Literature survey, Problem formulation and Objectives = 30%

3.Presentation of methodology and Experimentation = 30%

4.Results and Discussion = 20%

5.Questions and Answers = 10%

Rubrics for SEE:


2. Literature survey = 15%

3. Problem formulation, Objectives and Scope of Work = 10%

4. Methodology, Experimentation/Theoretical modelling = 10%



7. Submission/Publication of technical paper for Publication/ Presentation in Journals/Conference

= 20%


PO1 PO2 PO3

CO1 3 2 3

CO2 2 2 3

CO3 3 3 3


CN1






M.Tech in Computer Science and Engineering

Specialization: Computer Networking

CN1


VISION

“To strive for excellence in education for the realization of a vibrant and

inclusive society through knowledge creation and dissemination”

MISSION



Pursue academic excellence through high quality research and

publications


Inculcate right values among students while encouraging

competitiveness to promote leadership qualities




CN2

Bangalore University



University Visvesvaraya College of Engineering (UVCE) was started as a School of

Mechanical Engineering by Bharat Ratna Sir. M. Visvesvaraya in the year 1913 to meet the

needs of the State for skilled workers with S V Setty as its Superintendent. Later, it was

converted to a full-fledged Engineering College in the year 1917 under the name Government

Engineering College and was affiliated to the University of Mysore. It is the fifth Engineering

College to be established in the country.

After the formation of Bangalore University in 1964, UVCE became one of the

Constituent Colleges of Bangalore University. This is one of the oldest Institutions in the

country imparting technical education leading to B.E., M.E, B.Arch., M.Sc. (Engineering),

M.Arch. and Ph.D. degrees in various disciplines of Engineering and Architecture. The

Institution currently offers 7 Undergraduate (B.E. / B.Arch.) Full-time, three Undergraduate

(B.E.) Part-time and 24 Postgraduate (M.E. / M.Arch.) Programmes.

VISION

The vision of UVCE is to strive for excellence in advancing engineering education through

path breaking innovations across the frontiers of human knowledge to realize a vibrant,

inclusive and humane society.

MISSION

The mission of UVCE is to prepare human resource and global leaders to achieve the above

vision through discovery, invention and develop friendly technologies to promote scientific

temper for a healthy society. UVCE shapes engineers to respond competently and confidently

to the economic, social and organizational challenges arising from globally advancing

technical needs.

CN3



M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme -

2K18

Specialization: Computer Networking


Strive for Centre of Excellence in advancing Computer Science and Engineering education to

produce highly qualified human resources to meet local and global requirement.


CSM1. Implementing effectively, the outcome based education by imparting knowledge of

basics and advances in Computer Science and Engineering and other allied disciplines.

CSM2. Preparing and equipping human resources to become global leaders through

innovation, discovery, sustainable and environment friendly technology.


interdisciplinary research, online courses, interaction with institutions of higher learning and

industries, R and D laboratories of national importance, alumni, employers and other internal &

external stake holders.

CSM4. Imbibing awareness of entrepreneurship, ethics, honesty, credibility, social and

environmental consciousness and providing opportunity to the faculty and technical staff for

continuous academic improvement and to equip them with then latest trends in Software

Engineering and thereby inculcate the habit of continuous learning in faculty, staff and

students.

CN4

Program Outcomes

Computer Networking Graduates will be able to:

CNPO1: An ability to independently carry out research/investigate and development work to

solve practical problems

CNPO2: An ability to write and present a substantial technical report/document

CNPO3: Students should be able to demonstrate a degree of mastery over the area as per the

specialization of the problem. The mastery should be at a level higher than the

requirements in the appropriate bachelor degree

Program Educational Objectives (PEO):

The postgraduates of M.Tech in Computer Networking will provide the knowledge and skill

to:

CNPEO1: Possess strong fundamentals of computer networks, develop analytical and

computational skills to solve real time hardware / software problems and apply

innovative technical techniques to develop solutions in an ever changing world.

CNPEO2: Develop ability to establish peer-recognized expertise in the field of Computer

Networking and excel in research by articulating this expertise in formulating and

solving new problems using mathematical foundations, algorithmic principles and

computer networking concepts.

CNPEO3: Demonstrate leadership capabilities to communicate, collaborate, inspire, innovate

and be the leaders in the field of Computer Networking.

CN5

BANGLORE UNIVERSITY


M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (COMPUTER NETWORKING) under CBCS

Scheme – 2K18

Semester I



Teaching scheme

Hrs/Week Teaching

DPT

Total

Hrs/week

CIE

Marks

*SEE

Marks Credits

L T P S



3 18CN1C03 TCP / IP 4 0 0 0 CSE 4 50 50 4

4

18CS1E1A Cloud Computing

4 0 0 0

CSE

CSE

CSE

4 50 50 4 18CS1E1B Mobile Computing

18CS1E1C Wireless Networks

5


CSE

CSE

4 50 50 4 18CS1E2B Advances in Storage Area Networks 4 0 0 0

18CN1E2C Distributed Database Systems 4 0 0 0



8 18CN1S01 Seminar - I 0 0 2 0 CSE 2 50 -- 1

9 18CS1M02 Audit Course - I (Technical Paper Writing) 2 0 0 0 English 2 50 -- 1

Total 30 450 350 26

CN6

Semester II



Teaching scheme

Hrs/Week Teaching

DPT

Total

Hrs/week

CIE

Marks

*SEE

Marks Credits

L T P S



3 18CN2C03 Internet of Things 3 0 2 0 CSE 4 50 50 4

4

18CS2E1A Data Warehousing and Mining

4 0 0 0

CSE

CSE

CSE

4 50 50 4 18CS2E1B Stochastic Process and Queuing Theory

18CN2E1C Optimization Techniques

5

18CS2E2A Network Security

4 0 0 0

CSE

CSE

CSE

4 50 50 4 18IT2E2B Cyber Security

18CS2E2C Web Security


7 18CN2S01 Seminar - II 0 0 2 0 CSE 2 50 -- 1

8 18CS2M01 Audit Course - II (Pedagogy Studies) 2 0 0 0 CSE 2 50 -- 1

Total 28 400 300 24

CN7

Semester III



Teaching scheme

Hrs/Week Teaching

DPT

Total

Hrs/week

CIE

Marks

*SEE

Marks Credits

L T P S

1

18CS3E1A Machine Learning 4 0 0 0

CSE 4 50 50 4 18CS3E1B Big Data Analytics 3 0 2 0

18CS3E1C High Performance Computing 4 0 0 0

2 Open Elective 4 0 0 0 CSE 4 50 50 4

3 18CN3S01 Seminar - III 0 0 2 0 CSE 2 50 1

4 18CN3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5

5 18CN3D01 Dissertation Phase - I 0 0 10 0 CSE 10 50 50 5

Total 30 250 200 19

Open Elective




Teaching

Dept.

Total hrs

/ week

CIE

Marks

*See

Marks Credits

L T P S

1




2


4 0 0 0 Civil 4 50 50 4 18CV3P1B Water Laws, Rights and Administration


18CV3P1D Remote Sensing and Geographic Information System



4




5




CN8

COURSE TYPE

CS: COMPUTER SCIENCE AND ENGINEERING CN: COMPUTER NETWORK C: PROFESSIONAL CORE

E: PROFESSIONAL ELECTIVE P: OPEN ELECTIVE M: MANDATORY AUDIT

L: LABORATORY S: SEMINAR I: INTERNSHIP / MINI PROJECT

D: DISSERTATION

L – Theory lecture, T – Tutorial, P – Lab work, S – Self study:


NOTE:

1) In any course (Program Core or Program Elective), if self study of 4 hrs per week for students is allocated, then the teaching scheme of

such courses will be 3-0-0-4 and the total credits will be 4.

2) * = SEE shall be conducted for 100 marks and the marks obtained shall be reduced to 50 marks.

3) # = The CIE test of the lab component of integrated course shall be conducted with the external examiner for 50 marks and shall be

reduced to 25 marks.

Semester IV



Teaching scheme

Hrs/Week Teaching

DPT

Total

Hrs/week

CIE

Marks

SEE

Marks Credits

L T P S

1 18CN4S01 Seminar - IV 0 0 2 0 CSE 2 50 1

2 18CN4D01 Dissertation Phase - II 0 0 30 0 CSE 30 50 50 15

Total 32 100 50 16



CN8

I Semester

CN9

Course Code 18CS1C01 M. Tech (Computer Networking)

Category Professional Core


Scheme and

Credits


L T P S Credits

3 1 - - 4





COURSE OBJECTIVES






set.






Chinese-Reminder Theorem, Fermat‟s Little theorem, The sum and number of Divisors, The











(K/FIFO).






Application.

UNIT -IV RECURRENCE RELATIONS: 09 Hours

Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of



CN10





Hamiltonian Graphs, Multi-graphs, Planar Graphs, Euler„s Formula, Graph Colouring and




units

REFERENCES

1. David M Burton, “Elementary Number Theory”, Allyn and Bacon, 1980.

2. K. S. Trivedi, “Probability and Statistics with Reliability, Queuing for Computer

Science Applications”, John Wiley and Sons, II Edition, 2008.

3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, “Queuing Networks

and Markov Chains”, John Wiley and Sons, II Edition, 2006.



5. J. A. Bondy and U. S. R. Murty, “Graph Theory and Applications”, Macmillan

Press, 1982.

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 1

CO2 2

CO3 1 1

CO4 1

CO5 2


CN11




Scheme and

Credits

No. of

Hours/Week

Semester – I

L T P SS Credits

4 0 - - 4

CIE Marks:

50


Hrs


COURSE OBJECTIVE











UNIT II- DATA LINK LAYER 10 Hours

Multiple Access Control Protocols(MAC), ALOHA, CSMA/CD (802.3), Data Link

Protocol- HDLC,PPP, Wired LAN‟s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,

Wireless LAN‟s(802.11), Broadband Wireless(802.16).

UNIT III- ROUTING AND CONGESTION CONTROL 10 Hours

Design issues, Routing Algorithms, Distance Vector Routing, Link State Routing, Routing







Management – Exponential RTO back-off – KARN‟s Algorithm – Window









Networks(SDN).

CN12

REFERENCES

1. Behrouz A Forouzan and Firouz Mosharraf, “Computer Networks, A Top-Down

Approach”, TMH, 2012.

2. Andrew S. Tanenbaum and David J. Wetherall, “Computer Networks”, Pearson

Education, 5th Edition,2011.

3. William Stallings, “High Speed Networks and Internet”, , Second Edition, 2012.

4. Prakash C Guptha, “Data Communication and Computer Networks”, PHI , 6th

printing 2012.

5. Larry L. Peterson and Bruce S Davis , “Computer Network A System

Approach”, Elsevier, 5th

edition 2010.

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks

Mapping of Course Outcomes (COs) to Program Outcomes (POs)

PO1 PO2 PO3

CO1 2

CO2 2

CO3 3 2 2

CO4 3 2

CO5 2 2 2

1:Low, 2:Medium, 3:High





CN13

Course Code 18CN1C03 M. Tech (Computer Networking)


Course title TCP/IP PROTOCOLS


L T P S Credits

4 0 - - 4



1. Basics of Computer Networks

2. Layered Architecture

Course Objectives:


1. Acquire the concept of packet switching and addressing.

2. Understand the IP packet format, operations and various IP network protocols.

3. Acquire the knowledge of transport layer services and TCP protocols.

4. Learn other important network protocols, future needs and challenges.

5. Review the recent development in wireless network and sensor network.

UNIT I - OSI MODEL AND TCP/IP PROTOCOL SUITE 09 hours

The OSI Model, TCP/IP Protocol Suite, Architecture, Addressing, Wired Local Area

Networks, Wireless LANS, Point to Point WANS, Switched WANS, Connecting Device.

UNIT II - NETWORK LAYER 09 hours

Switching: Packet Switching, Network: Network Layer Services, Network Layer Issues,

Classful Addressing, Classless Addressing, Special Addresses, NAT Delivery, Forwarding

Structure of a Router.-– 08 Hours

UNIT III - INTERNET PROTOCOL 10 hours

Datagram, fragmentation, options, checksum, IP package, Address Mapping, ARP Protocol,

ARP Package, RARP, ICMP Protocol, Messages, Debugging Tools, ICMP Packages.

UNIT IV - TCP PROTOCOL 10 hours

Transport Layer Services, TCP Protocols, TCP Connection, State Transition Diagrams,

Windows in TCP, flow, congestion and error control, TCP package and operation.

UNIT V - OTHER IMPORTANT PROTOCOLS 10 hours

DHCP, DNS, TELNET, FTP, SMTP, POP, IPv6

UNIT VI

Recent developments in wireless networks, sensor networks and Internet of things

REFERENCES

1. Behrouz A. Forouzan, “TCP/IP Protocol Suite”, IV Edition, McGraw Hill, 2010.

2. Kevin R Fall and W. Richard Stevens, “TCP/IP Illustrated, Volume 1 The Protocols”,

II Edition, Addison-Wesley Professional Computing Series.

3. Douglas E Comer, “Internetworking with TCP/IP: Principles, Protocols and

Architectures, IV Edition, Prentice Hall, 1995.

4. Charles M Kozierok, “The TCP/IP Guide: A Comprehensive, Illustrated, Internet

Protocols Reference”, No Starch Press, 2005.

COURSE OUTCOMES


CO1. Distinguish the OSI and TCP/IP Protocol Stack

CO2: Demonstrate various network and transport layer services

CO3: Implement the IP protocols

CO4: Implement TCP protocols

CO5: Ivestigate TCP/IP in wireless and sensor network TCP/IP.

CN14


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE –

100

marks




marks




marks.


PO1 PO2 PO3

CO1 2

CO2 2

CO3 1

CO4 1

CO5 1 2


CN15

Course Code 18CS1E1A M. Tech (Computer Networking)

Category Professional Elective



L T P S Credits

4 0 - - 4





COURSE OBJECTIVES





4. Acquire the knowledge the cloud resource management and scheduling mechanism

5. Learn various security issues in cloud computing.







UNIT- II CLOUD COMPUTING: APPLICATIONS AND PARADIGMS 09 Hours

Challenges for Cloud Computing, Existing Cloud Applications and New Application





UNIT-III CLOUD VIRTUALIZATION 10 Hours

Virtualization, Layering and Virtualization, Virtual Machine Monitors, Virtual Machines,





UNIT-IV CLOUD RESOURCE MANAGEMENT AND SCHEDULING 10 Hours

Policies and Mechanisms for Resource Management, Applications of Control Theory to Task








CN16

UNIT-V CLOUD SECURITY 10 Hours

Cloud Security Risks, Security: The Top Concern for Cloud Users, Privacy and Privacy Impact



OS.


cloud computing.

REFERENCES

1. Dan C Marinescu, “Cloud Computing: Theory and Practice”, Morgan


2. George Reese, “Cloud Application Architectures: Building Applications and

Infrastructure in the Cloud”, O‟Reilly, 2009.

3. Rajkumar Buyya, James Broberg and Andrzej M. Goscinski , “Cloud Computing:

Principles and Paradigms”, Wiley, 2011.

4. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing: From

Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012.

COURSE OUTCOMES








CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




Total:100

marks




marks.


PO1 PO2 PO3

CO1 2 2

CO2 2

CO3 1 2

CO4 2 1

CO5 2 2


CN17

Course Code 18CS1E1B M. Tech (Computer Networking)




L T P S Credits

4 0 - - 4






COURSE OBJECTIVES:





synchronization.












UNIT-III DATABASES, DATA DISSEMINATION AND BROADCASTING SYSTEMS 10

Hours





video Broadcasting.





LANGUAGES 10 Hours






CN18


PO1 PO2 PO3

CO1 2

CO2 2

CO3 2

CO4 2 2

CO5 2 2


UNIT-VI

Recent trends in wireless and mobile network security, mobile cloud computing.

REFERENCES

1. Raj Kamal, “Mobile Computing”, Oxford University Press, 2007.

2. Ashok Talukder, Ms Roopa Yavagal, and Mr. Hasan Ahmed, “Mobile Computing,

Technology, Applications and Service Creation”, II Edition, Tata McGraw Hill, 2010.

3. Jochen Schiller, “Mobile Communications”, Addison-Wesley. II Edition, 2004.

4. Hansmann, Merk, Nicklous, Stober, “Principles of Mobile Computing”, Springer, II Edition,

2003.

COURSE OUTCOMES







computing.


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


– 100

marks




Total:100

marks




CN19

Course Code 18CS1E1C M. Tech (Computer Networking)



Scheme and

Credits


L T P S Credits

4 0 - - 4


50



Computer Networks

COURSE OBJECTIVES:




basic operations


























UNIT-VI RECENT TRENDS

Recent trends in Wireless networks, Vehicular Adhoc Networks.

CN20

REFERENCES




and Sons Inc 2002




6.Feng Zhao, leonidas Guibas, “Wireless sensor Networks: An information processing

approach”, Elsevier, 2004

COURSE OUTCOMES









CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




Total:100

marks




50 marks


PO1 PO2 PO3

CO1 2 3

CO2 2 3

CO3 2 3

CO4 3 3

CO5 1 3


CN21


Category Professional Elective - Integrated


Scheme and

Credits


L T P S Credits

3 0 2 0 4

CIE Marks:

50


100




COURSE OBJECTIVES:









09 Hours

Evolution of Computing: Soft Computing Constituents, Conventional AI to

Computational Intelligence: Machine Learning Basics, Hard-Margin and Soft-Margin

SVMs- Concepts of Kernel and Feature Spaces, Basics of Optimization and Quadratic

programming, Introduction to Steganography and Applications of SVMs to Steganalysis

UNIT-II NEURAL NETWORKS 10 Hours

Introduction to ANN, Architectures, Learning methods, Bayesian Networks, Back

Propagation network, Perceptrons, Hopfield Networks, Kohonen Self Organizing Feature

Maps, Chaos Theory









Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions: Fuzzy Rules and

Fuzzy Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision

Making, Defuzzification





UNIT-VI

Recent Trends in deep learning, various classifiers, networks and genetic algorithm.


CN22





Network.






input pattern.




optimum path.



REFERENCES

1. S. N. Sivanandam and S. N. Deepa, “Principles of Soft Computing”, 2nd

Edition,

Wiley India, 2012.

2. Samir Roy, Udit Chakraborty, “Introduction to Soft Computing- Neuro-Fuzzy and

Genetic Algorithms”, First Edition, 2013.

3. David E Goldberg, “Genetic Algorithms in Search Optimization and Machine

Learning”, Addison Wesley, 1997.


COURSE OUTCOMES









Problem.


CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50


choice

20*3=60

Marks

Total:

CN23

SEE-100

Marks

Marks 100


internal choice

20*2=40

Marks


for 50 marks








PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


CN24

Course Code 18SE1E2B M. Tech (Computer Networking)




L T P S Credits

4 0 - - 4



1.Computer Networks


3.Operating Systems

COURSE OBJECTIVES














UNIT -II I/O TECHNIQUES: 10 Hours The Physical

I/O path from the CPU to the Storage System; SCSI; Fibre Channel Protocol Stack; Fibre

Channel SAN; IP Storage. Network Attached Storage: The NAS Architecture, The NAS

hardware Architecture, The NAS Software Architecture, Network connectivity, NAS as a



UNIT -III STORAGE VIRTUALIZATION: 10 Hours Definition of

Storage virtualization; Implementation Considerations; Storage virtualization on Block or file

level; Storage virtualization on various levels of the storage Network; Symmetric and

Asymmetric storage virtualization in the Network.




Software Components of SAN: The switch‟s Operating system; Device Drivers; Supporting the

switch‟s components; Configuration options for SANs.





CN25


REFERENCES


India 2013.

2. Robert Spalding: “Storage Networks The Complete Reference”, Tata McGraw-Hill, 2011.



4. Richard Barker and Paul Massiglia: “Storage Area Network Essentials A Complete Guide to

understanding and Implementing SANs”, Wiley India, 2006.

COURSE OUTCOMES :








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*2=40

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 1 2


CN26

Course Code 18CN1E2C M. Tech (Computer Networking)


Course title DISTRIBUTED DATABASE SYSTEMS


L T P S Credits

4 0 - - 4



Course Objectives


1. Understand the fundamental concepts and issues of managing large volume of shared data in a

parallel and distributed environment.

2. Acquire the knowledge of distributed and parallel database concepts.

3. Handle and mange queries in a distributed environment.

4. Learn the various causes for failure and measures to achieve reliability.

5. Acquire the knowledge of the pros and cons of various database systems.


Distributed data processing; What is a DDBS; Advantages and disadvantages of DDBS; Problem

areas; Overview of database and computer network concepts, Distributed Database Management

System Architecture: Transparencies in a distributed DBMS; Distributed DBMS architecture;

Global directory issues.

UNIT II- DISTRIBUTED DATABASE DESIGN 10 Hours

Alternative design strategies; Distributed design issues; Fragmentation; Data allocation, Semantics

Data Control View Management; Data security; Semantic Integrity Control, Query Processing

Issues: Objectives of query processing; Characterization of query processors; Layers of query

processing; Query decomposition; Localization of distributed data.

UNIT III - DISTRIBUTED QUERY OPTIMIZATION 10 Hours

Factors governing query optimization; Centralized query optimization; Ordering of fragment

queries; Distributed query optimization algorithms, Transaction Management: The transaction

concept; Goals of transaction management; Characteristics of transactions; Taxonomy of

transaction models, Concurrency Control: Concurrency control in centralized database systems;

Concurrency control in DDBSs; Distributed concurrency control algorithms; Deadlock

management.

UNIT IV - RELIABILITY 09 Hours

Reliability Concepts and Measures: System, State, and Failure, Reliability and Availability, Mean

Time between Failures/Mean Time to Repair, Failures in Distributed DBMS: Transaction Failures,

Site (System) Failures, Media Failures, Communication Failures, Local Reliability Protocols:

Architectural Considerations, Recovery Information Execution of LRM Commands,

Checkpointing, Handling Media Failures, Distributed Reliability Protocols: Components of

Distributed Reliability Protocols, Two-Phase Commit Protocol, Variations of 2PC, Dealing with

Site Failures : Termination and Recovery Protocols for 2PC, Three-Phase Commit Protocol.

UNIT V - PARALLEL DATABASE SYSTEMS 10 Hours

Parallel Database System Architectures: Objectives, Functional Architecture, Parallel DBMS

Architectures, Parallel Data Placement, Parallel Query Processing: Query Parallelism, Parallel

Algorithms for Data Processing, Parallel Query Optimization, Load Balancing: Parallel Execution

Problems, Intra-Operator Load Balancing, Inter-Operator Load Balancing, Intra-Query Load

Balancing, Database Clusters: Database Cluster Architecture, Replication, Load Balancing, Query

Processing , Fault-tolerance.

CN27

UNIT VI - Recent trends in Mobile Databases, Distributed Object Management, Multi-databases

REFERENCES

1. Principles of Distributed Database Systems, M. Tamer Ozsu Patrick Valduriez, 3rd

Edition,

Springer, 2011.

2. Distributed Databases principles and systems, Stefano Ceri, Giuseppe Pelagatti, Tata

McGraw Hill, Indian Edition, 2017.

3. Database System Concepts, Henry Korth, Abraham Silberschatz, S. Sudarshan, 5th

Edition,

2012.

4. Distributed Database Systems, D. Bell and J. Grimson, Addison-Wesley, 1992.

COURSE OUTCOMES


CO1: Design and Analyse Queries in Distributed Database Systems.

CO2: Efficiently retrieve information from database and references:

CO3: Balance the load effectively among parallel and distributed environment.

CO4: Effectively adopt reliability and recovery techniques in real time applications.

CO5: Apply and design suitable methods to achieve reliability in various stages of distributed and

parallel database.


CIE – 50

marks


marks

Unit-VI = 15 marks

Total:50


SEE – 100

marks




marks





PO1 PO2 PO3

CO1 2

CO2 2

CO3 2 2

CO4 2 2

CO5 3 2 2


CN28

Course Code 18CS1L01 M. Tech (Computer Networking)

Category Laboratory


Scheme and

Credits


L T P S Credits

- - 4 - 2





3. NS-3 simulator

COURSE OBJECTIVES







PART – A







6. DHCP protocol.


8. Dijkstra‟s shortest path routing protocol.

PART – B









MAC layer).

COURSE OUTCOMES



Java.

CO2: Implement and analyze a typical wired network using Java.




CN29


The student has to write and implement two programs selecting ONE from each part

Continuous Internal


– 50 Marks)





20 Write up 10



70


Total 100


Note. * = SEE shall be conducted for 100 marks for practical and the marks obtained shall be

reduced for 50 marks


PO1 PO2 PO3

CO1 1

CO2 2

CO3 2

CO4 2 2 3

CO5 2 2


CN30

Course Code 18CS1M01 M. Tech (Computer Networking)

Category Mandatory Audit



L T P SS Credits

2 0 - - 2



COURSE OBJECTIVES


1. Understand the formulation of research problem, scope and objectives of research problem





UNIT -I RESEARCH PROBLEM: 3 Hours

Meaning of research problem, Sources of research problem, Criteria Characteristics of a good

research problem, Errors in selecting a research problem, Scope and objectives of research problem.

Approaches of investigation of solutions for research problem, data collection, analysis,

interpretation, Necessary instrumentations



UNIT- III EFFECTIVE TECHNICAL WRITING: 6 Hours

Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of research


UNIT- IV NATURE OF INTELLECTUAL PROPERTY: 6 Hours

Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological research,



UNIT- V PATENT RIGHTS: 6 Hours

Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases. Geographical

Indications.

UNIT- VI NEW DEVELOPMENTS IN IPR:

Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer Software


REFERENCES

1. Stuart Melville and Wayne Goddard, “Research methodology: an introduction for science &

engineering students‟”

2. Wayne Goddard and Stuart Melville, “Research Methodology: An Introduction”

3. Ranjit Kumar, 2nd Edition, “Research Methodology: A Step by Step Guide for beginners”

Halbert, “Resisting Intellectual Property”, Taylor & Francis Ltd ,2007.

4. Mayall, “Industrial Design”, McGraw Hill, 1992.

5. Niebel, “Product Design”, McGraw Hill, 1974.

6. Asimov, “Introduction to Design”, Prentice Hall, 1962.

CN31

7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, “ Intellectual Property in New

Technological Age”, 2016.

8. T. Ramappa, “Intellectual Property Rights Under WTO”, S. Chand, 2008

COURSE OUTCOMES







in particular.







CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4

CO5 3 3


CN32



1. Carry out the literature review of general research area/current topic and analyse the

same effectively.



3. Acquire communication and time management skills for effective and impactful

presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,

adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear


GUIDE LINES


2. Topic may be of general/ specific interest to program of engineering or electives not

offered in the semester and to be selected in consultation with the faculty/Guide.



COURSE OUTCOMES:





Overcome inhibition in interacting with peers and hence develop the spirit of team

work. Overcome stage fear during the presentation.


CIE – 50

marks


Marks =20

Total:50


Course Code 18CN1S01 M. Tech (Computer Networking)

Category Seminar Semester: I

Course title SEMINAR - I

Scheme and Credits

No. of Hours/Week


0 0 2 0 1



CN33


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2



Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee

shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member

nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:



Review of literature and technical content : 35%


Report : 25%

CN34

Course Code 18CS1M02 M. Tech (Computer Networking)

Category Audit Course-I

Course title TECHNICAL PAPER WRITING


L T P SS Credits

2 0 - - 1



COURSE OBJECTIVES



2. Apply key skill while writing research paper and know about what to write in each section




UNIT-I PLANNING AND PREPARATION: 6 Hours

Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs and

Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness

UNIT- II CLARIFYING: 3 Hours

Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising, Paraphrasing and

Plagiarism, Sections of a Paper, Abstracts. Introduction

UNIT- III REVIEW OF THE LITERATURE: 6 Hours

Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.

UNIT- IV KEY SKILLS: 6 Hours

Key skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills

are needed when writing an Introduction, skills needed when writing a Review of the Literature,


skills are needed when writing the Methods, skills needed when writing the Results, skills are needed

when writing the Discussion, skills are needed when writing the Conclusions.

UNIT- VI NEW DEVELOPMENTS IN RESEARCH PAPER WRITING:

useful phrases, how to ensure paper is as good as it could possibly be the first- time submission

REFERENCES

1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books)

2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press


Highman‟sbook.

4. Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht

Heidelberg London, 2011

COURSE OUTCOMES





CN35




CIE -50

Marks



Quiz/AAT=20 Marks

Total: Marks

50


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


CN34

Semester II

CN35




Scheme and

Credits


L T P S Credits

4 0 - - 4



COURSE OBJECTIVE

















in a DAG; Johnson‟s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson

method. .








UNIT VI


structures

REFERENCES

1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,”

Introduction to Algorithms”, Third Edition, Prentice-Hall, 2011.

2. M T Goodrich, Roberto Tamassia, “Algorithm Design”, John Wiley, 2002.

3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in C++”, 4th

Edition,

Pearson, 2014.



5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, “Fundamentals of Data Structures in C”,

Silicon Pr, 2007.



CN36

COURSE OUTCOMES





problems





CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 2

CO2 2 2

CO3 2 2

CO4 2

CO5 2 2


CN37




Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES





3. Analyze the Distributed Deadlock Detection Algorithms and Agreement Protocols.












Concept of Lamport‟s Logical Clock and Vector Clocks, Termination Detection. A simple

solution to distributed mutual exclusion, Non Token based algorithms: Lamport‟s algorithm,

Ricart Agarwala‟s algorithm, Maekawa‟s algorithm, Token based algorithms: Suzuki Kasami‟s

broadcast algorithm, Raymond‟s tree based algorithm.




Detection Algorithm: Ho-Ramamoorthy‟s Algorithm, Distributed Deadlock Detection


Detection Algorithms: Menasce- Muntz Algorithm, Ho-Ramamoorthy‟s Algorithm.


Problem- Lamport -Shostak- Pease Algorithm, Dolev et al.‟s Algorithm

UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE: 10 Hours

Issues in Load Distribution, Components of a Load Distributing Algorithms, Load Distributing






CN38






REFERENCES


Mcgraw Hill, 2002.


and Sons, 2018.


Hall India, 2017.




COURSE OUTCOMES












CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*2=40

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 1 -

CO2 1 2

CO3 1 2

CO4 1 3

CO5 3 2 2


CN39

Course Code 18CN2C03 M. Tech (Computer Networking)

Category Professional Core - Integrated


Scheme and

Credits


L T P S Credits

3 0 2 - 4

CIE Marks:

50


100




COURSE OBJECTIVES




using NETCONF-YANG.


and Raspberry Pi.


services.














URLLib, SMTPLib.















UNIT VII (Lab Programs)



CN40






Arduino.




11. WAP for LED blink using Raspberry Pi. 12. Study and Implement Zigbee Protocol using Arduino / Raspberry Pi.

REFERENCES

1. Arshdeep Bahga and Vijay Madisetti, “Internet of Things: A Hands-on

Approach”, University Press, 2015.

2. Peter Waher, “Learning Internet of Things”, PACKT Publishing, 2015.

3. Adrian McEwen and Hakim Cassimally, “Designing Internet of Things”, John


COURSE OUTCOMES








CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks







CN41


PO1 PO2 PO3

CO1 1 2

CO2 1

CO3 3

CO4 1

CO5 2


CN42




Scheme and

Credits


L T P S Credits

4 0 - - 4









architecture.

UNIT I - INTRODUCTION TO DATA MINING: 9 Hours

Data Mining Functionalities, Data Pre-processing, Data Cleaning, Data Integration and


Association Rule Mining: Efficient and Scalable Frequent Item set Mining Methods, Mining

Various Kinds of Association Rules, Association Mining to Correlation Analysis, Constraint-Based

Association Mining, Handling categorical, Continuous Attributes, Concept hierarchy, Sequential

and Sub graph Patterns.

UNIT II - CLASSIFICATION AND PREDICTION: 10 Hours

Issues Regarding Classification and Prediction, Classification by Decision Tree Introduction,

Bayesian Classification, Rule Based Classification, Classification by Back propagation, Support

Vector Machines, Associative Classification, Lazy Learners, Other Classification Methods,

Prediction, Accuracy and Error Measures, Evaluating the Accuracy of a Classifier or Predictor,

Ensemble Methods, Model Section.

UNIT III - CLUSTER ANALYSIS: 10 Hours

Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning

Methods, Hierarchical methods, Density-Based Methods, Grid-Based Methods, Model-Based

Clustering Methods, Clustering High-Dimensional Data, Constraint-Based Cluster Analysis,

Outlier Analysis, Quality and validity of Cluster Analysis.

UNIT IV - MINING OBJECT, SPATIAL, MULTIMEDIA, TEXT AND WEB DATA: 9

Hours

Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial Data

Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web, Stream Data

Mining, Social Network Analysis.

UNIT V – DATA WAREHOUSING AND BUSINESS ANALYSIS: 10 Hours

Data warehousing Components, Building a Data warehouse, Mapping the Data Warehouse to a

Multiprocessor Architecture, DBMS Schemas for Decision Support, Data Extraction, Cleanup, and

Transformation Tools, Metadata, reporting, Query tools and Applications, Online Analytical

Processing (OLAP), OLAP and Multidimensional Data Analysis.

CN43

UNIT VI - Recent Trends in Distributed warehousing and Data Mining, Class Imbalance Problem,

Graph mining, Social Network Analysis.

REFERENCES REFERENCES

1. Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition,

Elsevier, 2011.

2. Vipin Kumar, Introduction to Data Mining - Pang-Ning Tan, Michael Steinbach, Addison Wesley, 2006.


4. Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAP”, Tata McGraw – Hill

Edition, Tenth Reprint 2007.

5. K.P. Soman, Shyam Diwakar and V. Ajay “Insight into Data Mining Theory and Practice”, Easter Economy

Edition, Prentice Hall of India, 2006.

G. K. Gupta “Introduction to Data Mining with Case Studies”, Easter Economy Edition, Prentice Hall of India,

2006. COURSE OUTCOMES


CO1. Demonstrate the concept of data mining principles, data warehousing Architecture and its

Implementation

CO2. Apply the association rules, design and deploy appropriate classification techniques for

mining the data





CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 3

CO2 2

CO3 3

CO4 2

CO5 3


CN44





L T P S Credits

4 0 - - 4




COURSE OBJECTIVES:







UNIT-I INTRODUCTION TO STOCHASTIC PROCESSES AND MARKOV CHAINS 09 Hours

Introduction, Specifications, Classification of Stochastic Processes, Stationary Process, Poisson Processes,



homogenous Chains.





for Wiener Process.



Server Queues (M|M|c), Choosing the Number of Servers, Queues with Truncation (M|M|c|K), Erlang‟s Loss






UNIT-V GENERAL ARRIVAL OR SERVICE PATTERNS 10 Hours

General Service, Single Server (M|G|1), General Service, Multi-server (M|G|c|∙, M|G|∞), General Input

(G|M|1, G|M|c).


REFERENCES

1. Jyothiprasad Medhi, “Stochastic Processes”, New Age International Publishers, II Edition, 2002.

2. Kishore S. Trivedi, “Probability and Statistics with Reliability, Queuing and Computer Science

Applications”, John Wiley and Sons, II Edition, 2008.

3. Donald Gross, John F. Shortle, James M. Thomson, and Carl M. Harris, “Fundamentals of Queuing

Theory”, John Wiley and Sons, IV Edition, 2008.

4. Oliver Knill, “Probability Theory and Stochastic Processes with Applications”, Overseas Press, 2009.

CN45


PO1 PO2 PO3

CO1 1

CO2 2

CO3 2

CO4 2

CO5 1


COURSE OUTCOMES







CO5:Investigate the performance analysis of data networks


CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




Total:100

marks




CN46

Course Code 18CN2E1C M. Tech (Computer Networking)


Course title OPIMIZATION TECHNIQUES

Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



1. Basics of Operations Research

COURSE OBJECTIVES


1. Understand the mathematical formulation of real world problems.

2. Understand linear programming and its associated methods.

3. Understand non-linear programming and its associated methods.

4. Understand unconstrained optimization techniques and apply them in solving


5. 5. Review of recent developments in geometric and dynamic programming

UNIT I – CLASSICAL OPTIMIZATION TECHNIQUES 10 Hours

Engineering Applications of Optimization, Statement of an Optimization Problem,

Classification of Optimization Problems, Single-Variable Optimization, Multivariable

Optimization with No Constraint, Multivariable Optimization with Equality Constraints,

Solution by Direct Substitution, Constrained Variation and Lagrange Multipliers,

Multivariable Optimization with Inequality Constraints: Kuhn–Tucker Conditions,

Constraint Qualification, Convex Programming Problem.

UNIT II – LINEAR PROGRAMMING 09 Hours

Simplex Method, Revised Simplex Method, Duality in Linear Programming,

Decomposition Principle, Sensitivity or Post-optimality Analysis. (only concepts, no

problems).

UNIT III – NON-LINEAR PROGRAMMING 09 Hours

Elimination Methods: Unrestricted Search, Search with Fixed Step Size, Search with

Accelerated Step Size, Exhaustive Search, Dichotomous Search, Interval Halving

Method, Fibonacci Method, Golden Section Method. Interpolation Methods: Quadratic

Interpolation Method, Cubic Interpolation Method, Direct Root Methods, Newton

Method, Quasi-Newton Method, Secant Method.

UNIT IV – UNCONSTRAINED OPTIMIZATION TECHNIQUES – I 10 Hours

Classification of Unconstrained Minimization Methods, General Approach, Rate of

Convergence, Scaling of Design Variables, Direct Search Methods: Random Search

Methods, Random Jumping Methods, Random Walk Method, Random Walk Method

with Direct Exploitation, Grid Search Method, Univariate Method, Powell‟s Method

UNIT V – UNCONSTRAINED OPTIMIZATION TECHNIQUES – II 10 Hours

Indirect Search Methods: Gradient of a Function, Evaluation of the Gradient, Rate of

Change of a Function along a Direction, Steepest Descent (Cauchy) Method, Conjugate

Gradient (Fletcher–Reeves) Method, Development of the Fletcher–Reeves Method,

Fletcher–Reeves Method, Newton‟s Method, Marquardt Method, Quasi-Newton

Methods, Rank 1 and Rank 2 Updates, Davidon–Fletcher–Powell Method, Broyden–

Fletcher–Goldfarb–Shanno Method, Test Functions

UNIT VI – Geometric Programming, Dynamic Programming.

REFERENCES

1. Singiresu S. Rao, “Engineering Optimization: Theory and Practice”, John Wiley

CN47

and Sons, IV Edition, 2009.

2. Edwin K P Chong and Stanislaw H Zak, “An Introduction to Optimization”, John

Wiley and Sons, IV Edition, 2010.

3. John K. Karlof, “Integer Programming: Theory and Practice”, CRC Press.

COURSE OUTCOMES


CO1: Formulate and use optimization techniques, model the real world problems and

simulate it.

CO2: Apply the concepts of linear and non-linear programming to engineering problems

and carry out sensitivity analysis.

CO3: Apply the concept of optimality criteria for various types of optimization problems.

CO4: Solve various constrained and unconstrained problems in single variable as well as

multivariable.

CO5: Categorise the problems related to dynamic and geometric programming.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 2

CO2 2

CO3 2

CO4 2

CO5 1


CN48




Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



COURSE OBJECTIVES





4: Analyze various issues of wireless security techniques.




















REFERENCES


Edition - 2017


2007.



5. KazemSohraby, Daniel Minoli and TaiebZnati, “wireless sensor networks -

Technology,

Protocols, and Applications”, Wiley Interscience 2007

6. Takahiro Hara,Vladimir I. Zadorozhny, and Erik Buchmann, “Wireless Sensor

NetworkTechnologies for the Information Explosion Era”, springer 2010

CN49

COURSE OUTCOMES


CO1: Analyze various security issues related to computer networks.

CO2: Implement various network security algorithms.

CO3: Design, Implement various security algorithms for distributed environment.

CO4: Analyze the security issues and apply the relevant algorithm to mitigate the same.

CO5: Analyze various security attacks in WSN.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 2 2

CO2 2

CO3 2 2

CO4 3 2

CO5 2 2


CN50

Course Code 18IT2E2B M. Tech (Computer Networking)


Course title CYBER SECURITY

Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



COURSE OBJECTIVES


1. Memorise basis of security concepts and security techniques.

2. Understand the cybercrime and law.

3. Identify and determine the motive and remedial measures for cybercrime, detection

and handling.

4. Analyze areas affected by cybercrime and identify Legal Perspectives in cyber

security.

5. Effectively design a secure cyber system.

UNIT I - INTRODUCTION TO SECURITY 09 Hours Introduction to Security: Need for security, Security approaches, Principles of security,



Symmetric & Asymmetric Key Cryptography: DES,RSA

UNIT II- INTRODUCTION TO CYBERCRIME 09 Hours

Cybercrime: Definition and Origins of the Word, Cybercrime and Information Security,

Cybercriminals, Classifications of Cybercrimes, Cybercrime: The Legal Perspectives,

Cybercrimes: An Indian Perspective, Cybercrime and the Indian ITA 2000, A Global

Perspective on Cybercrimes, Cybercrime Era: Survival Mantra for the Netizens.

Cyberoffenses: Criminals Plan: Attacks, Social Engineering, Cyberstalking, Cybercafe

and Cybercrimes, Botnets: The Fuel for Cybercrime, Attack Vector, Cloud Computing.

UNIT III CYBERCRIME: MOBILE AND WIRELESS DEVICES 10 Hours

Introduction, Proliferation of Mobile and Wireless Devices, Trends in Mobility, Credit

Card Frauds in Mobile and Wireless Computing Era, Security Challenges Posed by

Mobile Devices, Registry Settings for Mobile Devices, Authentication Service Security,

Attacks on Mobile/Cell Phones, Mobile Devices: Security Implications for organizations,

Organizational Measures for Handling Mobile, Organizational Security Policies and

Measures in Mobile Computing Era, Laptops.

UNIT IV- TOOLS AND METHODS USED IN CYBERCRIME 10 Hours

Introduction, Proxy Servers and Anonymizers, Phishing, Password Cracking, Keyloggers

and Spywares, Virus and Worms, Trojan Horses and Backdoors, Steganography, DoS

and DDoS Attacks, SQL Injection, Buffer Overflow, Attacks on Wireless Networks.

Phishing and Identity Theft : Introduction, Phishing, Identity Theft (ID Theft).

UNIT V- INTRODUCTION TO SECURITY POLICIES AND CYBER LAWS

10 Hours

CN51

Need for An Information Security Policy, Information Security Standards – ISO,

Introducing Various Security Policies and Their Review Process, Introduction to Indian

Cyber Law, Objective and Scope of the it Act, 2000, Intellectual Property Issues,

Overview of Intellectual - Property - Related Legislation in India, Patent, Copyright, Law

Related to Semiconductor Layout and Design, Software License.

UNIT VI - Recent developments in Security Policies and Cyber Laws

REFERENCES


Edition -

2017

2. Sunit Belapure and Nina Godbole, “Cyber Security: Understanding Cyber

Crimes, Computer Forensics And Legal Perspectives”, Wiley India Pvt Ltd,

ISBN: 978-81-265-21791, 2013.

3. Dr. Surya PrakashTripathi, RitendraGoyal, Praveen Kumar Shukla, KLSI.

“Introduction to information security and cyber laws”. Dreamtech Press. ISBN:

9789351194736, 2015.

4. Thomas J. Mowbray, “Cybersecurity: Managing Systems, Conducting Testing,

and Investigating Intrusions”, Copyright © 2014 by John Wiley & Sons, Inc,

ISBN: 978 -1-11884965 -1

5. I. A. Dhotre , “Cyber Forensics , Technical Publications; 1st Edition edition

(2016), ISBN- 13:978-9333211475


CO1: Interpret the basic concepts of cyber security, cyber law and their roles.

CO2: Articulate evidence collection and legal challenges

CO3: Discuss tools support for detection of various attacks.

CO4: Analyse various cyber risks.

CO5: Validate different cyber techniques in cyber system.


CIE –

50

mark

s


marks

Unit-VI = 15 marks

Total:

50


SEE

– 100

mark

s



internal choice. 20* 2 = 40 Marks Total:

100




50 marks.

CN52


PO1 PO2 PO3

CO1 3

CO2 2

CO3 2

CO4 2

CO5 2


CN53

Course Code 18CS2E2C M. Tech (Computer Networking)



Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



COURSE OBJECTIVES


1. Understand web application‟s vulnerability and malicious attacks.




5. Emphasis various basic concepts of Attacking Data Stores. .




Core Defense Mechanisms: Handling User Access Authentication, Session

Management, Access Control, Handling User Input, Varieties of Input Approaches to

Input Handling, Boundary Validation.



UNIT II: WEB APPLICATION TECHNOLOGIES 09 Hours

The HTTP Protocol, HTTP Requests, HTTP Responses, HTTP Methods, URLs, REST,





UNIT III: MAPPING THE APPLICATION 10 Hours

Enumerating Content and Functionality, Web Spidering, User-Directed Spidering,



Identifying Server-Side Technologies, Identifying Server-Side Functionality, Mapping

the Attack Surface.






Usernames, Predictable Usernames, Predictable Initial Passwords, Insecure Distribution

of Credentials. Attacking Access Controls.


CN54


Basic Vulnerability Injecting into Different Statement Types, Finding SQL Injection

Bugs, Fingerprinting the Database, The UNION Operator, Extracting Useful Data,

Extracting Data with UNION, Bypassing Filters, Second-Order SQL Injection, Advanced

Exploitation Beyond SQL Injection: Escalating the Database Attack, Using SQL

Exploitation Tools, SQL Syntax and Error Reference, Preventing SQL Injection.

UNIT VI Recent trends in Web Applications and its Security

REFERENCES

1. Defydd Stuttard, Marcus Pinto , The Web Application Hacker's Handbook: Finding

And Exploiting Security, Wiley Publishing, Second Edition.


3. Carlos Serrao, Vicente Aguilera, Fabio Cerullo, “Web Application Security” Springer;

1st Edition

4. Joel Scambray, Vincent Liu, Caleb Sima ,“Hacking exposed”, McGraw-Hill; 3rd




COURSE OUTCOMES


CO1:Achieve Knowledge of web application‟s vulnerability and malicious attacks.






CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 1 3

CO2 2 1 3

CO3 1 3

CO4 3 1 3

CO5 1 3


CN55

Course Code 18CS2L01 M. Tech (Computer Networking)

Category Laboratory


LAB

Scheme and

Credits


L T P S Credits

0 0 4 0 4

CIE Marks:

50


100




2. Java Programming









implementations are carried out in Java. Exercises should be designed to cover the

following topics:


2. AVL Tree














COURSE OUTCOMES





techniques.

CN56



Continuous Internal

Evaluation (CIE) (Lab – 50

Marks)


(Lab – 100 Marks) Marks


the Lab every week

20 Write up 10

Test at the end of the semester 20 Experiment 70


Total 100





PO1 PO2 PO3

CO1 2 2

CO2 2

CO3 2

CO4 2

CO5 2


CN57


Category Audit Course-2



L T P SS Credits

2 0 - - 1



COURSE OBJECTIVES







UNIT- I INTRODUCTION AND METHODOLOGY: 6 Hours

Aims and rationale, Policy background, Conceptual framework and terminology Theories of

learning, Curriculum, Teacher education. Conceptual framework, Research questions. Overview of

methodology and Searching.

UNIT- II THEMATIC OVERVIEW: 3 Hours

Pedagogical practices are being used by teachers in formal and informal classrooms in developing

countries. Curriculum, Teacher education

UNIT- III PEDAGOGICAL PRACTICES: 6 Hours

Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage: quality

assessment of included studies. How can teacher education (curriculum and practicum) and the

school curriculum and guidance materials best support effective pedagogy? Theory of change.

Strength and nature of the body of evidence for effective pedagogical practices. Pedagogic theory

and pedagogical approaches. Teachers‟ attitudes and beliefs and Pedagogic strategies.


Professional development: alignment with classroom practices and follow-up support Peer support

Support from the head teacher and the community. Curriculum and assessment Barriers to learning:

limited resources and large class sizes


Research design Contexts Pedagogy Teacher education Curriculum and assessment Dissemination

and research impact.


REFERENCES

1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools, Compare, 31

(2): 245-261.

2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of

Curriculum Studies, 36 (3): 361-379.



CN58

4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of

basic maths and reading in Africa: Does teacher preparation count? International Journal

Educational Development, 33 (3): 272–282.

5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education.

Oxford and Boston: Blackwell.

6. Chavan M (2003) Read India: A mass scale, rapid, „learning to read‟ campaign


COURSE OUTCOMES




CO2: What is the evidence on the effectiveness of these pedagogical practices, in what

conditions, and with what population of learners?

CO3: How can teacher education (curriculum and practicum) and the school curriculum and

guidance materials best support effective pedagogy




CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


CN59




same effectively.







GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50





Scheme and Credits

No. of Hours/Week


0 0 2 0 1



CN60


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2










Report : 25%

CN60

Semester III

CN61



Course title MACHINE LEARNING

Scheme and Credits No. of Hours/Week Semester – III

L T P S Credits

4 0 - - 4



Data Mining (Preferrable)


1. Understand the concept of how to extract patterns.

2. Design and analyse various machine learning algorithms and techniques with a modern

outlook, focusing on recent advances.

3. Develop supervised and unsupervised learning paradigms of machine learning.

4. Assess Deep learning techniques and various feature extraction strategies.

5. Evaluate the machine learning algorithms.

UNIT I - SUPERVISED LEARNING (REGRESSION/CLASSIFICATION) 09 Hours

Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes

Linear models: Linear Regression, Logistic Regression, Generalized Linear Models, Support

Vector Machines, Nonlinearity and Kernel Methods, Beyond Binary Classification: Multi-Class

/ Structured Outputs, Ranking

UNIT II - UNSUPERVISED LEARNING 10 Hours

Clustering: K-means / Kernel K-means, Dimensionality Reduction: PCA and kernel PCA,

Matrix Factorization and Matrix Completion, Generative Models (mixture models and latent

factor models)

UNIT III - MACHINE LEARNING ALGORITHMS 09 Hours

Evaluating Machine Learning algorithms and Model Selection, Introduction to Statistical

Learning Theory, Ensemble Methods (Boosting, Bagging, Random Forests)

UNIT IV 10 Hours

Sparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep

Learning and Feature Representation Learning

UNIT V 10 Hours

Scalable Machine Learning (Online and Distributed Learning) A selection from other advanced

topics, e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning, Inference in

Graphical Models, Introduction to Bayesian Learning and Inference

UNIT VI

Recent trends in various learning techniques of machine learning and classification methods for

IOT applications. Various models for IOT applications

REFERENCES

1. Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012

2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning,

Springer 2009

3. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007

COURSE OUTCOMES


CO1. Extract features that can be used for a particular machine learning approach in

various IOT applications.

CO2. Compare and contrast pros and cons of various machine learning techniques.

CN62

CO3. Get an insight of when to apply a particular machine learning approach.

CO4. Mathematically analyse various machine learning approaches and paradigms.

CO5. Design and formulate Supervised and Unsupervised learning paradigms of machine

learning


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




marks.


PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


CN63




Scheme and

Credits


L T P S Credits

3 - 2 - 4

CIE Marks:

50


100










analytics.


data, web analytics, big data and marketing, fraud and big data, risk and big data, credit risk

management, big data and algorithmic trading, big data and healthcare, big data in medicine,

advertising and big data, big data technologies, introduction to Hadoop, open source

technologies, cloud and big data, mobile business intelligence, Crowd sourcing analytics,

inter and trans firewall analytics.








design of Hadoop distributed file system (HDFS), HDFS concepts, Java interface, data flow,

Hadoop I/O, data integrity, compression, serialization, Avro, file-based data structures


MapReduce job run, classic Map-reduce, YARN, failures in classic Map-reduce and YARN,

job scheduling, shuffle and sort, task execution, MapReduce types, input formats, output

formats.


Hbase, data model and implementations, Hbase clients, Hbase examples, praxis. Cassandra,

Cassandra data model, Cassandra examples, Cassandra clients, Hadoop integration, Pig,

Grunt, pig data model, Pig Latin, developing and testing Pig Latin scripts. Hive, data types

and file formats, HiveQL data definition, HiveQL data manipulation, HiveQL queries.

UNIT VI -

Recent advances in Big data analytics

CN64

UNIT - VII (Lab Programs)







o Retrieving files

o Deleting files




(b) Performing a MapReduce Job for word search count (look for specific keywords in a

file)


Input:



Output:





good candidate for analysis with MapReduce, since it is semi structured and record-oriented.



(b) Filter the readings of a set based on value of the measurement, Output the line of

input files associated with a temperature value greater than 30.0 and store it in a

separate file.





Toys

Consumer Electronics



Reno

Toledo

Chandler


7. Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter your

data.






Scala.

CN65

REFERENCES

1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:

Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,

2013.


World of Polyglot Persistence", Addison-Wesley Professional, 2012.







COURSE OUTCOMES







analytics.


CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks







CN66


PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


CN67



Course title HIGH PERFORMANCE COMPUTING

Scheme and

Credits


L T P S Credits

4 0 - - 4



1. Computer Architecture


COURSE OBJECTIVES


1. Understand the modern processors, their architectures and several case studies.

2. Understand the need of parallelism and types of parallelism.

3. Analyse shared and distributed based memory parallel programming using OpenMP

and MPI.

4. Evaluate hybrid parallel programming using MPI and OpenMPI.

5. Review of recent trends in efficiency MPI programming and scalable parallel

processing.

UNIT-I MODERN PROCESSORS 10 Hours

Stored-Program Computer Architecture, General-Purpose Cache-Based Microprocessor

Architecture, Memory, Multi-Core Processors, Multithreaded Processors, Vector Processors.

Basic Optimization Techniques For Serial Code: Scalar Profiling, Common Sense

Optimizations, Simple Measures, Large Impact, The Role of Compilers, C++ Optimizations.

Data Access Optimization: Balance Analysis and Light Speed Estimates, Storage Order, Case

Study: The Jacobi Algorithm, Case Study: Dense Matrix Transpose, Algorithm Classification

and Access Optimizations, Case Study: Sparse Matrix-Vector Multiply.

UNIT-II PARALLEL COMPUTERS 09 Hours

Taxonomy of Parallel Computing Paradigms, Shared-Memory Computers, Distributed-

Memory Computers, Hierarchical (Hybrid) Systems, Networks, Basics of Parallelization:

Why Parallelize? Data and Functional Parallelism, Parallel Scalability.

UNIT-III SHARED-MEMORY PARALLEL PROGRAMMING WITH OpenMP

09 Hours

Introduction to OpenMP, Case Study: OpenMP-Parallel Jacobi Algorithm. Efficient OpenMP

programming: Profiling OpenMP Programs Performance Pitfalls, Case Study: Parallel Sparse

Matrix-Vector Multiply.

UNIT-IV DISTRIBUTED-MEMORY PARALLEL PROGRAMMING WITH MPI

10 Hours

Message Passing, Introduction to MPI, Example: MPI Parallelization of a Jacobi Solver.

Efficient MPI Programming: MPI Performance Tools, Communication Parameters,

Synchronization, Serialization, Contention, Reducing Communication Overhead,

Understanding Intra-Node Point-To-Point Communication. – 12 Hours

UNIT-V HYBRID PARALLELIZATION WITH MPI AND OpenMP 10 Hours

Basic MPI/OpenMP Programming Models, MPI Taxonomy of Thread Interoperability,

Hybrid Decomposition and Mapping, Potential Benefits and Drawbacks of Hybrid

Programming.

UNIT VI – Recent trends in efficient MPI programming and scalable parallel processing.

CN68

REFERENCES

1. Georg Hager and Gerhard Wellein, “Introduction to High Performance Computing for

Scientists and Engineers”, CRC Press, 2011.

2. Victor Eijkhout with Edmond Chow, Robert van de Geijn, “Introduction to High

Performance Scientific Computing”. II Edition, 2015.

3. Charles Severance Kevin Dowd, “High Performance Computing”, Oreilly Media, II

Edition, 1998

COURSE OUTCOMES


CO1: Discuss various modern processes, along with their architectures.

CO2: Categorize and compare different types of parallelism.

CO3: Asses shared and distributed based memory parallel programming using OpenMPI and

MPI.

CO4: Investigate hybrid parallel programming using MPI and OpenMP

CO5: Design an efficient Hpc system using MPI and OpenMP programming.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




marks.


PO1 PO2 PO3

CO1 2

CO2 1 1

CO3 1 2

CO4 1

CO5 1 1 1


CN69

Course Code 18CS3P1A M. Tech (Computer Networking)

Category Open Elective


Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES







UNIT-I INTRODUCTION: 9 Hours

Introduction Definition Future of Artificial Intelligence Characteristics and Problem Solving

Approach to Typical AI problems. State Space Search and Heuristic Search Techniques

Defining problems as State Space search, Production systems and characteristics, Hill

Climbing, Breadth first and depth first search, Best first search.

UNIT-II KNOWLEDGE REPRESENTATION ISSUES: 9 Hours

Representations and Mappings, Approaches to knowledge representation, Using Predicate







UNIT-IV REASONING I: 10 Hours

Symbolic Logic under Uncertainty , Non-monotonic Reasoning, Logics for non-monotonic






units

REFERENCES:








CN70


Press, 2009.


COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*2=40

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 2

CO4 2

CO5 2 2


CN71

Course Code 18CS3P1B M. Tech (Computer Networking)



Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES





organization.

2. Gain an understanding of how managers use business analytics to formulate and

solve business problems and to support managerial decision making.


UNIT -I BUSINESS ANALYTICS: 10 Hours

Overview of Business analytics, Scope of Business analytics, Business Analytics Process,


Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical methods,

Review of probability distribution and data modelling, sampling and estimation methods

overview

UNIT -II TRENDINESS AND REGRESSION ANALYSIS: 9 Hours


Resources, Business Analytics Personnel, Data and models for Business analytics, problem

solving, Visualizing and Exploring Data, Business Analytics Technology

UNIT -III ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS:

10 Hours

Team management, Management Issues, Designing Information Policy, Outsourcing,

Ensuring Data Quality, Measuring contribution of Business analytics, Managing Changes.

Descriptive Analytics, predictive analytics, predicative Modelling, Predictive analytics

analysis, Data Mining, Data Mining Methodologies, Prescriptive analytics and its step in

the business analytics Process, Prescriptive Modelling, nonlinear Optimization

UNIT -IV FORECASTING TECHNIQUES: 10 Hours






Development Model, Newsvendor Model, Overbooking Model, Cash Budget Model

UNIT- V DECISION ANALYSIS: 9 Hours

Formulating Decision Problems, Decision Strategies with the without Outcome

CN72



units

REFERENCES


Dara G. Schniederjans, Christopher M. Starkey, Pearson FT Press

2. Business Analytics by James Evans, persons Education

COURSE OUTCOMES










CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*2=40

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

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CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


CN73

Course Code 18CS3P1C M. Tech (Computer Networking)


Course title SYSTEM SIMULATION AND MODELING

Scheme and

Credits


L T P S Credits

3 1 - - 4



COURSE OBJECTIVES:



techniques






























CN74

REFERENCES

1. Jerry Banks and John Carson, “Discrete Event System Simulation”, Fourth Edition, PHI,

2005.

2. Geoffrey Gordon, “System Simulation”, Second Edition, PHI, 2006.

3. Frank L. Severance, “System Modelling and Simulation”, Wiley, 2001.

4. Averill M. Law and W. David Kelton, “Simulation Modelling and Analysis, Third


5. Jerry Banks, “Handbook of Simulation: Principles, Methodology, Advances,

Applications and Practice”, Wiley-Inter science, 1 edition, 1998.

COURSE OUTCOMES










CIE –

50

marks


= 10 marks

Total:50


SEE

– 100

marks







marks.


PO1 PO2 PO3

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CO2 3

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CO4 3

CO5 3 2


CN75




same effectively.







GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50





Scheme and Credits

No. of Hours/Week


0 0 2 0 1



CN76


PO1 PO2 PO3

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CO2 2 3 3

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Report : 25%

CN77

INTERNSHIP



1. Provide an opportunity to see how classroom and textbook learning applies to the real

world, and to expose the students to the relevant work experience.

2. Pay close attention to all the steps that go onto completing a job, thereby, help students

to become workforce ready before entering the job market as a graduate. Provide an

opportunity to select the topic of dissertation work by evaluating the requirement of

organisation.


GUIDELINES

1. Student has to approach the concerned heads of various Industries/organization, which

are related to the field of specialization of the M. Tech program.

2. If any student gets internship, he/she has to submit the internship offer letter duly signed

by the concerned authority of the company to the Chairperson of the Department.

3. The internship on full time basis will be after the examination of II semester and during

III semester for a period of 8 weeks without affects regular class work.

4. The progress has to be reported periodically to the faculty or to the Guide assigned by

the Chairperson as per the format acceptable to the respective industry /organizations

and to the Institution.


6. Students are advised to use ICT tools such as Skype to report their progress and

submission of periodic progress reports to the faculty in charge or guide.

7. Duly signed report from internal supervisor (faculty incharge or guide) and external

supervisor from the organization where internship is offered has to be submitted to the

Chairperson of the Department for his/her signature and further processing for

evaluation.

The broad format of the internship final report shall contain Cover Page, Certificate from

College, Certificate from Industry / Organization of internship, Acknowledgement,

Synopsis, Table of Contents, chapters of Profile of the Organization - Organizational

structure, Products, Services, Business Partners, Financials, Manpower, Societal Concerns,

Professional Practices, Activities of the Department where internship is done, Tasks

Performed and summary of the tasks performed. specific technical and soft skills that

student has acquired during internship, References & Annexure.

Course Code 18CN3I01 M. Tech (Computer Networking)

Category Internship / Mini Project Semester: III


Scheme and Credits

No. of Hours/Week


--- --- 10 --- 5


batch of 6 students


CN78

COURSE OUTCOMES:



problems what

engineers ready do.

2. Get equipped with experience required before entering the job market. Explore the

possibility of formulating the dissertation problem.



CIE 1.Marks awarded by guide (Internal examiner) = 50 marks


50*

Marks

SEE Presentation of internship in the presence of Guide (Internal examiner) and external


50**

Marks

Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50

marks.

**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50

marks.


PO1 PO2 PO3

CO1 3 2

CO2 3 2

CO3 3


Rubrics for CIE:




4. Document = 40%


Rubrics for SEE:



CN79


4. Document = 40%


MINI PROJECT









available, conducting experiments on various engineering subjects, material characterisation,

studying a software tool for solution to an engineering problem, etc.

The mini project must be carried out preferably using the resources available in the

department/college and it can be of interdisciplinary also.

COURSE OUTCOMES:







CIE

1.Marks awarded by guide (Internal examiner) = 50 marks

2.Marks awarded by the department internship/mini project monitoring committee

= 50 marks

50*

Marks



50**

Marks


marks.


marks.

CN80

Rubrics for CIE


Rubrics for SEE:

The SEE shall be done by two examiners out of which one examiner is the guide of mini

project. The following weightage would be given for the examination. Evaluation shall be done

in batches, not exceeding 6 students.

Sl.

no

Particulars Weightage Marks Total

marks of

SEE


50**



Discussion

35% 30

4 Report 25% 25

5 Viva Voce 20% 20

Total 100% 100

Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for

50 marks.


PO1 PO2 PO3

CO1 3 3

CO2 2 3

CO3 3


Sl.

no


marks of

CIE


50*



25% 25




Total 100% 100

CN81


1. Choose a problem applying relevant knowledge and skills acquired during the course. Formulate the

specifications of the project work, identify the set of feasible solutions, prepare, and execute project plan

considering professional, cultural and societal factors. Identify the problem-solving methodology using

literature survey and present the same.

2. Develop experimental planning and select appropriate techniques and tools to conduct experiments to

Evaluate and critically examine the outcomes followed by concluding the results and identifying

relevant applications. Preparation of synopsis, preliminary report for approval of topic selected along

with literature survey, objectives and methodology.


GUIDELINES

The Dissertation work will start in III semester and should be a problem with research potential and

should involve scientific research, design, generation/collection and analysis of data, determining

solution and must preferably bring out the individual contribution.

The Dissertation work will have to be done by only one student and the topic of dissertation must be

decided by the guide and the student. The dissertation work shall be carried out, on-campus or in an

industry or in an organisation with prior approval from the Chairperson of the Department. The student

has to be in regular contact with the guide atleast once in a week.


College/Industry/Organisation, Acknowledgement, List of Figures and Tables Contents, Nomenclature,

Chapters of Introduction including motivation to choose topic, Literature survey, Conclusion of

literature survey, Objectives and Scope of Dissertation, Methodology to be followed, Experimental

requirements, References and Annexure.

The preliminary results (if available) of the problem of Dissertation work may also be discussed in

the report.

COURSE OUTCOME:



National/International reference journals, articles and contact resource persons for selected topics of

Dissertation.


3. Present and defend the contents of Dissertation work phase I in front of technically qualified audience

effectively.

Course Code 18CN3D01 M. Tech (Computer Networking)



Scheme and Credits

No. of Hours/Week


0 0 10 0 5



CN82




50*

Marks


examiner) and external examiner

50**

Marks









Rubrics for SEE:







PO1 PO2 PO3

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CO2 3 3

CO3 3 3


CN82

Semester IV

CN83




effectively.

2. Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic and

prepare content rich presentation.




GUIDE LINES


2. Topic may be of general/ specific interest to program of engineering or electives not offered in the

semester and to be selected in consultation with the faculty/Guide.



COURSE OUTCOMES:




3. Make an effective presentation with seamless flow of content within the time allocated. Overcome




CIE – 50

marks


Marks =20

Total:50





Scheme and Credits

No. of Hours/Week


0 0 2 0 1



CN84


Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall comprise

of Chairman of the Department, Faculty/Guide and one more faculty member nominated by Chairman. The

evaluation criteria shall be as per the rubrics given below:





Report : 25%


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2


CN85


1. Apply/Use different experimental techniques, equipments, software/ Computational/

Analytical /Modelling and Simulation tools required for conducting tests and generate other

relevant data. Students will also be able to design and develop an experimental setup/test rig.


3. Create a detailed technical document as per format based on the outcome of dissertation

work phase I and II.

GUIDELINES

Dissertation work phase II is the continuation of project work started in III semester. The

report of Dissertation work that includes the details of Dissertation work phase I and

phase II should be presented in a standard format. The candidate shall prepare a detailed

report of dissertation that includes Cover Paper, Certificate from

College/Industry/Organisation, Acknowledgement, Abstract, Table of contents, List of

Figures and Table, Nomenclature, Chapter of Introduction, Literature survey, Conclusion

of literature survey, Objectives and Scope of dissertation work, Methodology,

Experimentation, Results, Discussion, Conclusion, Scope for future work, References,

Annexure and full text of the publication (submitted or published)

COURSE OUTCOMES:



Analytical/Modelling and simulation tools individually and generate data for validation

of hypothesis.

2. Investigate and assess the results obtained within the scope of experiments conducted

followed by conclusions.

3. Prepare a detailed technical document, Present and defend the contents of Dissertation

work in presence of technically qualified audience effectively.

Course Code 18CN4D01 M. Tech (Computer Networking)



Scheme and Credits

No. of Hours/Week


--- --- 30 --- 15



CN86


CIE




100

marks

50*

marks

SEE


2. Dissertation evaluation by external examiner = 100 marks



300

marks

50**

marks

Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50

marks.

** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for

50 marks.

Rubrics for CIE:

1. Presentation of background of dissertation work = 10%

2. Literature survey, Problem formulation and Objectives = 30%

3. Presentation of methodology and experimentation = 30%



Rubrics for SEE:







7. Acceptance/Publication of technical paper in Journals/Conference = 20%


PO1 PO2 PO3

CO1 3 2 3

CO2 2 2 3

CO3 3 3 3








Specialization: Bioinformatics

BI-1


VISION

“To strive for excellence in education for the realization of a vibrant and inclusive

society through knowledge creation and dissemination”

MISSION

· Impart quality education to meet national and global challenges

· Blend theoretical knowledge with practical skills

· Pursue academic excellence through high quality research and publications

· Provide access to all sections of society to pursue higher education

· Inculcate right values among students while encouraging competitiveness to


· Produce socially sensitive citizens

· Hasten the process of creating a knowledge society

· To contribute to nation building

BI-2














Programmes.

VISION



society.

MISSION





BI-3


Department of Computer Science and Engineering, UVCE, Bengaluru M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme - 2K18

Specialization: Bioinformatics






CSEM1. Impart quality education and promote scientific temper

CSEM2. Blend theoretical knowledge with practical skills.

CSEM3. Inculcate right values in students.

CSEM4. Providing access to all sections of the society to purse higher education.

CSEM5. Pursue academic excellence through quality teaching, research and publishing

CSEM6: Promote leadership qualities among students

CSEM7: Hasten the process of creating a knowledge society

CSEM8: Produce socially sensitive citizens

Program Outcomes (PO)

BIPO1: An ability to independently carry out research /investigation and development work to


BIPO2: An ability to write and present a substantial technical report/document

BIPO3: Students should be able to demonstrate a degree of mastery over the area as per the

specialization of the program. The mastery should be at a level higher than the

requirements in the appropriate bachelor program

BI-4


The post graduates of M.Tech in Bioinformatics will be provided the knowledge and skill to:

Program Educational Objectives:

M. Tech (Bioinformatics)

BIPE01 Be Bioinformatics engineers with a strong fundamental in engineering principles to

design new drugs or find evolutionary patterns that exist among species through

simulation

BIPE02 An ability to define, assess, tailor bioinformatics processes and methodologies for

development of industrial applications and entrepreneurship skills

BIPE03 To be acquainted with various aspects of current research trends, and modern

technology for lifelong learning.

BI-5


SCHEME OF STUDIES AND EXAMINATION FOR 24 MONTHS COURSE FOR THE AWARD OF

M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (Specialization: BIOINFORMATICS) under CBCS Scheme – 2K18



L T P S


2 18 1C02 4 0 0 0 CSE 4 50 50 4

3 18BI1C03 Introduction to Bioinformatics 4 0 0 0 CSE 4 50 50 4

4

18BI1E1A Bio-molecular Structure Interaction and Dynamics 4 0 0 0

CSE 4 50 50 418BI1E1B Genomics and Proteomics 4 0 0 0

18BI1E1C Programming in Bioinformatics 4 0 0 0

5

18BI1E2A Advanced Biochemistry and Immunology 4 0 0 0

CSE4 50 50 4

18BI1E2B Metabolic Engineering 4 0 0 0

18BI1E2C Biostatics and Applications 4 0 0 0

6 18BI1L01 Advanced Bioinformatics Laboratory 0 0 4 0 CSE 4 50 50 2

7 18CS1M01 Research Methodology & IPR. 2 0 0 0 CSE 2 50 50 2

8 18SE1S01 Seminar -I 0 0 2 0 CSE 2 50 -- 1


Total 30 450 350 26

*=SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.

BI-6



L T P S



3 18BI2C03 Structure Bioinformatics 3 0 2 0 CSE 4 50 50 4

4

18BI2E1A Enzyme Kinetics 4 0 0 0CSE

4 50 50 418BI2E1B Next Generation Sequencing 4 0 0 0

18BI2E1C Microarray Bioinformatics 4 0 0 0

5

18BI2E2A Molecular Mechanics and Simulation 4 0 0 0CSE

4 50 50 418BI2E2B System Biology 4 0 0 0

18BI2E2C Python for Bioinformatics

6 18CS2L01 Advanced Data Structures and Algorithms Laboratory 0 0 4 0 CSE 4 50 50 2

7 18BI2S01 Seminar -II 0 0 2 0 CSE 2 50 -- 1


Total 28 400 300 24

Semester III

Sl. No Cours Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits


L T P S

1 18BI3E1A Protein And Insilico Drug Design

18BI3E1B Recombinant DNA Technology 4 0 0 0 CSE 4 50 50 4

18BI3E1C Genetic Engineering and Biotechnology

2

Open Elective 4 0 0 0 CSE 4 50 50 4

3 18BI3S01 Seminar -III 0 0 2 0 CSE 2 50 --- 1

4 18BI3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5

5 18BI3D01 Dissertation Phase -I 0 0 10 0 CSE 10 50 50 5

Total 30 250 200 19

BI-7



L T P S

1 18BI4S01 Seminar -IV 0 0 2 0 CSE 2 50 --- 1

2 18BI4D01 Dissertation Phase -II - - 30 - CSE 30 50 50 15

Total 32 100 50 16

1 18BIMOOC MOOC Course - - - - 03


COURSE TYPE

BI: BIOINFORMATICSCS: COMPUTER SCIENCE AND ENGG C: PROFESSIONAL CORE E: PROFESSIONAL ELECTIVE





Note:

1. In Any curse(Program core or Program Elective), if self-study of 4 hours per week per students is allocated, then teaching scheme of such course will be 3-0-0-4

and the total credits will be 4.

2. *=SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.

3. #= the CIE test of the lab component of integrated course shall be conducted with the external examiners for 50 marks and shall be reduced to 25 marks

BI-8


SCHEME OF STUDIES AND EXAMINATION FOR 24 MONTHS COURSE FOR THE AWARD OF

M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (Specialization: Bioinformatics) under CBCS Scheme – 2K18

Open Elective for M. Tech CBCS Scheme



L T P S

1


4 0 0 0 CSE 4 50 50 418CS3P1B Business Analytics

18CS3P1C Modelling and Simulation

2


4 0 0 0 Civil 4 50 50 418CV3P1B Water Laws, Rights and Administration


18CV3P1D Remote Sensing and Geographic information System

318 3P1A Composite and Smart Materials 4 0 0 0 Mech 4 50 50 4

18 3P1B Industrial Safety

4


4 0 0 0 EEE4 50 50 4

18EE3P1B Robotics and Automation


5


4 0 0 0 ECE 4 50 50 418EC3P1B M-Commerce and Applications


BI-9

Course Code 18CS1C01 M.Tech(Bioinformatics)

Category Theory-Professional Core

Course Title MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE

Scheme and Credits No. of Hours/Week Semester-I

L T P SS Credits

3 1 4

CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours

Prerequisite(if any):



COURSE OBJECTIVES:



2. Apply the concepts of stochastic process and queuing theory required to devise analytical

models for the real problems of computer science.

3. Analyze the various concepts of arranging, selecting and combining objects from a set.

4. Understand the concept of advanced graph theory that can be used to model any network,

physical or conceptual.

UNIT -I MATHEMATICAL LOGIC AND NUMBER THEORY: 10 Hours

The Division algorithm, the Greatest Common Divisor, the Euclidean algorithm. The basic properties of

Congruencies, Binary and decimal representation of integer, linear congruence, Chinese-Reminder

Theorem, Fermat’s Little theorem, The sum and number of Divisors, The mobius inversion formula, The

Greatest integer function (No theorem proofs).


Random Variables, Probability Distribution, Binomial Distribution, Poisson Distribution, Geometric

Distribution, Exponential Distribution, Normal Distribution, Uniform Distribution. Two Dimensional

Random Variables. Introduction to Stochastic Processes, Markov process, Markov chain, one step and

n-step Transition Probability, Chapman Kolmogorov theorem (Statement only), Transition Probability

Matrix, Classification of States of a Markov chain. Introduction to Markovian queuing models, Single

Server Model with Infinite system capacity, Characteristics of the Model (M/M/1) : (∞/FIFO), Single

Server Model with Finite System Capacity, Characteristics of the Model (M/M/1) : (K/FIFO).


Basics of Counting: Permutations, Permutations with Repetitions, Circular Permutations, Restricted

Permutations, Combinations: Restricted Combinations, Generating Functions of Permutations and

Combinations, Binomial and Multinomial Coefficients, Binomial and Multinomial Theorems, The

Principles of Inclusion Exclusion, Pigeonhole Principle and its Application

UNIT -IV RECURRENCE RELATIONS: 09 Hours Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of Generating

Functions, Recurrence Relations, and Formulation as Recurrence Relations, Solving Recurrence

Relations by Substitution and Generating Functions, Method of Characteristic Roots, Solving

Inhomogeneous Recurrence Relations.


Basic Concepts of Graphs, Sub graphs, Matrix Representation of Graphs: Adjacency Matrices, Incidence

Matrices, Isomorphic Graphs, Paths and Circuits, Eulerian and Hamiltonian Graphs, Multi-graphs, Planar

Graphs, Euler‘s Formula, Graph Colouring and Covering, Chromatic Number, Spanning Trees,

Algorithms for Spanning Trees (Concepts and Problems Only, Theorems without Proofs).

UNIT -VI Recent advances and research being done in the topics mentioned above units

REFERENCES


2. K. S. Trivedi, “Probability and Statistics with Reliability, Queuing for Computer Science

Applications”, John Wiley and Sons, II Edition, 2008.

3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, “Queuing Networks and Markov

Chains”, John Wiley and Sons, II Edition, 2006.

4. Richard A Brualdi, Introductory Combinatorics 5th Edition, Pearson 2009

5. J. A. Bondy and U. S. R. Murty, “Graph Theory and Applications”, Macmillan Press, 1982.

BI-10

COURSE OUTCOMES:








CIE -50

Marks

Test I (Unit I,II, & III)-15 Marks

Test II (Unit IV & V) -15 Marks

Quiz/ AAT = 5 Marks

Unit- VI(AAT) =15 Marks

Total: Marks 50

SEE-100

Marks

Answer Five Full Questions

Unit which have 10 Hours shall have internal

choice

20*3=60

Marks Total : Marks

100 Unit which have 09 Hours shall not have

internal choice

20*2=40

Marks

Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks

Mapping of course Outcomes(COs) to Program Outcomes(POs)

PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3 2

1. LOW, 2. MEDIUM, 3.HIGH

BI-11

Course Code 18CS1C02 M.Tech (Bioinformatics)



Scheme and

Credits

No. of

Hours/Week

Semester – I

L T P SS Credits

4 0 - - 4

CIE Marks:

50


Hrs


COURSE OBJECTIVE












Protocol- HDLC,PPP, Wired LAN’s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,

Wireless LAN’s(802.11), Broadband Wireless(802.16).








Management – Exponential RTO back-off – KARN’s Algorithm – Window









Networks(SDN).

REFERENCES






BI-12






printing 2012.



edition 2010.

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3 2 2

CO4 3 2

CO5 2 2 2


BI-13

Course Code 18BI1C03 M.Tech (Bioinformatics)


Course Title INTRODUCTION TO BIOLOGY AND BIOINFORMATICS


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Learn basic concepts of bioinformatics and the importance of sequence databases such as

Nucleic acid sequence databases

2. Apply the sequence analysis and pair wise sequence alignment methods

3. Analyze Sequence databases :PubMed, BioMed Central, Public Library of Sciences (PloS),

CiteXplore

4. Evaluate significance of biological database in bioinformatics, Cell cycle and Divisions and

Genetics

5. Create and protein sequences and analysis nucleic acid

UNIT I- INTRODUCTION TO BIOLOGY: 10 Hours

Differences in the basic structure and composition of prokaryotic cells and eukaryotic animal and plant

cells; structure and function of eukaryotic (plant and animal) cell organelles. Diversity in the size and

shape of cells depending on functions within different tissues; variations in the number and structure of

organelles depending on the type of cells (e.g., rich smooth endoplasmic reticulum in lipid secreting

cells). Mendelian laws of inheritance, examples of multiple alleles governing one phenotype;

overview of cytogenetics and genetic linkage; brief overview of molecular genetics, overview of

Central Dogma of molecular biology; epistasis, models for dominance, co-dominance and

pseudo-dominance, epigenetics; major human genetic disorders.

UNIT II- BIOINFORMATICS RESOURCES: 09 Hours

Aim and branches of Bioinformatics, Application of Bioinformatics, Role of internet and www in

bioinformatics. Bioinformatics Resources: NCBI, EBI, ExPASy, RCSB, DDBJ: The knowledge of

databases and bioinformatics tools available at these resources, organization of databases: data contents,

purpose and utility. Open access bibliographic resources and literature databases: PubMed, BioMed

Central, Public Library of Sciences (PloS), CiteXplore.

UNIT III- SEQUENCE DATABASES : 10 Hours

Sequence databases: Nucleic acid sequence databases: GenBank, EMBL, DDBJ; Protein sequence

databases: Uniprot-KB: SWISS-PROT, TrEMBL, UniParc; Structure Databases: PDB, NDB, PubChem,

ChemBank. Sequence file formats: Various file formats for bio-molecular sequences: GenBank, FASTA,

GCG, MSF etc. Protein and nucleic acid properties: Proteomics tools at the ExPASy server, GCG utilities

and EMBOSS, Computation of various parameters

UNIT IV- SEQUENCE ANALYSIS: 09 Hours

Sequence Analysis: Basic concepts of sequence similarity, identity and homology, definitions of

homologues, orthologues, paralogues and xenologues Scoring matrices: basic concept of a scoring

matrix, Matrices for nucleic acid and proteins sequences, PAM and BLOSUM series, matrix derivation

methods and principles..

UNIT V- SEQUENCE ALIGNMENT: 10 Hours Sequence alignment: Measurement of sequence similarity; Similarity and homology. Pairwise sequence

alignment: Basic concepts of sequence alignment, Needleman and Wunsch, Smith and Waterman

algorithms for pairwise alignments, gap penalties, use of pairwise alignments for analysis of Nucleic acid

and protein sequences and interpretation of results.

UNIT VI- Recent advances and research being done in the topics mentioned above units

BI-14

REFERENCES BOOKS:

1. Bioinformatics: Sequence and Genome Analysis Mount D., Cold Spring Harbor Laboratory Press,

New York. 2004.

2. Bioinformatics- a Practical Guide to the Analysis of Genes and Proteins by Baxevanis, A.D. and

Francis Ouellellette, B.F., Wiley India Pvt Ltd. 2009.

3. Orengo CA, Jones DT, Thornton, JM (Eds.), “Bioinformatics - Genes, Proteins and Computers”,

Bios Scientific Publishers Ltd., 2003..

COURSE OUTCOMES:


CO1. Understand differences in the basic structure and composition of prokaryotic cells and

eukaryotic animal and plant cells

CO2: Determine protein and nucleic acid properties using sequence database

CO3: Compare sequence alignments methods

CO4: Validate the significance of biological database in bioinformatics

CO5: Development of Solutions that use of pairwise alignments for analysis of Nucleic acid and

protein sequences


CIE -50

Marks



Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks



choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3 2


BI-15

Course Code 18BI1E1A M.Tech(Bioinformatics)

Category Theory-Professional Elective

Course Title BIOMOLECULAR STRUCTURE INTERACTION AND DYNAMICS


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Learn basic concepts of structural features of proteins,

2. Understand molecular modeling and conversion of 2D Structural data into 3D form.

3. Acquire the knowledge of membrane proteins and simulation methods for membranes

UNIT I-BIOMOLECULAR STRUCTURE AND MODELING: 09 Hours Historical Perspective, Introduction to Molecular Modeling, Roots of Molecular modeling in Molecular

mechanics. Introduction to X-Ray crystallography and NMR spectroscopy. Introduction to PDB and 3D

Structure data, Structure of PDB and other 3D Structure record. Protein Structure Hierarchy: Structure

Hierarchy. Helices – Classic α-Helix and π Helices, Left-Handed α-Helix and Collagen Helix. β-Sheets

- Turns and Loops. Supersecondary and Tertiary structure. Complex 3D Networks. Classes in Protein

Architecture – Folds, α-Class, Bundles,Folded leaves, Hairpin arrays. β-Class folds, Anti-parallel β

domains, parallel and Antiparallel Combinations. α/β and α+β-Class, α/β Barrels, Open twisted α/β folds,

Leucine-rich α/β folds.α+β folds. Quaternary structure. Discussions with case studies.

UNIT II- FORCE FIELDS: 09 Hours Formulation of the Model and Energy, Quantifying Characteristic Motions, Complex Biomolecular

Spectra, Spectra as force constant sources, In-Plane and Out-of-Plane Bending. Bond Length Potentials

- Harmonic term, Morse term, Cubic and Quadratic terms. Bond Angle Potentials - Harmonic and

Trigonometric terms, Cross bond stretch / Angle bend terms. Torsional potentials - Origin of rotational

barriers, Fourier terms, Torsional parameter Assignment, Improper torsion, Cross dihedral/Bond angle,

Dihedral terms. Van der Waals potentials. Rapidly decaying potential. Parameter fitting from experiment.

Two parameter calculation protocols. Coulomb potential - Coulomb’s Law. Slowly decaying potential,

Dielectric function and Partial charges. Discussions with case studies.

UNIT III- MOLECULAR MODELING: 10 Hours Modelling basics, Generation of 3D Coordinates Crystal data, Fragment libraries, and conversion of 2D

Structural data into 3D form. Force fields, and Geometry optimization. Energy minimizing procedures –

Use of Charges, Solvent effects and Quantum Mechanical methods. Computational tools for Molecular

modeling. Methods of Conformational analysis – Systematic search procedures, Monte Carlo and

molecular dynamics methods. Determining features of proteins – Interaction potential, Molecular

electrostatic potential, molecular interaction fields, Properties on molecular surface and Pharmacophore

identification.

UNIT IV- 3D QSAR METHODS: 10 Hours

Comparative protein modeling – Conformational properties of protein structure, Types of secondary

structural elements, Homologous proteins. Procedures for sequence Alignments, Determination and

generation of structurally conserved regions, Construction ofstructurally variable regions, Side-Chain

modeling, Secondary structure prediction, Threading methods. Optimization and Validation of Protein

Models with suitable case studies. Computation of the Free Energy: Free energy calculations in

Biological Systems – Drug design, Signal transduction, Peptide folding, Membrane protein association,

Numerical methods for calculating the potential of mean force, Replica-Exchange-Based Free-Energy

Methods

UNIT V- MEMBRANE PROTEIN SIMULATIONS: 10 Marks

Membrane proteins and their importance, Membrane protein environments in vivo and in vitro. Modeling

a complex environment – Simulation methods for membranes, Membrane protein systems, Complex

solvents, Detergent micelles, Lipid bilayers, SelfAssembly and Complex systems. Modeling and

Simulation of Allosteric regulation in enzymes – Discussions with case studies. Electrostatics and

Enhanced Solvation Models: Implicit solvent electrostatics in Biomolecular Simulation, New distributed

BI-17

multipole methods. Quantum mechanical principles and applications to force field development with case

studies.


REFERENCES

1. Molecular Modeling by Hans-Dieter Höltje, Wolfgang Sippl, Didier Rognan, GerdFolkers,

2008.

2. Modeling of Bimolecular Structures and Mechanisms by Alberte Pullman, Joshua Jortner,

1995.

3. Mathematical Approaches to Biomolecular Structure and Dynamics by Jill P. Mesirov, Klaus

Schulten, De Witt L. Sumners, 1996.

4. Foundations of Molecular Modeling and Simulation by Peter T. Cummings, Phillip R.

Westmorland, Brice Carnahan, Published by American Institute of Chemical Engineers, 2001.

New Algorithms for Macromolecular Simulation by Timothy J. Barth, Michael Griebel, David

E.Keyes, Risto M. Nieminen, Dirk Roose, Tamar Schlick, Published by Springer, 2006.

COURSE OUTCOMES:


CO1: Learn about structural features of proteins.

CO2: Gain insights into the various tools used for modelling of small molecules, lipids and proteins.

CO3: Modelling and Simulation of Allosteric regulation in enzyme

CO4: Prove the membrane protein regulation through modeling and simulation

CO5: Investigate different 3D modelling along with uses cases


CIE -50

Marks



Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks



choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks


Mapping of course Outcomes(Cos) to Program Outcomes(Pos)

PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3 2


BI-18

Course Code 18BI1E1B M.Tech(Bioinformatics)


Course Title GENOMICS, PROTEOMICS


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Have a basic understanding of the organization of prokaryotic and eukaryotic genomes,

databases and sequencing techniques

2. Acquire molecular insight into the tools and techniques used in genome analysis.

3. Get an overview of the different protein purification and sequencing techniques

4. Get insight into the techniques involved in the identification and characterization of all the

proteins synthesized in a cell or a tissue.

UNIT I- INTRODUCTION TO GENOMICS: 09 Hours

Genome evolution and organization in prokaryotes and eukaryotes, Genome mapping: Genetic and

physical mapping. Molecular markers and protein markers, Genome sequencing, basics, strategies and

methodology. Comparative and Functional genomics; Model systems- Arabidopsis, Human, Drophila

and E coli. Serial analysis of gene expression (SAGE) and targeting induced local lesions in genome

(TILLING).Biological databases; Primary and secondary for nucleic acid and proteins, structural

databases, metabolic pathways and specialized databases. Genome Wide Association Studies (GWAS)

UNIT II- TOOLS FOR GENOMICS: 10 Hours

Computational analysis of sequences- finding genes and regulatory regions; Gene annotation; Similarity

searches; Pairwise and multiple alignments; Alignment statistics; Prediction of gene function using

homology, context, structures. Expression sequence tags (ESTs),

Microarrays technology- Principles and applications, FISH, transcriptome analysis and SNPs

determination. Allele mining and single nucteotide polymorphisms (SNPs).Transcriptomics; Cancer

Genomics, Epigenomics, Chemical Genomics; Metabolomics, Nutrigenomics, interactomics,

Metagenomics. Personal Genomics; Social, Legal and Ethical Implications of Human Genome Research.

UNIT III: INTRODUCTION AND SCOPE OF PROTEOMICS 9 Hours Introduction and scope of proteomics, Protein separation techniques: Ion exchange, Size exclusion and

affinity chromatographic techniques, Poly acrylamide gel electrophoresis, isoelectric focusing, two

dimensional poly acrylamide gel electrophoresis, Mass spectrometry based techniques for protein

identification.

UNIT IV- PROTEIN SEQUENCING : 10 Hours

Edman degradation, mass fingerprinting, protein synthesis and post translational modifications.

Identification of phosphorylated proteins, characterization of multi-protein complexes, protein – protein

interactions and quantitative proteomics- Characterization of interaction clusters using two-hybrid

systems. Protein arrays-definition, applications- diagnostics, expression profiling, Functional

proteomics, Protein structure analysis, Protein databases, Clinical and biomedical applications of

proteomics..

UNIT V-MICROARRAY TECHNIQUES: 10 Hours

Importance and applications of microarray techniques in biotechnology, Types – Single and multiple

approaches. Challenges of microarray technology. Microarray Probe preparation, hybridization, Image

processing, Transformation of expression ratio and data normalization. Low and high level information

Analysis – Data Preprocess of Chemical compounds Microarray, Biomolecular microarray – Protein and

proteomics Microarray, DNA Microarray, MicroRNA Population, Cellular and tissue microarray.

Microarray Database for Serial Analysis of Gene Expression, Gene Expression Omnibus of NCBI, Array

Express of EMBL and Antibodies Arrays on Miniature Western Blots methods.


BI-19

REFERENCES

1. Sandor Suhai, Genomics and Proteomics: Functional and Computational aspects, Kluwer academic

publishers, 2007, ISBN: 9780306468230.

2. Liebler,D.C. Introduction to Proteomics: Tools for the New Biology, Humana Press,

2002. ISBN-13: 978-0896039926.

3. R.M. Twyman, Principles of Proteomics, garland Science/BIOS Scientific publishers, 2004, ISBN-

10: 1-85996-273-4.

4. Steven Russell, Lisa A. Meadows and Roslin R. Russell. Microarray Technology in Practice: 2nd

Edition, Academic Press, 2013. ISBN 13: 978-0-12-372516-5.

COURSE OUTCOMES:


CO1: Explain the construction concepts of various genome maps and large scale sequencing

CO2: Apply diagnostic tools for plant, animal and human diseases

CO3: Analyse proteomics to solve complex biological problems regardless of types of organism.

CO4: Develop the basic concepts of microarrays and analyse the differential gene expression.

CO5: Sketch an execution flow of micro-array experiment to determine gene expression


CIE -50

Marks



Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks



choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 3

CO2 3

CO3 3 -

CO4 3

CO5 3 3


BI-20

Course Code 18BI1E1C M.Tech(Bioinformatics)


Course Title PROGRAMMING IN BIOINFORMATICS


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Learn about various algorithms that are used in developing software.

2. Learning various software used in modern biology

3. Mathematical operations using R Language

UNIT I- INTRODUCTION TO BIOPERL AND BIOPERL OBJECTS: 09 Hours

Basics of Perl. Introduction to BioPerl and BioPerl Objects – Brief descriptions (Seq, PrimarySeq,

LocatableSeq, RelSegment, LiveSeq, LargeSeq, RichSeq, SeqWithQuality, SeqI), Location objects,

Interface objects and Implementation objects. Sequence Representation: Representing large sequences

(LargeSeq), Representing changing sequences (LiveSeq).

UNIT II- ACCESSING SEQUENCE DATA – USING BIOPERL: 09 Hours

Accessing sequence data from local and remote databases, Accessing remote databases

(Bio::DB::GenBank, etc), Indexing and accessing local databases (Bio::Index::*,bp_index.pl,

bp_fetch.pl, Bio::DB::*). Sequence and Alignment format Interconversion – Transforming sequence files

(SeqIO), Transforming alignment files (AlignIO). Performing Sequence analysis – Global alignment,

Local alignment, Multiple sequence alignment, Parsing BLAST alignment report and Parsing multiple

sequence alignment.

UNIT III- EXCEPTION HANDLING BIOPYTHON BIOINFORMATICS: 10 Hours Parsing DNA data files, Image manipulation, Sequence analysis – Sequence alignment (pair wise and

multiple sequence alignment), Dynamic Programming, Detecting tandem repeats and generating Hidden

Marko Models, Simulation of EST Clustering. Data mining – Text mining, Simulating Genetic algorithm.

Analysis of Microarray data – Spot finding and Measurement.

UNIT IV- OVERVIEW OF THE R LANGUAGE: 10 Hours

Defining the R project, Obtaining R, Generating R codes, Scripts, Text editors for R, Graphical User

Interfaces (GUIs) for R, Packages. R Objects and data structures: Variable classes, Vectors and matrices,

Data frames and lists, Data sets included in R packages, Summarizing and exploring data, Reading data

from external files, Storing data to external files, Creating and storing R workspaces.

UNIT V- MANIPULATING OBJECTS IN R: 10 Hours Mathematical operations (recycling rules, propagation of names, dimensional attributes, NA handling),

Basic matrix computation (element-wise multiplication, matrix multiplication, outer product, transpose,

eigenvalues, eigenvectors), Textual operations, Basic graphics (high-level plotting, lowlevel plotting,

interacting with graphics.

UNIT VI – Recent advances and research being done in the topics mentioned above units

REFERENCES :

1. John Lewis, Peter Joseph DePasquale, Joseph Chase, Joe Chase, Java Foundations Addison-

Wesley, 2010.

2. D. Curtis Jamison, Perl Programming for Biologists Wiley-IEEE, 2003.

3. Mitchell L Model, Bioinformatics Programming Using Python O’Reilly Media, Inc., 2009.

4. Alain F. Zuur, Elena N. Ieno, and Erik Meesters. A Beginner’s Guide to R. Use R.

Springer,2009.

5. Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon. Bioconductor case studies.

Springer, 2008

6. Robert Gentleman, Bioinformatics with R. Chapman & Hall/CRC, Boca Raton, FL, 2008.

7. Robert Gentleman. R Programming for Bioinformatics. Computer Science & Data Analysis.

Chapman & Hall/CRC, Boca Raton, FL, 2008.

8. Peter Dalgaard. Introductory Statistics with R. Springer, 2nd edition, 2008.

9. Python for Bioinformatics (Chapman & Hall/CRC), Sebastian Bassi, 2009. BI-21

COURSE OUTCOMES:


CO1. Use various algorithms used in software development.

CO2: Apply knowledge about various software’s and their applications

CO3: Draw the graphics and create and store R workspaces

CO4: Relate the libraries available in Perl and R to solve bioinformatics challenges

CO5: Investigate models to retrieve data from biological data sets


CIE -50

Marks



Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks



choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 3

CO2 3

CO3 2

CO4 3

CO5 3 2


BI-22



Course Title ADVANCED BIOCHEMISTRY AND IMMUNOLOGY


L T P SS Credits

4 0 0 0 4



COURSE OBJECTIVES


1. Understand the underlying principles of biochemistry and immunology which form the basis

of biosciences.

2. Understanding Proteins and Carbohydrates.

3. Understanding Lipids, Nucleic acids and Enzymes.

4. Introducing the basics of immune system.

5. Understanding antigen-antibody reaction.

UNIT I- PROTEINS AND CARBOHYDRATES: 09 Hours

Proteins: amino acids– physical and chemical properties of amino acids, peptides, Ramachandran plot,

Amino acid biosynthesis, Metabolism – Urea cycle. Sugars and polysaccharides: Monosaccharides,

polysaccharides and glycoprotein, Metabolism of carbohydrates – Glycolysis-TCA cycle –

gluconeogenesis- glycogen metabolism.

UNIT II- LIPIDS, NUCLEIC ACIDS AND ENZYMES: 09 Hours Lipids: Lipid classification, properties of lipid aggregates, Biological membrane, Lipid linked proteins

and lipoproteins, Biosynthesis – fatty acids, triglycerides, Cholesterol. Metabolism – oxidation of fatty

acid, ATP synthesis. Nucleic acids: Structure of DNA, Forms of DNA - A, B, Z Structures, classification

of RNA.

UNIT III- ENZYMES: 10 Hours Enzymes: Nomenclature, classification, substrate specificity, coenzymes, regulation of enzyme activity.

Rate of enzyme reaction, kinetics, inhibition, effect of pH and temperature.

UNIT IV- IMMUNE SYSTEM: 10 Hours Innate vs. Acquired, humoral and cell mediated immunity, Immunity at Body Surfaces. Cells of the

immune system, Organs of the immune system – primary and secondary lymphoid organ, Antibody

structure and isotypes, Antigens.

UNIT V- IMMUNE RESPONSE: 10 Hours Major histocompatibility complex, HLA typing, Antigen processing and presentation Pathways.

Lymphokines and Cytokines: The complement system, Cell-mediated effectors responses (CTL, NK,

DH). Vaccines. Autoimmunity: Breakdown in Self-Tolerance. Transplantation: tissue and organ grafting.


REFERENCES

1. VoetD. and J.G. Voet, “Biochemistry”, Wiley Publications, Second Edition, 2005.

2. D.L Nelson and M.M Cox, “Lehninger’s Principles of Biochemistry”, W.H FreemanPublications,

5thedition, 2008.

3. Thomas Devlin,“Textbook of Biochemistry with Clinical Correlations”, 7th edition,John Wiley

&Sons,2010.

4. Roitt, “Essential Immunology”, 10 thedition. Blackwell Science, 2005.

5. Richard A. Goldsby, Thomas J. Kindt and Barbara A. Osborne, Kuby“Immunology”,4thedition,

W. H. Freeman & Company, 2000.

6. Janeway et al., “Immunobiology”, 4th edition, Current Biology Publications, 1999.

7. William E. Paul, “Fundamental Immunology”, 4th edition, Lippencott Raven, 1999.

COURSE OUTCOMES:


CO1: Explore Nucleic acids, Structure of DNA, Forms of DNA - A, B, Z Structures, classification of

RNA.

CO2: Identify Cells of the immune system, Organs of the immune system

BI-23

CO3: Get properties of lipid aggregates, Biological membrane, Lipid linked proteins and lipoproteins

CO4: Determines levels of immunity at organism and cellular level

CO5: Investigate auto immunity process


CIE -50

Marks



Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks



choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 1

CO5 2 3


BI-24



Course Title METABOLIC ENGINEERING


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Metabolome and its study

2. Applications of Metabolomics

3. Comprehensive models cellular reactions

4. Metabolic flux analysis and its applications

UNIT I- METABOLOMICS: 09 Hours

Overview- Background and definitions of Metabolomics- importance of Metabolomics.

UNIT II- TECHNOLOGIES IN METABOLOMICS: 10 Hours

Technologies-Mass spectrometry: principles, definitions, nomenclature, Metabolite isolation and

analysis by Mass Spectrometry, metabolite library, HPLC- capillary electrophoresis coupled with Mass

spectrometry.

UNIT III- APPLICATIONS: 10 Hours Applications of Metabolomics to biology: examples and case studies, Metabolome informatics, data

integration and mining.

UNIT IV- METABOLIC ENGINEERING: 09 Hours Metabolic engineering: introduction, mass balance, black box, metabolic flux analysis, stoichiometry,

Principles of metabolic engineering

UNIT V- FLUX BALANCE ANALYSIS: 10 Hours Flux balance analysis, flux balance methods, group based flux balance, metabolic control analysis:

overview, control coefficients, methods of measuring control. Flux analysis of networks- top down

approach, bottom up approach.


REFERENCES

1. Tomita M., T. Nishioka, “Metabolomics: The Frontier of Systems Biology”, Springer, 2003.

2. Gregory N. Stephanopoulos, “Metabolic Engineering: Principles and

Methodologies”,Academic press, First Edition, 1998.

3. Wolfram Weckwerth, “Metabolomics: Methods and Protocols”, Humana Press, 2007.

4. Sang Yup Lee, E. Terry Papoutsakis, “Metabolic engineering”, CRC Press, 1999

5. William J. Griffiths, “Metabolomics, metabonomics and metabolite profiling”, RoyalSociety of

Chemistry, 2008.

COURSE OUTCOMES:


CO1: Technologies in metabolomics and -Mass spectrometry

CO2: Do flux balance analysis, flux balance methods, group based flux balance analysis

CO3: Take up case studies and Applications of Metabolomics to biology

CO4: Compare and Anlyze mass stoichiometry and HPLC techniques used in metabolomics

CO5: Design and Develop prediction model for flux analysis in biological networks


CIE -50

Marks



Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks



choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks

BI-25



PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3 2


BI-26



Course Title BIOSTATISTICS AND APPLICATIONS


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Know the techniques of numerical methods

2. Learn the basics of Biostatistics

3. Understand the concept of hypothesis

UNIT I- FUNDAMENTALS OF STATISTICS: 09 Hours

Introduction to statistics, Measurement of data scale and Central tendency, Measures of dispersion: range,

percentile, variance and standard deviation. Data handling and statistical variables. Characteristics of

biological data, Elementary theory of statistical errors. Continuous random variables-normal distribution,

discrete random variables-Binomial and poisons distribution. Logarithmic transformations. Application

of statistics to biological problems and their interpretation.

UNIT II- INFERENTIAL STATISTICS: 10 Hours Basics of experimental design, Random block design, stratified design; cohort studies, case-control

studies, and odd ratio. Principles of statistical inference: Parameter estimation, hypothesis testing.

Statistical inference on categorical variables; categorical data and Single- and Double-blind experiments;

Sampling distributions: Bivariate distribution-conditional and marginal distribution-Discrete

distribution-Binomial, Poisson, geometric distribution-Continuous distribution, Normal, simple

problems-properties.

UNIT III- HYPOTHESIS TESTING: 10 Hours Null and alternative hypotheses, decision criteria, critical values, type I and type II errors, Meaning of

statistical significance; Power of a test; One sample hypothesis testing: Normally distributed data: z, t

and chi-square tests; F-tests. Binomial proportion testing. Independent and dependent sample

comparison, Wilcoxon Signed Rank Test, Wilcoxon-Mann-Whitney Test, Kruskal-Wallis test and

Analysis of variance: One-way and Two-way Tables.

UNIT IV- STATISTICAL CURVE FITTING: 09 Hours Correlation-Correlation coefficient, properties-problems on Karl Pearson and Spearman Rank correlation

coefficient, Rank correlation-Regression equations problems-curve fitting by the method of least squares-

fitting curves of the form ax+b,ax2 +bx+c,abx and axb - Bivariate correlation application to biological

problems. Regression: simple linear regression; Least squares method; Multiple linear regression model,

Optimization strategies with case studies.

UNIT V - STATISTICS IN MICROARRAY 10 Hours Genome mapping and bioinformatics: Types of microarray, objectives of the study, experimental designs

for micro array studies, microarray analysis, interpretation, validation and microarray informatics.

Genome mapping, discrete sequence matching, programs for mapping sequences with case studies.

UNIT VI - Recent advances and research being done in the topics mentioned above units

REFERENCES

1. Wayne W. Daniel , Chad L. Cross., “Biostatistics : Basic Concepts and Methodology for the

Health Sciences” Wiley India Pvt Ltd. 10th Edition, 2014. ISBN: 9788126551897.

2. Shalabh Helge Toutenburg., “Statistical Analysis of Designed Experiments”, Wiley Publication,

Third Edition, 2010. ISBN-13: 978-1441911476.

3. Arora P.N and Malhan P.K. “Biostatistics”, Himalaya Publishing House, 2013 ISBN: 81-8318-

691-2.

COURSE OUTCOMES:


CO1: Understanding of statistical methods and numerical methods.

CO2: Apply principles of statistical inference, Parameter estimation, hypothesis testing

BI-27

CO3: Use Correlation-and Rank correlation

CO4: Sketch the role of different curve fitting techniques in correlation analysis

CO5: Formulate the design process for use of microarrays in genome mapping


CIE -50

Marks



Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks



choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3 2


BI-28


For selected application, the student have to demonstrate different phase of software development life

cycle

Continuous Internal

Evaluation(Lab=50)

Marks Semester End Evaluation (SEE) Marks

Performance of the student in the

lab every week

20 Write-Up 20

Test at end of the semester 20 Experiment/Execution 70

Vice-Voce 20 Vice-Voce 10

Total(CIE) 50 Total(SEE) 50*


Course Code 18BIL01 M.Tech(Bioinformatics)

Category Practical

Course Title ADVANCED BIOINFORMATICS LAB


L T P SS Credits

0 0 4 2



COURSE OBJECTIVES:

The course will enable the student to

1. Learn about developing bench skills through lab exercises, oriented towards utilizing various

web based tools for bioinformatics projects

Each student has to work individually on assigned lab exercises. Lab sessions could be scheduled as

one contiguous four-hour session per week. It is recommended that all implementations are carried out

in suitable tools. Exercises should be designed to cover the following topics:

1. Sequence retrieval from nucleic acid and protein databases.

2. Retrieval of information about structure, bioassay chemical compounds (such as Drugs and

naturally occurring compounds).

3. 3 Retrieval of information about physical and chemical properties of chemical compounds (such

as Drugs and naturally occurring compounds).

4. Gene sequence assembly and contig mapping and identification of Gene.

5. Primer and Promoter design for a given sequences

6. Sequence searches using FASTA and BLAST, and Phylogenetic analysis.

7. Prediction of secondary structure for given protein and RNA sequences.

8. Retrieval of protein structure from PDB and its visualization and modification.

9. Prediction of 3D structure of unknown protein sequence.

10. Prediction of protein-protein interactions.

11. EST clustering and EST mapping, and Genome annotation

12. Microarray data analysis- normalization, clustering.

Study of Profiles, Patterns and PSSMs

COURSE OUTCOMES:


CO1. Design and implement retrieval of information about physical and Chemical properties of chemical

compounds.

CO2. Retrieval of protein structure from PDB and its visualization and modification.

CO3. Prediction of protein-protein interactions.

CO4: Design and develop Sequence retrieval from nucleic acid and protein database.

CO5: Investigate the performance of proteins structure predication techniques.

BI-29



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BI-30

22

Course Code 18CS1M01 M.Tech(Bioinformatics)



Scheme and

Credits


L T P SS Credits

2 0 - - 2


Hrs


COURSE OBJECTIVES



problem


3. Analyze Approaches of investigation of solutions for research problem











Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of

research proposal, a presentation and assessment by a review committee


Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological

research, innovation, patenting, development. International Scenario: International cooperation on

Intellectual Property. Procedure for grants of patents, Patenting under PCT.

UNIT- V PATENT RIGHTS: 6 Hours Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases.

Geographical Indications.


Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer

Software etc. Traditional knowledge Case Studies, IPR and IITs.

REFERENCES

1. Stuart Melville and Wayne Goddard, “Research methodology: an introduction for

science & engineering students’”


3. Ranjit Kumar, 2nd Edition, “Research Methodology: A Step by Step Guide for

beginners” Halbert, “Resisting Intellectual Property”, Taylor & Francis Ltd ,2007.






BI-31

23


COURSE OUTCOMES







in particular.







CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4

CO5 3 3


BI-32

24




same effectively.







GUIDE LINES






COURSE OUTCOMES:







Course Code 18BI1S01 M.Tech (Bioinformatics)



Scheme and Credits

No. of Hours/Week


0 0 2 0 1



BI-33

25


CIE – 50

marks


Marks =20

Total:50



PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2










Report : 25%

BI-34

26



Course title TECHNICL PAPER WRITING

Scheme and

Credits


L T P SS Credits

2 0 - - 1

CIE Marks: 50 SEE Marks: -- Total Max. Marks: 50


COURSE OBJECTIVES




section





Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs


Vagueness


Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising, Paraphrasing

and Plagiarism, Sections of a Paper, Abstracts. Introduction




Key skills are needed when writing a Title, key skills are needed when writing an Abstract,


the Literature,






REFERENCES


Books)



Highman’sbook.

BI-35

27



COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=20 Marks Total: Marks

50


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


BI-36

28

SEMISTER-II

BI-37

29

Course Code 18CS2C01 M.Tech(Bioinformatics)



Scheme and

Credits


L T P SS Credits

4 0 - - 4


Hrs


COURSE OBJECTIVE





4. Acquire the knowledge of polynomial, non-polynomial and approximation problems.

5. Understand the recent developments in the area of algorithmic design



Recurrences -The substitution method, recursion-tree method, the master method, Probabilistic

Analysis and Randomized Algorithms.

UNIT- II BASIC DATA STRUCTURES 09 Hours

Stacks and Queues, Vectors, Lists, and Sequences, Trees, Priority Queues and Heaps,

Dictionaries and Hash Tables, Search Trees and Skip lists- Ordered Dictionaries and Binary

Search Trees, AVL Trees, Bounded-Depth Search Trees, Splay Trees, Skip Lists.



subsequence’s. Graph algorithms: Bellman - Ford Algorithm; Single source shortest paths in a

DAG; Johnson’s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson method.

.


Quad trees and K-d trees. String-Matching Algorithms: Naïve string Matching; Rabin - Karp

algorithm; String matching with finite automata; KnuthMorris-Pratt algorithm.


Polynomial time, Polynomial time verification, NP-Completeness and reducibility, NP-Complete

problems. Approximation Algorithms: vertex cover problem, the set – covering problem,

randomization and linear programming, the subset – sum problem.

UNIT- VI Recent advances and research being done in the topics mentioned above units

BI-38

30

REFERENCES

1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,” Introduction to

Algorithms”, Third Edition, Prentice-Hall, 2011.


3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in C++”, 4th Edition, Pearson,

2014.

4. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, ―Data Structures and Algorithms‖,

Pearson Education, Reprint 2006.

5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, “Fundamentals of Data Structures in C”, Silicon

Pr, 2007.

6. Aho, Hopcroft and Ullman, Data structures and algorithms, 1st edition, Pearson Education,

India, 2002, ISBN: 8177588265, 978817758826

COURSE OUTCOMES


CO1: Develop and analyze algorithms for red-black trees, B-trees and Splay trees and for text

processing applications.


problems





CIE -50

Marks

Test I (Unit I,II, & III)-

15 Marks


Marks

Quiz/ AAT = 5 Marks


Total: Marks

50

SEE-100

Marks

Unit which have 10 Hours shall have

internal choice

20*3=60

Marks

Total:

Marks 100 Unit which have 09 Hours shall not have

internal choice

20*2=40

Marks

Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for

50 marks


PO1 PO2 PO3

CO1 2

CO2 2 2

CO3 2 2

CO4 2

CO5 2 2


BI-39

31

Course Code 18CS2C02 M.Tech (Bioinformatics)



Scheme and

Credits


L T P SS Credits

4 0 - - 4


50

Total Max. Marks: 100 Duration of SEE: 3

Hrs


COURSE OBJECTIVES






4. Evaluate the Algorithms for Distributed Scheduling, Examine the Commit Protocols and

review Concurrency Control Algorithms.



Concept of Batch system, Multiprogramming, Time Sharing, Parallel, Distributed and Real-time

System, Process Management: Concept of Process, Synchronization, CPU Scheduling, IPC,

Deadlock: Concept of Deadlock Prevention, Avoidance, Detection and its Recovery. Memory

Management: Contiguous allocation, Paging and Segmentation. Virtual memory: Demand

Paging, Page Replacement Algorithms. Distributed OS- Design Approaches and Issues in DOS.

Message Passing Model and RPC.


Concept of Lamport’s Logical Clock and Vector Clocks, Termination Detection. A simple

solution to distributed mutual exclusion, Non Token based algorithms: Lamport’s algorithm,

Ricart Agarwala’s algorithm, Maekawa’s algorithm, Token based algorithms: Suzuki Kasami’s

broadcast algorithm, Raymond’s tree based algorithm.


Deadlock handling, Strategies in Distributed Systems, Issues in Deadlock detection and

resolution, Control Organization for distributed deadlock detection, Centralized deadlock

detection algorithm: The Ho Ramamoorthy’s algorithm, Distributed deadlock detection

algorithms: A path- pushing algorithm and Edge chasing algorithm, Hierarchical deadlock

detection algorithms: The Menasce- Muntz Algorithm, The Ho Ramamoorthy’s algorithm.

Agreement Protocols: The Byzantine Agreement Problem, Solution to the Byzantine Agreement

Problem- Lamport -Shostak- Pease algorithm, Dolev et al.’s algorithm

UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE 10 Hours

Issues in load distribution, components of a load distributing algorithms, load Distributing

algorithms, performance comparison, selecting suitable load sharing algorithms, Requirements

of load sharing policies. Commit Protocols, Nonblocking Commit Protocols, Voting Protocols,

Dynamic Voting Protocols, The Majority Based Dynamic Voting Protocol, Dynamic Vote

Reassignment Protocols.

BI-40

32


Requirement of Database OS, A Concurrency Control Model of a Database System, The Problem

of concurrency control, Serializability Theory, Concurrency control algorithms, Basic

Synchronization Primitives, Lock Based algorithms, timestamp based algorithms, Optimistic

algorithms, and Concurrency Control algorithms for data replication.


REFERENCES


Mcgraw Hill, 2002.


and Sons, 2018.


Hall India, 2017.




COURSE OUTCOMES












CIE -50

Marks

Test I (Unit I,II, & III)-15

Marks


Marks

Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-

100

Marks


choice

20*3=60

Marks Total:


internal choice

20*2=40

Marks

Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50

marks


PO1 PO2 PO3

CO1 1 -

CO2 1 2

CO3 1 2

CO4 1 3

CO5 3 2 2


BI-41

33

Course Code 18BI2C03 M.Tech(Bioinformatics)


Course Title STRUCTURAL BIOINFORMATICS

Scheme and Credits No. of Hours/Week Semester-II

L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Understand various data format for structural databases

2. Learn importance of structure-function relationship of biomolecules

3. Learn how various interactions played major for biomolecules

4. Knowledge about predicting the structure of biomolecules

5. Understand the essence of structural validation

UNIT I - DATA REPRESENTATION AND DATABASES 09 Hours

PDB, mmCIF and other formats, structure based databases for proteins and nucleic acids. Comparative

features-the CATH domain structure Database, Protein structure evolution and the SCOP Database.

UNIT II - DATA INTEGRITY AND COMPARATIVE FEATURES 09 Hours

Structural Quality Assurance, Structure Comparison and Alignment. Structure and Functional

Assignment-Identifying Structural Domains in Proteins, Inferring Protein Function from Structure.

UNIT III - BIOMOLECULES INTERACTION 10 Hours Electrostatic interactions, Prediction of Protein- protein interactions, Prediction of Protein- nucleic acid

interactions, Docking Methods: Introduction, Docking and scoring, Application in the drug design

UNIT IV - STRUCTURAL MODELING 10 Hours Scoring functions: force fields, surface area based functions, knowledge based potentials, searching

procedures: grid based, stochastic methods, building complete protein structures using homology

modelling, fold recognition, Ab initio methods, Analysis of Folds.

UNIT V - STRUCTURAL VALIDATION AND APPLICATION 10 Hours

Validation: CASP and CAFASP experiments and their findings, Structural bioinformatics in drug design:

Modern drug discovery, Drug target, Lead identification, Lead Optimization.


REFERENCES BOOKS:

1. Philip E. Bourne, HelgeWeissig, “Structural Bioinformatics”, John Wiley & Sons, Inc, 2003.

2. Becker OM., MackKerell AD Jr., Roux B., Watanabe M (Eds.), “Computational Biochemistry and

Biophysics”, Dekker, 2001.

3. Hinchliffe A., “Molecular Modelling for Beginners”, Wiley, 2003.

4. Orengo CA, Jones DT, Thornton, JM (Eds.), “Bioinformatics - Genes, Proteins and Computers”,

Bios Scientific Publishers Ltd., 2003..

COURSE OUTCOMES:


CO1: Identify structure based databases for proteins and nucleic acids.

CO2: Apply biomolecules interaction and structural modelling to design of drugs

CO3: Analyse CASP and CAFASP experiments and their findings

CO4: Evaluate modern drug discovery, drug target, lead identification, lead Optimization

CO5: Build complete protein structures using homology modelling


BI-42

34

CIE -50

Marks



Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks



choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 3

CO2 3

CO3 2

CO4 3

CO5 3 2


BI-43

35



Course Title ENZYME KINETICS


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Enhance skills in the areas of biochemical processes

2. Provide the fundamental background of biological systems,

3. Bio-molecules, micro-organisms, fermentation processes, Bioreactors and kinetics.

UNIT I - ENZYMES AND PROTEINS 09 Hours

Enzymes and Proteins: Detailed structure of proteins and enzymes. Functions. Methods of Production

and purification of Enzymes. Nomenclature and Classification of enzymes. Kinetics and mechanism of

Enzyme action: Michaelis–Menten and Briggs Haldane approach. Derivation.

UNIT II - KINETICS OF ENZYME ACTION: 09 Hours

Reversible Enzyme. Two-substrate. Multi-complexes enzyme kinetics (Derivation of rate equations).

Experimental determination of rate parameters: Batch and continuous flow experiments. Lineweaver–

Burk, Eadie-Hofstee and Hanes-Woolf Plots. Batch Kinetics (Integral and Differential methods).

UNIT III - ENZYME INHIBITION: 10 Hours

Effect of Inhibitors (Competitive, non-competitive, uncompetitive, substrate and product inhibitions),

Temperature and pH on the rates enzyme catalyzed reactions. Determination of kinetic parameters for

various types of inhibitions. Dixon method. Enzyme immobilization: Uses. Methods of enzyme

immobilization.

UNIT IV - FERMENTATION TECHNOLOGY: 10 Hours

Ideal reactors: A review of Batch and Continuous flow reactors for biokinetic measurements.

Microbiological reactors: Operation and maintenance of typical aseptic aerobic fermentation processes.

Formulation of medium: Sources of nutrients. Alternate bioreactor configurations. Introduction to

sterilization of bioprocess equipment.

UNIT V - GROWTH KINETICS OF MICROORGANISMS: 10 Hours

Transient growth kinetics (Different phases of batch cultivation). Quantification of growth kinetics:

Substrate limited growth, Models with growth inhibitors, Logistic equation, Filamentous cell growth

model. Continuous culture: Optimum Dilution rate and washout condition in Ideal Chemostat.

Introduction to Fed-batch reactors.


REFERENCES :

1. Biochemical Engineering Fundamentals, Bailey and Ollis, II Edition, McGraw Hill, 1976.

2. Bioprocess Engineering, Shuler M. L. and Kargi F., 2ndEdition, Prentice Hall, 2002.

3. Biochemical Engineering, James Lee, Prentice Hall, 1992.

4. Biochemical Reactors, Atkinson B, Pion Ltd., London, 1974.

5. Industrial Microbiology, Casida, Wiley, New York, 1968

6. Principles of Fermentation Technology, Stanbury and Whitekar, 2ndEdition, Butterworth-

Heinemann An Imprint of Elsevier

COURSE OUTCOMES:


CO1: Explain structure of proteins and enzymes , nomenclature and classification of enzymes

CO2: Determination of kinetic parameters for various types of inhibitions.

CO3: Differentiate batch and continuous flow reactors

CO4 : Assess substrate limited growth, Models with growth inhibitors, Logistic equation, Filamentous

cell growth model

BI-44

36

CO5: Develop methods of enzyme immobilization.


CIE -50

Marks


Marks


Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks


choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 3 2


BI-45

37



Course Title NEXT GENERATION SEQUENCING TECHNOLOGIES


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Know the application of NGS in various areas of research.

2. Highlight the various techniques (software’s and tools) used for NGS analysis.

3. Provide in-depth understanding of the use of NGS in clinical and medical diagnostic approach.

UNIT I - NEXT GENERATION SEQUENCING 10 Hours

Sanger sequencing principles - history and landmarks, of Sequencing Technology Platforms, A survey

of next-generation sequencing technologies, A review of DNA enrichment technologies, Application of

High-Throughput Sequencing, Application of NGS to the diagnosis of genetic disorders,

Computational Infrastructure and Basic Data Analysis.

UNIT II - INTERACTION ANALYSIS OF CHIP-SEQ 10 Hours

Base-Calling for Bio-informaticians, De Novo Short-read Assembly, Short-Read Mapping, DNA-Protein

Interaction Analysis (CHIP-Sequence), Generation and Analysis of Genome-Wide DNA Methylation

Maps, Differential Expression for RNA Sequencing (RNA-Sequence) Data: Mapping, summarization,

statistical Analysis and Experimental Design.

UNIT III - ANALYSIS OF METAGENOMIC DATA 09 Hours MicroRNA Expression Profiling and Discovery, Dissecting Splicing Regulatory Network by Integrative

Analysis of CLIP-Sequence Data, Analysis of Metagenomic Data, NGS-based noninvasive prenatal

diagnosis, Diagnosis of inherited neuromuscular disorders by NGS Application of NGS in hearing loss

diagnosis.

UNIT IV - EXOME SEQUENCING 10 Hours

Exome sequencing as a discovery and a diagnostic tool, Challenges of NGS based molecular diagnostics,

NGS-Based Clinical Diagnosis of Genetically Heterogeneous Disorders, Molecular Diagnosis of

Congenital Disorders of Glycosylation (CDG), NGS improves the Diagnosis of X-Linked Intellectual

Disability (XLID), NGS Analysis of Heterogeneous Retinitis Pigmentosa.

UNIT V - NGS ANALYSIS OF THE WHOLE MITOCHONDRIAL GENOME NGS 09 Hours

Analysis of the Whole Mitochondrial Genome, Noninvasive Prenatal Diagnosis Using Next-Generation

Sequencing, High-Throughput Sequencing Data Analysis Software: Current state and future

developments.


Reference Books:

1. Valencia, C.A., Pervaiz, M.A., Husami, A., Qian, Y., Zhang, K, “Next Generation Sequencing

Technologies in Medical Genetics”, Springer, 2013.

2. Lee-Jun C. Wong, “Next Generation Sequencing: Translation to Clinical Diagnostics”, Springer,

2013.

3. Naiara Rodríguez-Ezpeleta, Michael Hackenberg, “Bioinformatics for High Throughput

Sequencing”, Springer, 2012.

4. Masoudi-Nejad, Ali, Narimani, Zahra, Hosseinkhan, Nazanin, “Next Generation Sequencing and

Sequence Assembly: Methodologies and Algorithms”, Springer, 2013.

5. Wu, Wei, Choudhry, Hani (Eds.), “Next Generation Sequencing in Cancer Research: Volume 1:

Decoding the Cancer Genome”, Springer, 2013.

BI-46

38

COURSE OUTCOMES:


CO1: Understand Exome sequencing as a discovery and a diagnostic tool

CO2: Discuss MicroRNA expression profiling and discovery

CO3: Analyse application of NGS to the diagnosis of genetic disorders

CO4: Evaluate diagnosis of inherited neuromuscular disorders by NGS Application of NGS in hearing

loss diagnosis.

CO5: Design and develop Sequencing Data Analysis Software


CIE -50

Marks


Marks


Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks


choice

30*2=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 3

CO2 2

CO3 3

CO4 3

CO5 3 2


BI-47

39



Course Title MICROARRAY BIOINFORMATICS


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. DNA Microarray and its statistical analysis.

2. Analysis of RNA data. Statistical computing and Statistical Genetics.

3. Hierarchical Clustering, Self-Organization Maps (SOM), Identifying Genes

UNIT-I DNA MICROARRAY 09 Hours The Technical Foundations, Why are MicroArray Important?, What is a DNA MicroArray?, Designing

a MicroArray Experiment-The Basic steps, Types of MicroArray.

UNIT –II MICROARRAY DATABASES 09 Hours

NCBI and MicroArray Data Management, GEO (Gene Expression Omnibus), MAML, The benefits of

GEO and MAML, The Promise of MicroArray Technology in Treating Disease.

UNIT–III: MICROARRAY DATA NORMALIZATION 10 Hours

Micro-Array Data Pre-processing, Data-Data normalization, Measuring Dissimilarity of Expression

Pattern-Distance Motifs and Dissimilarity measures, Visualizing Micro-Array Data-Principal

Component Analysis, Micro-Array Data.

UNIT –IV MICROARRAY DATA ANALYSIS 10 Hours

KMeans Clustering, Hierarchical Clustering, Self-Organization Maps (SOM), Identifying Genes:

Expressed usually in a sample- Expressed significantly in population-Expressed differently in two

populations, Classifying Samples from two populations using Multilayer Perceptron, Support Vector

Machines and their applications, Using genetic algorithm and Perceptron for feature selection and

supervised classification..

UNIT-V MICROARRAY APPLICATIONS 10 Hours

Gene Ontology and pathway analysis, Promoter analysis and gene regulatory network, Coexpression

analysis, CGH & Genotyping chips, Chromosome aberration and polymorphism via genome-wide

scanning, Future direction of microarray approach, Pharmacogenomics, Toxicogenomics, Data mining.


REFERENCE BOOKS:

1. ArunJogota, “Microarray Data Analysis and Visualization”, the Bay Press, 2001.

2. Ernst Wit and John McClure, “Statistics for Microarrays Design, Analysis and Inference”, John

Wiley & Sons, 2004.

3. Steen Knudsen, “Guide to analysis of DNA Microarray data”, John Wiley & Sons, 2004.

4. DovStekel, “Microarray Bioinformatics”, Cambridge University Press, 2003.

5. Draghic S., Chapman, “Data Analysis tools for DNA Microarray”, Hall/ CRC Press, 2002.

6. Uwe R. Müller, Dan V. Nicolau,“Microarray Technology and Its Applications”, Springer, 2005.

COURSE OUTCOMES:


CO1: K-Means Clustering, Hierarchical Clustering, Self-Organization Maps (SOM), Identifying Genes

CO2: Using genetic algorithm and Perceptron for feature selection and supervised classification.

CO3: Apply Gene Ontology and pathway analysis, Promoter analysis and gene regulatory network

CO4: Evaluate Support Vector Machines and their applications

CO5: Develop CGH & Genotyping chips, Chromosome aberration and polymorphism via genome-

wide scanning


BI-48

40

CIE -50

Marks


Marks


Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks


choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 3

CO2 2

CO3 3

CO4 3

CO5 3 2


BI-49

41



Course Title MOLECULAR MECHANICS AND SIMULATION


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Understand the basic concepts in Molecular Mechanics.

2. Learn empirical force field models.

3. Understand computer simulation techniques and conformational analysis

UNIT-I CONCEPTS IN MOLECULAR MECHANICS 09 Hours Concepts In Molecular Mechanics: Introduction, Coordinate systems, Units of Length and Energy,

Potential Energy surfaces, other surfaces, Molecular Graphics. .

UNIT –II COMPUTATIONAL QUANTUM MECHANICS 09 Hours

Computational Quantum Mechanics: One-electron atoms, Poly electron atoms and molecules, Molecular

orbitals, Hartree-Fock Equations, Molecular Properties using ab initio methods, Semi-empirical methods,

Huckel Theory..

UNIT –III EMPIRICAL FORCE FIELD METHODS 10 Hours

Empirical Force Field Methods: Bond Stretching, Angle Bending, Torsional Terms, Non bonded and

ectrostatic interactions, Van der Waals Interaction, Hydrogen bonding parameterization, United atom

force field representation, Force field parameterization.

UNIT –IV COMPUTER SIMULATION METHODS 10 Hours

Computer Simulation Methods: Simple Thermodynamic properties, Phase space, Practical aspects of

Computer simulation, Boundaries, Truncating the potential, Minimum Image convention, Long range

forces. Conformational Analysis: Systematic methods for exploring conformational space, Random

search methods, Evolutionary algorithms, Simulated Annealing, Restrained molecular methods,

Molecular fitting, Clustering algorithm, Reducing dimensionality of data set, Pooling.

UNIT-V MONTE CARLO SIMULATIONS 10 Hours

Monte Carlo Simulations: Calculating properties by integration, metropolis methods- metropolis Monte

Carlo methods- simulations of molecules- models- biased methods- different ensembles calculating

chemical potentials- Gibbs ensemble methods.


References:

1. Andrew R. Leach, “Molecular Modeling: Principles and applications”, Prentice Hall, 2ndedition,

1996.

2. Alan Hinchliffe, “Modelling Molecular Structures”, John Wiley, 2000.

3. Ramachandran K. I., G. Deepa, K.Namboori,“Computational Chemistry and Molecular Modeling:

Principles and Applications”, Springer, 2008.

4. Charles R. Cantor, Paul ReinhardSchimmel, “Biophysical Chemistry: The Behavior of Biological

Macromolecules PART III”, W. H. Freeman, 1980.

COURSE OUTCOMES:


CO1: Explain Monte Carlo Simulations for application

CO2: Apply Computational Quantum Mechanics for analysis

CO3: Verify systematic methods for exploring conformational space, Random search methods

CO4: Assess different ensembles calculating chemical potentials- Gibbs ensemble methods.

CO5: Investigate exploring conformational space, Random search methods, Evolutionary algorithms

BI-50

42


CIE -50

Marks


Marks


Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks


choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 2

CO2 3

CO3 3

CO4 3

CO5 3 2


BI-51

43



Course Title SYSTEM BIOLOGY


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Learning the Principles of Systems Biology

2. Learning the Standard models and approaches

3. Understand signal transduction and other biological processes

4. Understand modeling of gene expression.

UNIT-I MATLAB USAGE 09 Hours Data types, data structures, conditional loops, 2D and 3D plots, matrix operations, ODE solvers, curve

fitting. MM kinetics, numerical solutions to first order ordinary differential equations (ODE) using

MATLAB. Introduction to system biology and mathematical modelling.

UNIT –II STATIC NETWORK MODELS 10 Hours

Interaction graphs, Bayesian reconstruction of interaction networks, signalling networks, metabolic

networks, modelling with ODE's with examples. Discrete and continuous linear system models,

continuous non-linear systems, stability analysis, parameter sensitivity, parameter estimation, linear

regression of several variables. Physiological modelling: simple models of oscillations with heart as an

example, few more examples..

UNIT-III GENE REGULATION 10 Hours

Models of regulation, transcription factors, gene interaction network, Lac Operan as an example. Protein

system, proteins as enzymes, transporters and carriers, protein protein interaction network, protein-

promoter interactions, comparison of system biology between prokaryotes Vs. Complex eukaryotes.

UNIT – IV METABOLIC PATHWAYS AND THEIR REPRESENTATION 10 Hours

KEGG. Mathematical formulation of elementary biochemical reactions, metabolic flux analysis,

modelling metabolic pathways with ODE, Pharmaco kinetic models (PBPK) with examples, signal

transduction systems. Population systems: Population growth, models of population growth, population

dynamics under external perturbations.

UNIT-V RESOURCE USAGE 09 Hours Resource usage with case studies, in protein-protein interactions, protein-promoter interactions,

pathways and cross-talk between pathways. Comparison of systems biology for prokaryotes vs. complex

eukaryotes.

UNIT –VI Recent advances and research being done in the topics mentioned above units

REFERENCES:

1. Advanced analysis of gene expression microarray data; A. Zhang; World Scientific Publishing,

2006.

2. Systems biology: A textbook; E. Klipp et.al. Wiley, 2009.

3. DNA microarrays; M. Schena; Scion Publishing, 2006.

4. Computational Systems Biology of Cancer; E. Barillot, L. Calzone, P. Hupe and J-P Vert;

CRC Press; 2013.

5. Matlab: A practical introduction to programming and problem solving; S. Attaway,

Butterworth-Hienemann, 2009.

6. Matlab for neuroscientists: An introduction to scientific computing in matlab; P Wallisch, M

Lusignan, M Benayoun, and T. I. Baker, Elsevier, 2009.

7. Essentials of MATLAB programming; S. J. Chapman, 2nd Edition, BAE SYSTEMS,

Australia.

COURSE OUTCOMES:


CO1: Explain bayesian reconstruction of interaction networks, modelling metabolic pathways with ODE

BI-52

44

CO2: Apply Physiological modelling to determine models of oscillations with heart

CO3: Analyse formulation of elementary biochemical reaction

CO4: Evaluate resource usage with case studies

CO5: Design evolution model and self-organization


CIE -50

Marks


Marks


Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks


choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 3 2


BI-53

45



Course Title PYTHON FOR BIOINFORMATICS


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Apply Python for bioinformatics applications.

2. Describe Object oriented programming in Python and different module

3. Biological sequence analysis using Python. Describe advanced analysis techniques using

Python. Describe expression analysis using Python

UNIT- I PYTHON FUNDAMENTALS: 09 Hours Running programs, types and operations, Functions, modules, classes, Exceptions.

UNIT –II OBJECT ORIENTED PROGRAMMING MODULES: 10 Hours

Object Oriented Programming, Threads, process, synchronization, databases and persistence, NumPy,

SciPy, image manipulation, Akando and Dancer modules.

UNIT –III BIOLOGICAL SEQUENCE ANALYSIS: 10 Hours Biopython: Parsing DNA data files, Sequence Alignment, Dynamic programming, Hidden Markov

Model, Genetic algorithms, Multiple Sequence Alignment, gapped alignment.

UNIT –IV ADVANCED ANALYSIS TECHNIQUES : 10 Hours

Trees, text mining, clustering, Self-Organizing Map, Principal Component Analysis, Fourier transforms,

Numerical Sequence Alignment.

UNIT-V EXPRESSION ANALYSIS: 09 Hours

Gene expression array analysis, Spot finding and Measurement, Spreadsheet Arrays and Data Displays,

Applications with Expression Arrays


REFERENCES BOOKS

1. Jason Kinser, “Python for Bioinformatics”, Jones & Bartlett Publishers, 2008.

2. Mark Lutz, “Learning Python”, 3rd edition, O'Reilly, 2007.

3. Alex Martelli, David Ascher, “Python cookbook”, O'Reilly, 2002.

COURSE OUTCOMES:


CO1: Understand python, Object Oriented Programming, Threads, process, synchronization, databases

and persistence

CO2: Apply Python for bioinformatics applications

CO3: Evaluate program of Trees, text mining, clustering

CO4: Implement Bio-python programs for Parsing DNA data files

CO5: Design program for Spreadsheet Arrays and Data Displays


CIE -50

Marks



Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks


choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks

Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks Mapping of course Outcomes(COs) to Program Outcomes(POs)

PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

BI-54

46

CO4 3

CO5 3 2


BI-55

47

Course Code 18CS2L01 M.Tech(Bioinformatics)

Category Practical

Course title ADVANCED DATS STRUCTURES AND ALGORITHMS LABORATORY

Scheme and

Credits


L T P SS Credits

0 0 4 0 2




2. Java Programming







Each student has to work individually on assigned lab exercises. Lab sessions could be scheduled as

one contiguous four-hour session per week. It is recommended that all implementations are carried

out in Java. Exercises should be designed to cover the following topics:


2. AVL Tree














COURSE OUTCOMES




CO3: Design and develop efficient algorithms with minimum complexity using design techniques.




For examination an experiment shall be set

Continuous Internal

Evaluation(Lab=50)


Performance of the student in the

lab every week

20 Write-Up 20

BI-56

48





Note: *= SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50

marks


PO1 PO2 PO3

CO1 2 2

CO2 2

CO3 2

CO4 2

CO5 2

BI-57

49




same effectively.







GUIDE LINES






COURSE OUTCOMES:







Course Code 18BI2S01 M.Tech (Bioinformatics)



Scheme and Credits

No. of Hours/Week


0 0 2 0 1



BI-58

50


CIE – 50

marks


Marks =20

Total:50



PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2










Report : 25%

BI-59

51


Category Audit Course - II



L T P SS Credits

2 0 - - 1

CIE Marks: 50 SEE Marks: -- Total Max. Marks: 50


COURSE OBJECTIVES












Pedagogical practices are being used by teachers in formal and informal classrooms in



Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage:




pedagogical practices. Pedagogic theory and pedagogical approaches. Teachers’ attitudes



Professional development: alignment with classroom practices and follow-up support Peer

support Support from the head teacher and the community. Curriculum and assessment



Research design Contexts Pedagogy Teacher education Curriculum and assessment


BI-60

52


REFERENCES


Compare, 31 (2): 245-261.










6. Chavan M (2003) Read India: A mass scale, rapid, ‘learning to read’ campaign


COURSE OUTCOMES






CO3: How can teacher education (curriculum and practicum) and the school curriculum

and guidance materials best support effective pedagogy




CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks 50


CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


BI-61

53

SEMISTER-III

BI-62

54



Course Title PROTEIN AND INSILICO DRUG DESIGN

Scheme and Credits No. of Hours/Week Semester-III

L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Understand protein structure and engineering.

2. Comprehend the construction and structure generation by molecular modelling.

3. Design drugs through molecular modelling.

4. Describe various docking methods. Explain the computer assisted and new Lead drug discovery

strategies

Unit- I PROTEIN STRUCTURE PREDICTION AND ENGINEERING: 10 Hours

Primary structure and its determination, secondary structure prediction and determination of motifs,

profiles, patterns, fingerprints, super secondary structures, protein folding pathways, tertiary structure,

quaternary structure, methods to determine tertiary and quaternary structure, post translational

modification. Methods of protein isolation, purification and quantification; large scale synthesis of

engineered proteins, design and synthesis of peptides; methods of detection and analysis of proteins.

Protein database analysis, methods to alter primary structure of proteins, examples of engineered proteins

Unit –II MOLECULAR MODELING: 09 Hours

Constructing an Initial Model, Refining the Model, Manipulating the Model, Visualization. Structure

Generation or Retrieval, Structure Visualization, Conformation Generation, Deriving Bioactive

Conformations, Molecule Superposition and Alignment, Deriving the Pharmacophoric Pattern, Receptor

Mapping, Estimating Biological Activities, Molecular Interactions: Docking, Calculation of Molecular

Properties

UNIT –III INSILICO DRUG DESIGN: 10 Hours

Generation of Rational Approaches in Drug Design, Molecular Modelling: The Second Generation,

Conceptual Frame and Methodology of Molecular Modelling, The Field Currently Covered, Importance

of the "Bioactive Conformation", Molecular Mimicry and Structural Similarities, and Superimposition

Techniques, Rational Drug Design and Chemical Intuition, An Important Key and the Role of the

Molecular Model, Limitations of Chemical Intuition

UNIT-IV DOCKING METHODS: 09 Hours

Three - Dimensional Description of Binding Site Environment and Energy Calculation, Automatic

Docking Method, Three-Dimensional Database Search Approaches, Automated Structure Construction

Methods, Structure Construction Methods with known Three-Dimensional Structure of the Receptor,

Structure Construction in the case of Unknown Receptor Structure. Points for Consideration in Structure

Construction Methods, Handling of X-Ray Structures of Proteins, Future Perspectives. Other web based

programs available for molecular modelling, molecular docking and energy minimization techniques –

Scope and limitations, interpretation of results.

UNIT-V COMPUTER ASSISTED NEW LEAD DESIGN AND DRUG DISCOVERY: 10 Hours

Introduction, Basic Concepts, Molecular Recognition by Receptor and Ligand Design, Active

Conformation, Approaches to Discover New Functions, Approaches to the Cases with known and

unknown receptor structure, The Drug Development Process, Introduction, The Discovery and

Development Process, New Lead Discovery Strategies, Composition of Drug Discovery Teams, The

Practice of Computer-Assisted Drug Discovery (CADD), Current Practice of CADD in the

pharmaceutical Industry, Management Structures of CADD Groups, Contributions and Achievements of

CADD Groups, Limitations of CADD Support, Inherent Limitations of CADD Support, State of Current

Computational Models, Software and Hardware Constraints.


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REFERENCES :

1. The molecular modeling perspective in drug design by N Claude Cohen, 1996, Academic Press.

2. Protein Engineering by Moody P.C.E. and A.J. Wilkinson, IRL Press, Oxford, 1990.

3. Biochemistry by Voet and Voet, Wiley New York.

4. Bioinformatics Methods & Applications-Genomics, Proteomics & Drug Discovery by S C

Rastogi, N Mendiratta & P Rastogi, PHI, 2006.

5. Fundamentals of Biochemistry by John Willey, 3rd edition, 2004.

COURSE OUTCOMES:


CO1: Explain primary structure and its determination, secondary structure prediction and determination

of motifs,

CO2: Apply the Drug Development Process

CO3: Analyse design methodology of molecular modelling

CO4: Evaluate Approaches to discover new lead design and drug discovery

CO5: Design Docking, Calculation of Molecular Properties


CIE -50

Marks


Marks


Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks


choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 2

CO2 3

CO3 3

CO4 3

CO5 3 2


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Course Title RECOMBINANT DNA TECHNOLOGY


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Acquire knowledge of central dogma of molecular biology and rDNA technology.

2. Study the techniques of Recombinant DNA technology.

3. Acquire the various methods of genetic transformation of living systems, and selection,

screening and analysis of recombinants.

4. Know various advanced techniques of genetic manipulation of microbes, plants and animals.

UNIT-I CENTRAL DOGMA OF MOLECULAR BIOLOGY: 09 Hours

Molecular structure of genes and chromosomes, Replication, transcription and translation in

prokaryotes and eukaryotes. Gene regulation: Gene regulation and Operon concept, Constitutive,

Inducible and Repressible systems; Operators and Regulatory elements; Positive and negative

regulation of operon: lac, trp, ara, his, and gal. Promoters and enhancers, Structure and function of

different types of RNA and mRNPs. Regulation of Translation: global vs mRNA-specific. Translation

inhibitors, Posttranslational modifications of proteins. Protein trafficking and transport.

UNIT –II COMPONENTS OF RDNA TECHNOLOGY: 10 Hours

Isolation and purification of DNA (genomic and plasmid) and RNA. Chemical synthesis of DNA:

Phosphoramidite method, use of synthesized oligonucleotides. Labelling nucleic acids: Radioactive and

non-radioactive, end labeling, nick translation, primer extension. Nucleic acid hybridization, Gel

electrophoresis. Restriction enzymes, DNA modifying enzymes (Nucleases, Polymerases), DNA ligases.

Host cells: Prokaryotic and eukaryotic hosts. Vectors: plasmid, bacteriophage and other viral vectors,

cosmids, Ti plasmid, Ri plasmids, Yeast Episomal Plasmids (YEPs), Yeast integrative plasmids (Yips),

Yeast replicative plasmids, Yeast Artificial Chromosome (YAC), mammalian and plant expression

vectors, Gate-way vectors.

UNIT –III GENETIC TRANSFORMATION AND CLONING STRATEGIES: 10 Hours Transformation and transfection, Packaging phage DNA in vitro, Alternative DNA deliver methods:

Electroporation, microinjection, biolistic. Cloning from mRNA: synthesis if cDNA, cloning cDNA in

plasmid vectors, cloning cDNA in bacteriophage vectors. Cloning from genomic DNA: Genomic

libraries, preparation of DNA fragments for cloning, ligation, packaging, and amplification of libraries.

Advanced cloning strategies: synthesis and cloning of cDNA, Expression of cloned DNA molecules,

Cloning large DNA fragments in BAC and YAC vectors.

UNIT-IV SELECTION, SCREENING, AND ANALYSIS OF RECOMBINANTS: 09 Hours Genetic selection and screening methods: Using chromogenic substrates, Insertional inactivation,

Complementation of defined mutation, other genetic selection methods. Screening using nucleic acid

hybridization: Nucleic acid probes, Screening clone banks. Screening using PCR, Immunological

screening for expressed genes. Analysis of cloned genes: Characterization based on mRNA translation

in vitro, Restriction mapping, Blotting techniques, DNA sequencing.

UNIT-V THE APPLICATIONS OF RDNA TECHNOLOGY: 10 Hours

Production of proteins: Native and fusion proteins, Yeast expression systems, Baculovirus expression

system, mammalian cell lines. Protein engineering: Rational design, Directed evolution. RNAi

technology: si RNA and miRNA mediated gene silencing, antisense technology. Genome editing:

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas systems, Zinc finger nucleases,

Transcription activator-like effector nuclease (TALENS). Applications of synthetic Riboswitches,

Identification of genes responsible for human diseases. Gene therapy, DNA profiling, Transgenic plants

and animals. Ethical and regulatory issues.


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REFERENCES :

1. Nicholl DST., An introduction to Genetic Engineering, Cambridge, 3rd edition, 2010,

ISBN:978-0-521-61521-1 2.

2. Glick BR, Pasternak JJ, and Patten CL, Molecular Biotechnology – Principles and applications

of recombinant DNA, ASM Press, 4th Edition. 2010. ISBN:978-1-55581-498-4.

3. Brown TA. Gene Cloning and DNA Analysis – An Introduction, Wiley-Blackwell Science, 6th

Edition, 2010, ISBN: 9781405181730.

4. Lodish H, Berk A, Kaiser CA, Krieger M, Scott MP, Bretscher A, Ploegh H and Matsudaira P,

Molecular Cell Biology, Freeman, 8th Edition, 2016, ISBN-13: 978-1464183393.

COURSE OUTCOMES:


CO1: Differentiate between global vs mRNA-specific

CO2: Apply RNAi technology

CO3: Analyse Applications of synthetic Riboswitches, Identification of genes responsible for human

diseases

CO4: Evaluate selection, screening, and analysis of recombinants

CO5: Use RDNA technology to design synthetic Riboswitches for identification of genes responsible

for human diseases


CIE -50

Marks


Marks


Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks


choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 3 2


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Course Title GENETIC ENGINEERING AND BIOTECHNOLOGY


L T P SS Credits

4 0 4



COURSE OBJECTIVES:


1. Understand types of vectors used in recombinant DNA technology

2. Learn mutagenesis, oligonucleotide derived mutagenesis

3. Acquire knowledge on synthetic biology’, and its relevance to informatics and genetic engineering

UNIT-I CONCEPTS IN RECOMBINANT DNA TECHNOLOGY: 10 Hours

Basic principles; introduction to types of vectors used in recombinant DNA technology, their specific

uses and comparison of their features; an overview of various enzymes used in recombinant DNA

technology. Types of vectors and applications. cis and trans-genesis, Agrobacterium mediated genetic

transformation and binary vectors, particle bombardment, transfections, knockouts and transgenics.

UNIT –II USE OF OLIGONUCLEOTIDES AND PCR: 09 Hours

Principles, process and application of PCR, reverse-transcription-PCR and real time PCR. Application

of primers, probes and PCR in various other techniques and research strategies.

UNIT –III GENETIC ENGINEERING: 10 Hours

Mutagenesis: deletion mutagenesis, oligonucleotide derived mutagenesis, site directed mutagenesis. Case

studies in applications of rDNA technology and genetic engineering. Concept of ‘synthetic biology’, and

its relevance to informatics and genetic engineering. Ethical considerations, and potential negative

impacts.

UNIT-IV OTHER COMMON MOLECULAR BIOLOGY TECHNIQUES : 10 Hours

Common methods in the context of questions/problems usually addressed in molecular biology research:

purification, detection and localization of DNA, RNA and proteins, and the corresponding techniques..

UNIT-V STRUCTURAL VALIDATION AND APPLICATION: 09 Hours

Validation: CASP and CAFASP experiments and their findings, Structural bioinformatics in drug design:

Modern drug discovery, Drug target, Lead identification, Lead Optimization.


REFERENCES :

1. Molecular cell biology; H. Lodish, A. Berk, S.L. Zipursky, P. Matsudaira, D. Baltimore and J.

Darnell; W.H Freeman & Comp., 6th ed., 2007.

2. Molecular biology of the cell; B. Alberts et. al.; Taylor & Francis Publishers, 5th ed., 2008.

3. The cell: a molecular approach; G.M. Cooper and R.E. Hausman; ASM Press, 5th ed., 2009.

4. Lewin's Genes X; J E. Krebs, E S. Goldstein, S T. Kilpatrick. Jones & Bartlett Publishers, Inc.

2009.

5. An introduction to genetic analysis; A.J.F. Griffiths, W. H. Freeman & Co., 2008.

6. Recombinant DNA; J.D. Watson; Scientific Amercian Books, 1992

COURSE OUTCOMES:


CO1: Explain vectors used in recombinant DNA technology, their specific uses and comparison of their

features

CO2: Apply CASP and CAFASP for experiments

CO3: Analyse various enzymes for recombinant DNA

CO4: Detect and localization of DNA, RNA and proteins

CO5: Develop reverse-transcription-PCR and real time PCR


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59

CIE -50

Marks


Marks


Quiz/ AAT = 5 Marks


Total: Marks 50

SEE-100

Marks


choice

20*3=60

Marks Total : Marks


internal choice

20*2=40

Marks



PO1 PO2 PO3

CO1 3

CO2 2

CO3 3

CO4 3

CO5 3 2


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Course Code 18CS3P1A M.Tech(Bioinformatics)

Category Theory-Professional Open Elective


Scheme and

Credits


L T P SS Credits

4 0 - - 4



COURSE OBJECTIVES







UNIT-I INTRODUCTION 9 Hours





UNIT-II KNOWLEDGE REPRESENTATION ISSUES 9 Hours


Logic and Representing Knowledge as Rules , Representing simple facts in logic, Computable

functions and predicates, Procedural vs Declarative knowledge, Logic Programming, Forward

vs backward reasoning.

UNIT-III SOFTWARE AGENTS 10 Hours



UNIT-IV REASONING I 10 Hours



UNIT-V METHODS 10 Hours



UNIT -VI Recent advances and research being done in the topics mentioned above units

REFERENCES:

1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach", Prentice Hall,

Third Edition, 2009.


3. I. Bratko, "Prolog: Programming for Artificial Intelligence", Fourth edition, Addison-

Wesley Educational Publishers Inc., 2011.

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61




Press, 2009.


COURSE OUTCOMES





CO4: Analyze agents usage for AI



CIE -50

Marks


Marks


Marks

Quiz/ AAT = 5 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 2

CO4 2

CO5 2 2


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Course Code 18CS3P1B M.Tech(Bioinformatics)



Scheme and

Credits


L T P SS Credits

4 0 - - 4

CIE Marks: 50 SEE

Marks: 50

Total Max. Marks:

100



COURSE OBJECTIVES



2. Analyse data using statistical and data mining techniques.


organization.




UNIT -I BUSINESS ANALYTICS 10 Hours





overview

UNIT -II TRENDINESS AND REGRESSION ANALYSIS 9 Hours




UNIT -III ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS

10 Hours






UNIT -IV FORECASTING TECHNIQUES 10 Hours







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63

UNIT- V DECISION ANALYSIS 9 Hours



UNIT- VI Recent advances and research being done in the topics mentioned above

units

REFERENCES:




COURSE OUTCOMES






modelling to support business decision-making




CIE -50

Marks


Marks


Marks

Quiz/ AAT = 5 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3

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Course Code 18CS3P1C M.Tech(Bioinformatics)



Scheme and

Credits


L T P SS Credits

4 0 - - 4



COURSE OBJECTIVES:



techniques


















Input Modelling - Data collection - Assessing sample independence – Hypothesizing distribution

family with data - Parameter Estimation - Goodness-of-fit tests – Selecting input models in

absence of data- Output analysis for a Single system – Terminating Simulations – Steady state

simulations.









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65

REFERENCES


2005.







COURSE OUTCOMES










CIE –

50

marks

Test I (Unit I,II, & III)-15 Marks Quiz/ AAT = 5

Marks

Unit- VI(AAT) =15

Marks

Total:50

marks


SEE

– 100

marks







marks.


PO1 PO2 PO3

CO1 2

CO2 3

CO3 3

CO4 3

CO5 3 2


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66



1. Carry out the literature review of general research area/current topic and analyse the same effectively.



3. Acquire communication and time management skills for effective and impactful presentation. Interact with

peers to acquire the qualities of thoughtfulness, friendliness, adaptability, responsiveness, and politeness

in-group discussion. Overcome stage fear during the presentation.

GUIDE LINES






COURSE OUTCOMES:





inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear during

the presentation.

Course Code 18BI3S01 M.Tech (Bio Informatics)



Scheme and Credits

No. of Hours/Week


0 0 2 0 1



BI-75

67


CIE – 50

marks


Marks =20

Total:50



PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2



Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall comprise of

Chairman of the Department, Faculty/Guide and one more faculty member nominated by Chairman. The






Report : 25%

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68

INTERNSHIP



1. Provide an opportunity to see how classroom and textbook learning applies to the real world, and to

expose the students to the relevant work experience.

2. Pay close attention to all the steps that go onto completing a job, thereby, help students to become

workforce ready before entering the job market as a graduate. Provide an opportunity to select the topic

of dissertation work by evaluating the requirement of organisation.


GUIDELINES

1. Student has to approach the concerned heads of various Industries/organization, which are related to the

field of specialization of the M. Tech program.

2. If any student gets internship, he/she has to submit the internship offer letter duly signed by the concerned

authority of the company to the Chairperson of the Department.

3. The internship on full time basis will be after the examination of II semester and during III semester for a

period of 8 weeks without affects regular class work.

4. The progress has to be reported periodically to the faculty or to the Guide assigned by the Chairperson as

per the format acceptable to the respective industry /organizations and to the Institution.


6. Students are advised to use ICT tools such as Skype to report their progress and submission of periodic

Course Code 18BI3I01 M.Tech (Bioinformatics)



Scheme and Credits

No. of Hours/Week


--- --- 10 --- 5

CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs for Batch

of Six students


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69

progress reports to the faculty in charge or guide.

7. Duly signed report from internal supervisor (faculty incharge or guide) and external supervisor from the

organization where internship is offered has to be submitted to the Chairperson of the Department for

his/her signature and further processing for evaluation.

The broad format of the internship final report shall contain Cover Page, Certificate from College, Certificate

from Industry / Organization of internship, Acknowledgement, Synopsis, Table of Contents, chapters of

Profile of the Organization - Organizational structure, Products, Services, Business Partners, Financials,

Manpower, Societal Concerns, Professional Practices, Activities of the Department where internship is

done, Tasks Performed and summary of the tasks performed. Specific technical and soft skills that student

has acquired during internship, References and Annexure.

COURSE OUTCOMES:


1. Apply the gained experience along with the theoretical knowledge to solve the real world problems what

engineers ready do.







50*

Marks


examiner=100

50**

Marks



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Rubrics for CIE:




4. Document = 40%


Rubrics for SEE:




4. Document = 40%



PO1 PO2 PO3

CO1 3 2

CO2 2 2

CO3 3


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71

MINI PROJECT


1. Understand the method of applying engineering knowledge/use application software to solve specific

problems after carrying out literature survey.




Student shall take up small problems in the field of domain of program as mini project. It can be related to a

solution to an engineering problem, verification and analysis of experimental data available, conducting

experiments on various engineering subjects, material characterisation, studying a software tool for solution to an

engineering problem, etc.

The mini project must be carried out preferably using the resources available in the department/college and it can

be of interdisciplinary also.

COURSE OUTCOMES:



Individually to generate data/ solve problems.



Rubrics for CIE:


Sl.

no


of CIE


50*



25% 25




Total 100% 100

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72

Rubrics for SEE:

The SEE shall be done by two examiners out of which one examiner is the guide of mini project. The following

weightage would be given for the examination. Evaluation shall be done in batches, not exceeding 6 students.

Sl.

no


of SEE


50**


3 Methodology and Experimental Results & Discussion 30% 30

4 Report 25% 25

5 Viva Voce 20% 20

Total 100% 100

Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.


PO1 PO2 PO3

CO1 3 3

CO2 2 3

CO3 2 3


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73



specifications of the project work, identify the set of feasible solutions, prepare, and execute project plan

considering professional, cultural and societal factors. Identify the problem-solving methodology using

literature survey and present the same.

2. Develop experimental planning and select appropriate techniques and tools to conduct experiments to

Evaluate and critically examine the outcomes followed by concluding the results and identifying relevant

applications. Preparation of synopsis, preliminary report for approval of topic selected along with literature

survey, objectives and methodology.


GUIDELINES

The Dissertation work will start in III semester and should be a problem with research potential and should

involve scientific research, design, generation/collection and analysis of data, determining solution and must

preferably bring out the individual contribution.

The Dissertation work will have to be done by only one student and the topic of dissertation must be

decided by the guide and the student. The dissertation work shall be carried out, on-campus or in an industry

or in an organisation with prior approval from the Chairperson of the Department. The student has to be in

regular contact with the guide atleast once in a week.


College/Industry/Organisation, Acknowledgement, List of Figures and Tables Contents, Nomenclature,

Chapters of Introduction including motivation to choose topic, Literature survey, Conclusion of literature

survey, Objectives and Scope of Dissertation, Methodology to be followed, Experimental requirements,

References and Annexure.

Course Code 18BI3D01 M.Tech (Bioinformatics)



Scheme and Credits

No. of Hours/Week


0 0 10 0 5



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74

The preliminary results (if available) of the problem of Dissertation work may also be discussed in the

report.

COURSE OUTCOME:



National/International reference journals, articles and contact resource persons for selected topics of

Dissertation.


3. Present and defend the contents of Dissertation work phase I in front of technically qualified audience

effectively.




50*

Marks


examiner) and external examiner=100 Marks

50**

Marks









Rubrics for SEE:

BI-83

75







PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3


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76

SEMISTER-IV

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77



1. Carry out the literature review of general research area/current topic and analyse the same effectively.



3. Acquire communication and time management skills for effective and impactful presentation. Interact with

peers to acquire the qualities of thoughtfulness, friendliness, adaptability, responsiveness, and politeness

in-group discussion. Overcome stage fear during the presentation.

GUIDE LINES






COURSE OUTCOMES:





inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear during

the presentation.

Course Code 18BI4S01 M.Tech ( Bioinformatics)



Scheme and Credits

No. of Hours/Week


0 0 2 0 1



BI-86

78


CIE – 50

marks


Marks =20

Total:50



Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall comprise of

Chairman of the Department, Faculty/Guide and one more faculty member nominated by Chairman. The


Rubrics for CIE:




Report of Seminar : 25%


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2


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79


1. Apply/Use different experimental techniques, equipments, software/ Computational/ Analytical /Modelling

and Simulation tools required for conducting tests and generate other relevant data. Students will also be

able to design and develop an experimental setup/test rig.


3. Create a detailed technical document as per format based on the outcome of dissertation work phase I and II.

GUIDELINES

Dissertation work phase II is the continuation of project work started in III semester. The report of Dissertation

work that includes the details of Dissertation work phase I and phase II should be presented in a standard

format. The candidate shall prepare a detailed report of dissertation that includes Cover Paper, Certificate

from College/Industry/Organisation, Acknowledgement, Abstract, Table of contents, List of Figures and

Table, Nomenclature, Chapter of Introduction, Literature survey, Conclusion of literature survey, Objectives

and Scope of dissertation work, Methodology, Experimentation, Results, Discussion, Conclusion, Scope for

future work, References, Annexure and full text of the publication (submitted or published).

COURSE OUTCOMES:


1. Conduct experiments/ implement the capabilities of different Software /Computational / Analytical/

Modelling and simulation tools individually and generate data for validation of hypothesis.

2. Investigate and assess the results obtained within the scope of experiments conducted followed by

conclusions.

3. Prepare detailed technical document, present and defend the contents of Dissertation work in presence of

technically qualified audience effectively.

Course Code 18BI4D01 M.Tech ( Bioinformatics)



Scheme and Credits

No. of Hours/Week


--- --- 30 --- 15



BI-88

80


CIE




100

marks

50*

marks

SEE


2. Dissertation evaluation by external examiner=100 Marks



300

marks

50**

marks


** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for 50 marks.

Rubrics for CIE:






Rubrics for SEE:




4. Methodology, experimentation /Theoretical modelling = 10%





PO1 PO2 PO3

CO1 3 2 3

CO2 2 2 3

CO3 3 3 3


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BI-90







Specialization: Software Engineering

SE-1


VISION

“To strive for excellence in education for the realization of a vibrant and inclusive

society through knowledge creation and dissemination”

MISSION

· Impart quality education to meet national and global challenges

· Blend theoretical knowledge with practical skills

· Pursue academic excellence through high quality research and publications

· Provide access to all sections of society to pursue higher education

· Inculcate right values among students while encouraging competitiveness to


· Produce socially sensitive citizens

· Hasten the process of creating a knowledge society

· To contribute to nation building

SE-2














Programmes.

VISION



society.

MISSION





SE-3


Department of Computer Science and Engineering, UVCE, Bengaluru M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme - 2K18

Specialization: Software Engineering


Strive for excellence in education for the realization of a vibrant and inclusive society through knowledge

creation and dissemination.


CSEM1. Impart quality education and promote scientific temper

CSEM2. Blend theoretical knowledge with practical skills.

CSEM3. Inculcate right values in students.

CSEM4. Providing access to all sections of the society to purse higher education.

CSEM5. Pursue academic excellence through quality teaching, research and publishing

CSEM6: Promote leadership qualities among students

CSEM7: Hasten the process of creating a knowledge society

CSEM8: Produce socially sensitive citizens

Program Outcomes (PO)

SEPO1: An ability to independently carry out research /investigation and development work to


SEPO2: An ability to write and present a substantial technical report/document

SEPO3: Students should be able to demonstrate a degree of mastery over the area as per the

specialization of the program. The mastery should be at a level higher than the

requirements in the appropriate bachelor program


The post graduates of M.Tech in Software Engineering will be provided the knowledge and skill to:

SE-4


M. Tech (Software Engineering)

SEPE01 An ability to analyze, design and synthesize software systems from the individual

component to the entire system architecture

SEPE02 An ability to define, assess, and tailor software quality practices, software engineering

fundamentals and methodologies for development of software projects in a various of

domain.

SEPE03 Be an effective member of a multi-disciplinary software development team and

manage the projects with an awareness of individual professional and ethical

responsibilities.

SEPE04 An ability to critically analyze the issues of industry trends, communicate to varied

stakeholders and use various state-of-the-art practices and tools

.

SE-5



M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (Specialization: SOFTWARE ENGINEERING) under CBCS Scheme – 2K18



L T P S



3 18SE1C03 Software Architecture 4 0 0 0 CSE 4 50 50 4

4

18SE1E1A Agile Software Architecture 4 0 0 0

CSE 4 50 50 4 18SE1E1B Software Reliability Metrics and Models 4 0 0 0

18SE1E1C Software Requirements Engineering 4 0 0 0

5

18SE1E2A Software Design Patterns 4 0 0 0

CSE 4 50 50 4 18SE1E2B Advances Storage Area Networks 4 0 0 0

18SE1E2C Software Verification and Validation 4 0 0 0

6 18SE1L01 Software Development Laboratory 0 0 4 0 CSE 4 50 50 2


8 18SE1S01 Seminar -I 0 0 2 0 CSE 2 50 -- 1


Total 30 450 350 26

Note*=SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.

SE-6



L T P S



3 18SE2C03 Software Testing and Quality Assurance 4 0 0 0 CSE 4 50 50 4

4

18SE2E1A Software Test Automation 4 0 0 0

CSE 4 50 50 4 18WT2E1B User Interface Design and Evaluation 3 0 2 0

18SE2E1C Enterprise Resource Planning 4 0 0 0

5

18SE2E2A Software Agents 4 0 0 0 CSE

4 50 50 4 18SE2E2B Software Security 4 0 0 0

18SE2E2C Software Engineering for Web Applications

6 18CS2L01 Advanced Data Structures and Algorithms Laboratory 0 0 4 0 CSE 4 50 50 2

7 18SE2S01 Seminar -II 0 0 2 0 CSE 2 50 -- 1


Total 28 400 300 24

Semester III

Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits


L T P S CSE

1 18IT3E1A Social Network 4 0 0 0

CSE 4 50 50 4 18SE3E1B Business Intelligence 4 0 0 0

18SE3E1C Software Project Management 4 0 0 0

2 Open Elective 4 0 0 0

---4 50 50 4

3 18SE3S01 Seminar -III 0 0 2 0 CSE 2 50 -- 1

4 18SE3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5

5 18SE3D01 Dissertation Phase -I 0 0 10 0 CSE 10 50 50 5

Total 30 250 200 19

SE-7



L T P S

1 18SE4S01 Seminar -IV 0 0 2 0 CSE 2 50 -- 1

2 18SE4D01 Dissertation Phase -II - - 30 - CSE 30 50 50 15

Total 32 100 50 16

1 18SEMOOC MOOC Course 03


COURSE TYPE

SE: SOFTWARE ENGINEERING CS: COMPUTER SCIENCE AND ENGG C: PROFESSIONAL CORE E: PROFESSIONAL ELECTIVE




Numbers under teaching scheme indicates contact clock hours. Note:

1. In Any curse(Program core or Program Elective), if self-study of 4 hours per week per students is allocated, then teaching scheme of such course will be 3-0-0-4 and the

total credits will be 4.

2. *=SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.

3. #= the CIE test of the lab component of integrated course shall be conducted with the external examiners for 50 marks and shall be reduced to 25 marks

SE-8



M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (Specialization: SOFTWARE ENGINEERING) under CBCS Scheme – 2K18

Open Elective for M. Tech CBCS Scheme



L T P S

1



18CS3P1C Simulation and Modelling

2



18CV3P1C Waste To Energy

18CV3P1D Remote Sensing and Geographic information System

3 18ME3P1A Composite and Smart Materials 4 0 0 0 Mech 4 50 50 4

18ME3P1B Industrial Safety

4


4 0 0 0

EEE4 50 50 4

18EE3P1B Robotics and Automation


5




SE-9

1

Course Code 18CS1C01 M. Tech(Software Engineering)



Scheme and

Credits


L T P SS Credits

3 1 - - 4





COURSE OBJECTIVES






set.






Chinese-Reminder Theorem, Fermat‟s Little theorem, The sum and number of Divisors, The





Distribution. Introduction to Stochastic Processes, Markov process, Markov chain, one step

and n-step Transition Probability, Chapman Kolmogorov theorem (Statement only),

Transition Probability Matrix, Classification of States of a Markov chain. Introduction to

Markovian queuing models, Single Server Model with Infinite system capacity,

Characteristics of the Model (M/M/1) : (∞/FIFO), Single Server Model with Finite System

Capacity, Characteristics of the Model (M/M/1) : (K/FIFO).






Application.








Hamiltonian Graphs, Multi-graphs, Planar Graphs, Euler„s Formula, Graph Colouring and




units

SE-10

2

REFERENCES


2. K. S. Trivedi, “Probability and Statistics with Reliability, Queuing for Computer

Science Applications”, John Wiley and Sons, II Edition, 2008.

3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, “Queuing Networks

and Markov Chains”, John Wiley and Sons, II Edition, 2006.



5. J. A. Bondy and U. S. R. Murty, “Graph Theory and Applications”, Macmillan

Press, 1982.

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 1

CO2 2

CO3 1 1

CO4 1

CO5 2


SE-11

3

Course Code 18CS1C02 M. Tech(Software Engineering)



Scheme and

Credits


L T P SS Credits

4 0 - - 4



COURSE OBJECTIVE












Protocol- HDLC,PPP, Wired LAN‟s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,

Wireless LAN‟s(802.11), Broadband Wireless(802.16).








Management – Exponential RTO back-off – KARN‟s Algorithm – Window









Networks(SDN).

REFERENCES







printing 2012.



edition 2010.

SE-12





4

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3 2 2

CO4 3 2

CO5 2 2 2


SE-13

5

Course Code 18SE1C03 M. Tech(Software Engineering)


Course title SOFTWARE ARCHITECTURE

Scheme and

Credits


L T P SS Credits

4 0 - - 4



Software Engineering

COURSE OBJECTIVES The course will enable the student to:

1. Gain knowledge on the fundamentals of software architecture.

2. Understand various software architecture

3. Evaluate the various software architecture methodologies.

4. Develop the software architecture and quality attributes.

5. Analyze software architecture and software quality.

UNIT- I INTRODUCTION: 09Hours

Basic Concepts of Software Architecture - Architecture business cycle - architectural patterns

- reference models - architectural structures, views ; Introduction to Styles - Simple Styles -

Distributed and Networked Architectures - Architecture for network based applications -

Decentralized Architectures.

UNIT - II DESIGN METHODOLOGIES: 10Hours

Structured Design - Design Practices – Stepwise Refinement – Incremental Design –

Structured System-Analysis and Design – Jackson Structured Programming – Jackson

System Development

UNIT- III ARCHITECTURE DESCRIPTION DOCUMENTATION AND

EVALUATION: 09 Hours

Early Architecture Description Languages –Domain and Style Specific ADLs –Extensible

ADL Documenting Software architecture –Architecture Evaluation –ATAM. Baseline.

UNIT - IV ARCHITECTURE DESIGN: 10 Hours Typical Architectural Design - Data Flow - Independent Components - Call and Return –

Using Styles in Design – choices of styles – Architectural design space – Theory of Design

Spaces –Design space of Architectural Elements – Design space of Architectural styles.

UNIT- V CREATING AN ARCHITECTURE: 10 Hours Understanding Quality Attributes - Functionality and Architecture –Architecture and Quality-

Attributes-System Quality Attributes –Quality attribute Scenarios in Practice - Introducing

Tactics -Availability Tactics –Modifiability Tactics –Performance Tactics -Security Tactics –

Testability Tactics –Usability Tactics –Relationship of Tactics to Architectural Patterns –

Architectural Patterns and Styles.


REFERENCES 1. Len Bass, Paul Clements, Rick Kazman, ―Software Architecture in Practice, Third

Edition, Addison, Wesley, 2012.

2. David Budgen, "Software Design", Second Edition, Pearson Education, 2004.

3. Richard N.Taylor, NenadMedvidovic and Eric M.Dashofy, ―Software Architecture,

Foundations, Theory and Practice, Wiley 2010.

4. Hong Zhu, ―Software Design Methodology from Principles to Architectural Styles,

Elsevier, 2005.

SE-14

6

5. Mary shaw and David Garlan, Software Architecture –Perspectives on an emerging

discipline, Pearson education, 2008.


CO1: Classify various software architecture

CO2: Explain software architecture and architecture design.

CO3: Demonstrate distributed and networked architectures.

CO4: Design methods for improving software quality from the perspective of software

architecture.

CO5: Evaluate the software architecture and software quality.


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks 50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 2 3


SE-15

7

Course Code 18SE1E1A M. Tech(Software Engineering)


Course title AGILE SOFTWARE ENGINEERING

Scheme and

Credits


L T P SS Credits

4 0 - - 4



SOFTWARE ENGINEERING

COURSE OBJECTIVES


1. Uunderstand an iterative, incremental development process leads to faster

delivery of more useful software

2. Apply agile development methods

3. Analyse the roles of prototyping in the software process

4. Explore the principles and practices of extreme programming

5. Create the concept of Mastering Agility

UNIT-I AGILE METHODS: 9 Hours Introduction to Agile: Understanding Success, Beyond Deadlines, The Importance of

Organizational Success, Enter Agility, Agile Methods,

UNIT-II UNDERSTANDING XP: 9 Hours The XP Lifecycle, The XP Team, XP Concepts, Adopting XP: Assess Agility-

UNIT-III PRACTICING XP: 10 Hours Thinking: Pair Programming, Energized Work, Informative Workspace, Root-Cause

Analysis, Retrospectives, Collaborating: Trust, Sit Together, Real Customer Involvement,

Ubiquitous Language, Stand-Up Meetings, Coding Standards, Iteration Demo, Reporting,

Releasing:“Done Done”, No Bugs, Version Control, Ten-Minute Build, Continuous

Integration, Collective Code Ownership, Documentation. Planning: Vision, Release

Planning, The Planning Game, Risk Management, Iteration Planning, Slack, Stories,

Estimating. Developing: Incremental requirements, Customer Tests, Test-Driven

Development, Refactoring, Simple Design ,Incremental Design and Architecture, Spike

Solutions, Performance Optimization, Exploratory Testing

UNIT-IV MASTERING AGILITY: 10Hours Values and Principles: Commonalities, About Values, Principles, and Practices, Further

Reading, Improve the Process: Understand Your Project, Tune and Adapt, Break the Rules,

Rely on People :Build Effective Relationships, Let the Right People Do the Right Things,

Build the Process for the People, Eliminate Waste :Work in Small, Reversible Steps, Fail

Fast, Maximize Work Not Done, Pursue Throughput

UNIT-V DELIVER VALUE: 10 Hours Exploit Agility, Only Releasable Code Has Value, Deliver Business Results, Deliver

Frequently, Seek Technical Excellence :Software Doesn‟t Exist, Design Is for

Understanding, Design Trade-offs, Quality with a Name, Great Design, Universal Design

Principles, Principles in Practice, Pursue Mastery


REFERENCES 1. James shore, Chromatic, “The Art of Agile Development (Pragmatic guide to

agile software development)”, O'Reilly Media, Shroff Publishers &Distributors,

2007.

2. Robert C. Martin, “ Agile Software Development, Principles, Patterns, and

SE-16

8


PO1 PO2 PO3

CO1 2

CO2 3

CO3 2

CO4 2

CO5 2


Practices” Prentice Hall; 1stedition, 2002.

3. Craig Larman, “Agile and Iterative Development A Manger‟s Guide”,Pearson

Education, First Edition, India, 2004.

4. David J. Anderson; Eli Schragenheim, ―Agile Management for Software

Engineering: Applying the Theory of Constraints for Business Results, Prentice

Hall, 2003

5. Hazza&Dubinsky, ―Agile Software Engineering, Series: Undergraduate Topics

inComputer Science, Springer, VIII edition, 2009

6. Craig Larman, ―Agile and Iterative Development : A manager ̳s Guide, Addison-

Wesley, 2004


CO1:Identify various software development process and Agile methods

CO2:Understand The XP Lifecycle, XP Concepts, Adopting XP

CO3:Work on Pair Programming, Root-Cause Analysis, Retrospectives, Planning,

Incremental Requirements, Customer Tests

CO4: Analyse Agile principles and practices

CO5: Implement Concepts to Eliminate Waste


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks

SE-17

9

Course Code 18SE1E1B M. Tech(Software Engineering)


Course title SOFTWARE RELIABILITY METRICS AND MODELS

Scheme and

Credits


L T P SS Credits

4 0 - - 4




COURSE OBJECTIVES


1. Understand software reliability and software quality

2. Remember different notions of defects and classify them

3. Apply software metrics in a rigorous way to measure the software quality

4. Analyse resource usage model, resource utilization, recommended Models

5. Evaluate reliability of software

UNIT - I INTRODUCTION TO SOFTWARE RELIABILITY: 9 Hours Basic Concepts, Failure and Faults, Environment, Availability, Modeling, uses,

requirements reliability metrics, design &code reliability metrics, testing reliability

metrics

UNIT- II SOFTWARE RELIABILITY MODELING: 10 Hours Concepts, General Model Characteristic, Historical Development of models, Model

Classification scheme, Markovian models, General concepts, General Poisson Type

Models, Binomial Type Models, Poisson Type models, Fault reduction factor for Poisson

Type models.

UNIT-III COMPARISON OF SOFTWARE RELIABILITY MODELS: 10 Hours Comparison Criteria, Failure Data, Comparison of Predictive Validity of Model Groups,

Recommended Models, Comparison of Time Domains, Calendar Time Modeling, Limiting

Resource Concept, Resource Usage model, Resource Utilization, Calendar Time

Estimation and confidence Intervals

UNIT-IV FUNDAMENTALS OF MEASUREMENT: 9 Hours Measurements in Software Engineering, Scope of Software metrics, Measurements theory,

Goal based Framework, Software Measurement Validation.

UNIT-V MEASURING SOFTWARE PRODUCT: 10 Hours Measurement of Internet Product Attributes, Size and Structure, External Product

Attributes, Measurement of Quality, Software Reliability: Measurement and Prediction.


units

REFERENCES

1. Norman Fenton, James Bieman, Software Metrics: A Rigorous and Practical

Approach, 3 rd edition, CRC Press, 2015

2. John D. Musa, Anthony Iannino, KazuhiraOkumoto, Software Reliability,

Measurement, Prediction, Application, Series in Software Engineering

andTechnology, McGraw Hill, 1987

3. John D. Musa, Software Reliability Engineering, Tata McGraw Hill, 1999

SE-18

10


CO1: Define software reliability and software quality

CO2: Explain different notions of defects and defects classification

CO3: Implement some software metrics fore measurement of software quality

CO4: Compare various software reliability models

CO5: Investigate software reliability of given application


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 2

CO4 3

CO5 2 3


SE-19

11

Course Code 18SE1E1C M. Tech(Software Engineering)


Course title SOFTWARE REQUIREMENTS ENGINEERING

Scheme and

Credits


L T P SS Credits

4 0 - - 4




COURSE OBJECTIVES


1. Understand the basics of requirements engineering

2. Apply different techniques for requirements elicitation

3. Analyse need of requirements analysis in requirement integration

4. Evaluate various methodologies for requirements development

5. Formulate the current trends in requirements prioritization and validation.

UNIT -I INTRODUCTION TO SOFTWARE RELIABILITY: 9 Hours Software Requirement Overview, Software Development Roles, Software Development

Process Kernels, Commercial Life Cycle Model, Vision Development, Stakeholders

Needs &Analysis, Stakeholder needs, Stakeholder activities.

UNIT -II REQUIREMENTS ELICITATION: 10 Hours The Process of Requirements Elicitation, Requirements Elicitation Problems, Problems

of Scope, Problems of Understanding, Problems of Volatility, Current Elicitation

Techniques, InformationGathering, Requirements Expression and Analysis, Validation,

An Elicitation Methodology Framework, A Requirements Elicitation Process Model,

Methodology over Method, Integration of Techniques, Fact-Finding, Requirements

Gathering, Evaluation and Rationalization, Prioritization, Integration and Validation.

UNIT -III REQUIREMENTS ANALYSIS: 10 Hours Identification of Functional and Non Functional Requirements, Identification of

Performance Requirements, Identification of safety Requirements, Analysis, Feasibility

and Internal Compatibility of System Requirements, Definition of Human Requirements

Baseline.

UNIT -IV REQUIREMENTS DEVELOPMENT: 10 Hours Requirements analysis, Requirements Documentation, Requirements Development

Workflow, Fundamentals of Requirements Development, Requirements Attributes

Guidelines Document, Supplementary Specification Document, Use Case Specification

Document, Methods for Software Prototyping, Evolutionary prototyping, Throwaway

prototyping.

UNIT -V REQUIREMENTS VALIDATION: 9 Hours Validation objectives, Analysis of requirements validation, Activities, Properties,

Requirement reviews, Requirements testing, Case tools for requirements engineering.


units

REFERENCES 1. Ian Sommerville, Pete Sawyer, Requirements Engineering: A Good Practice Guide,

Sixth Edition,Pearson Education, 2004

2. Dean Leffingwe, Don Widrig, Managing Software Requirements A Use Case

Approach, Second Addition, Addison Wesley, 2003

3. Karl Eugene Wiegers, Software Requirements, Word Power Publishers, 2000

4. Ian Graham, Requirements Engineering and Rapid Development, Addison Wesley,

1998

5. Wiegers, Karl, Joy Beatty, Software requirements, Pearson Education, 2013

SE-20

12


CO1. Summarize the basics of requirements engineering

CO2: Illustration of requirements elicitation

CO3: Abstract need of requirements analysis in requirement integration

CO4: Assess various methodologies for requirements development

CO5: Investigate new techniques in requirements prioritization and validation


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 3 3


SE-21

13

Course Code 18SE1E2A M. Tech(Software Engineering)


Course title SOFTWARE DESIGN PATTERNS

Scheme and

Credits


L T P SS Credits

4 0 - - 4




COURSE OBJECTIVES


1. Identify appropriate design patterns for design problems

2. Understand common design patterns

3. Apply incremental/iterative development

4. Evaluate the quality of software source code

5. Develop design pattern for Reframe badly designed program

UNIT – I INTRODUCTION TO DESIGN PATTERNS: 09 Hours

Design Patterns Arose from Architecture and Anthropology - Architectural to Software

Design Patterns - Advantages of Design Patterns - Adapter Pattern - Strategy Pattern -

Bridge Pattern - Abstract Factory Pattern

UNIT-II NEW PARADIGM OF DESIGN: 10 Hours

Principles and Strategies of Design Patterns - Open-Closed Principle – Designing from

Context - Encapsulating Variation. Commonality and Variability Analysis - Analysis

Matrix - Decorator Pattern - Open Closed Principle – The Principle of encapsulating

variation – Abstract Classes vs Interfaces

UNIT- III VALUES OF PATTERNS: 09 hours

Observer Pattern - Categories of Patterns - Template Method Pattern – Applying the

Template Method to the Case Study - Using Template Method Pattern to Reduce

Redundancy.

UNIT-IV APPLYING DESIGN PATTERNS: 10 Hours

Design Patterns: Factories - Singleton Pattern and the Double-Checked Locking Pattern -

Applying Singleton Pattern to Case Study. Object Pool Pattern - 31Management of Objects.

Factory Method Pattern - Factory Method Pattern – Object Oriented Pool Pattern

UNIT–V CASE STUDIES: 10 Hours

What to Expect from Design Patterns - The Pattern Community An Invitation – A Parting

Thought - A Case Study : Designing a Document Editor : Design Problems, Document

Structure, Formatting, Embellishing the User Interface, Supporting Multiple Look-and-Feel

Standards, Supporting Multiple Window Systems, User Operations Spelling Checking and

Hyphenation.


units

REFERENCES 1. Jason McC. Smith, “Elemental design Patterns”, Pearson, 2012.

2. Alan Shalloway and James R.Trott, “Design Patterns explained: A new

perspective on Object-Oriented Design, 2006.

3. Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, “Design Patterns:

Elements of Reusable Object-Oriented Software”, Addison-Wesley, 2003.

4. Eric Freeman, Elisabeth Freeman, Kathy Sierra, Bert Bates, “Head First Design

Patterns A Brain-Friendly Guide

SE-22

14


CO1: Outline appropriate design patterns for various problems

CO2: Apply principles in the design of object oriented systems.

CO3: Examine an understanding of a range of design patterns.

CO4: Comprehending a design presented using this vocabulary.

CO5: Assess and apply suitable patterns in specific contexts


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 3


SE-23

15

Course Code 18SE1E2B M. Tech(Software Engineering)


Course title ADVANCED STORAGE AREA NETWORKS


L T P SS Credits

4 0 - - 4



1.Computer Networks


3.Operating Systems

COURSE OBJECTIVES



























Software Components of SAN: The switch‟s Operating system; Device Drivers; Supporting the

switch‟s components; Configuration options for SANs.






REFERENCES

SE-24

16


India 2013.

2. Robert Spalding: “Storage Networks The Complete Reference”, Tata McGraw-Hill, 2011.



4. Richard Barker and Paul Massiglia: “Storage Area Network Essentials A Complete Guide to

understanding and Implementing SANs”, Wiley India, 2006.

COURSE OUTCOMES :








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 1 2


SE-25

17

Course Code 18SE1E2C M. Tech(Software Engineering)


Course title SOFTWARE VERIFICATION AND VALIDATION

Scheme and

Credits


L T P SS Credits

4 0 - - 4




COURSE OBJECTIVES


1. Understand the principles of verification and validation

2. Apply the various verification and validation techniques

3. Analyze usage of appreciate tools for verification and validation

4. Evaluate tracing tools, tools for testing and tools for verification and validation

5. Create UML behavioral diagrams –probabilistic model


Principles of verification and validation – software architecture frameworks – model driven

architecture – UML – systems modeling language – verification, validation and

accreditation.

UNIT -II METHODS OF SOFTWARE VERIFICATION: 09 Hours

Verification and validation life cycle – traceability analysis – interface analysis – design and

code verification – test analysis - Reviews – inspections - walkthroughs – audits – tracing –

formal proofs – Model based verification and validation - Program verification techniques –

formal methods of software verification – clean room methods.

UNIT -III TESTING: 10 Hours

Stages of Testing: Test Planning – Test design – Test case definition – Test procedure – Test

reporting – Unit testing: white box , black box and performance testing – system testing:

Function, performance, interface, operations, resource, security, portability, reliability,

maintainability, safety, regression and stress testing – integration testing – acceptance

testing: capability, constraint testing - structured testing – structured integration testing

UNIT -IV TOOLS FOR SOFTWARE VERIFICATION: 10 Hours

Tools for verification and validation: static analyzer – configuration management tools –

reverse engineering tools – tracing tools – tools for formal analysis – tools for testing – test

case generators – test harnesses – debuggers – coverage analyzers – performance analysers

– test management tools.

UNIT -V ADVANCED APPROACHES: 10 Hours

Automatic approach for verification and validation – validating UML behavioral diagrams –

probabilistic model checking of activity diagrams in SysML – metrics for verification and

validation

UNIT -VI Recent advances and research being done in the topics mentioned above

units

REFERENCES 1. Mourad Debbabi, Hassaine F, Jarrya Y., Soeanu A., Alawneh L.,”Verification

and Validation in Systems Engineering”, Springer, 2010

2. Marcus S. Fisher, “Software Verification and Validation: An Engineering and

Scientific Approach”, Springer, 2007

3. ESA Board for Software Standardization and Control (BSSC), “Guide to

software verification and Validation”, European Space Agency ESA PSS-05-10

Issue 1 Revision 1, 1995

4. Avner Engel, “Verification, Validation & Testing of Engineered

SE-26

18

Systems”,Wiley series in systems Engineering and Management, 2010.

COURSE OUTCOMES At the end the students will be able to:

CO1: Identify the different techniques for verification and validation

CO2: Determine available traceability analysis tools on sample requirements

CO3: Demonstrate coverage analyzers in terms of functionality or features used

CO4: Chart the various stages of testing, test planning

CO5: Design system test cases for various testing techniques


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 2 2


SE-27

19

Course Code 18SE1L01 M. Tech(Software Engineering)

Category Practical

Course title SOFTWARE DEVELOPMENT LAB

Scheme and

Credits


L T P SS Credits

- - 4 - 2


50

Total Max. Marks:

100


Prerequisites (if any): Fundamentals of software engg.

COURSE OBJECTIVES

The course will enable the student to

1. Understand software development life cycle of an application

2. Apply software development lifecycle to an application,

3. Analyse SRS and design document,

4. Validate codes, documentation and test cases at appropriate stages of software

development.

5. Create project plan

Choose any one application for performing the following phases.

1. Program Analysis and Project Planning.

Thorough study of the problem, Identify project scope, Objectives, Infrastructure., PROJECT

PLAN DOCUMENTATION

2. Software requirement Analysis

Describe the individual Phases / Modules of the project, Identify deliverables., SRS

DOCUMENTATION

3. Data Modeling

Use work products, Data dictionary, Use case diagrams and activity diagrams, build and test

class diagrams, Sequence diagrams , add interface to class diagrams., DESIGN

DOCUMENTATION

4. Software Development and Debugging :

Use technology of your choice to develop and debug the application, CODE

DOCUMENTATION

5. Software Testing :

Perform validation testing, Coverage analysis, memory leaks, develop test case hierarchy,

Site check and Site monitor., TEST CASE DOCUMENTATION


CO1: Identify software development phases

CO2: illustrate use case diagrams and activity diagrams

CO3: Verify SRS, design document,

CO4: Validate codes, documentation and test case at appropriate stages of software

development.

CO5: Investigate correctness of designed software

SE-28

20


For selected application, the student have to demonstrate different phase of software

development life cycle

Continuous Internal

Evaluation(Lab=50)


Performance of the student in

the lab every week

20 Write-Up 20




Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50

marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 2


SE-29

21

Course Code 18CS1M01 M.Tech (Software Engineering)




L T P SS Credits

2 0 - - 2



COURSE OBJECTIVES



















Indications.



REFERENCES

1. Stuart Melville and Wayne Goddard, “Research methodology: an introduction for science &

engineering students‟”


3. Ranjit Kumar, 2nd Edition, “Research Methodology: A Step by Step Guide for beginners”

Halbert, “Resisting Intellectual Property”, Taylor & Francis Ltd ,2007.







SE-30

22

COURSE OUTCOMES







in particular.







CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks Total:

Marks 100


choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4

CO5 3 3


SE-31

23




same effectively.







GUIDE LINES


2. Topic may be of general/ specific interest to program of engineering or electives not offered

in the semester and to be selected in consultation with the faculty/Guide.



COURSE OUTCOMES:








CIE –

50

marks


Marks =20

Total:50


Course Code 18SE1S01 M.Tech (Software Engineering)



Scheme and Credits

No. of Hours/Week


0 0 2 0 1



SE-32

24


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2










Report : 25%

SE-33

25

Course Code 18CS1M02 M.Tech(Software Engineering)




L T P SS Credits

2 0 - - 1



COURSE OBJECTIVES























REFERENCES




Highman‟sbook.



COURSE OUTCOMES







SE-34

26


CIE -50

Marks



Quiz/AAT=20 Marks

Total: Marks

50


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


SE-35

27

SEMISTER-II

SE-36

28

Course Code 18CS2C01 M.Tech(Software Engineering)



Scheme and

Credits


L T P SS Credits

4 0 - - 4



COURSE OBJECTIVE






5. Understand the recent developments in the area of algorithmic design

UNIT-1 REVIEW OF ANALYSIS TECHNIQUES: 09 Hours




UNIT- II BASIC DATA STRUCTURES: 09 Hours Stacks and Queues, Vectors, Lists, and Sequences, Trees, Priority Queues and Heaps,



UNIT -III DYNAMIC PROGRAMMING: 10 Hours



in a DAG; Johnson‟s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson

method. .

UNIT- IV TRIES AND STRING MACHING ALGORITHMS: 10 Hours



UNIT- V NP-COMPLETENESS: 10 Hours

Polynomial time, Polynomial time verification, NP-Completeness and reducibility, NP-




units

REFERENCES

1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,”

Introduction to Algorithms”, Third Edition, Prentice-Hall, 2011.


3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in C++”, 4th

Edition,

Pearson, 2014.



5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, “Fundamentals of Data Structures in C”,

Silicon Pr, 2007.



SE-37

29

COURSE OUTCOMES





problems



CO5: Use appropriate data structure and algorithms in real time applications..


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3

CO4 2

CO5 2 2


SE-38

30

Course Code 18CS2C02 M.Tech(Software Engineering)



Scheme and

Credits


L T P SS Credits

4 - - - 4



COURSE OBJECTIVES

















Concept of Lamport‟s Logical Clock and Vector Clocks, Termination Detection. A simple

solution to distributed mutual exclusion, Non Token based algorithms: Lamport‟s algorithm,

Ricart Agarwala‟s algorithm, Maekawa‟s algorithm, Token based algorithms: Suzuki Kasami‟s

broadcast algorithm, Raymond‟s tree based algorithm.




Detection Algorithm: Ho-Ramamoorthy‟s Algorithm, Distributed Deadlock Detection


Detection Algorithms: Menasce- Muntz Algorithm, Ho-Ramamoorthy‟s Algorithm.


Problem- Lamport -Shostak- Pease Algorithm, Dolev et al.‟s Algorithm












SE-39

31

REFERENCES


Mcgraw Hill, 2002.


and Sons, 2018.


Hall India, 2017.




COURSE OUTCOMES












CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 1 -

CO2 1 2

CO3 1 2

CO4 1 3

CO5 3 2 2


SE-40

32

Course Code 18SE2C03 M.Tech(Software Engineering)


Course title SOFTWARE TESTING AND QUALITY ASSURANCE

Scheme and

Credits


L T P SS Credits

4 0 0 - 4



COURSE OBJECTIVES


1. Understand software and the quality metrics of various softwares.

2. Apply quality metrics for quality assurance to various softwares.

3. Analyse methodologies in making Software.

4. Evaluate the product finally to check the product Quality.

5. Create new quality metrics for quality assurance

UNIT -I INTRODUCTION: 09 Hours Introduction to Software Quality - Challenges – Objectives – Quality Factors – Components of

SQA – Contract Review – Development and Quality Plans – SQA Components in Project Life

Cycle – SQA Defect Removal Policies – Reviews.

UNIT -II TESTING METHODOLOGIES: 09 Hours Basics of Software Testing – Test Generation from Requirements – Finite State Models –

Combinatorial Designs - Test Selection, Minimization and Prioritization for Regression

Testing – Test Adequacy, Assessment and Enhancement.

UNIT -III TEST STRATEGIES: 10 Hours Testing Strategies – White Box and Black Box Approach – Integration Testing – System and

Acceptance Testing – Performance Testing – Regression Testing - Internationalization Testing

– Ad-hoc Testing – Website Testing – Usability Testing – Accessibility Testing.

UNIT- IV TEST AUTOMATION AND MANAGEMENT: 10 Hours Test plan – Management – Execution and Reporting – Software Test Automation – Automated

Testing tools - Hierarchical Models of Software Quality – Configuration Management –

Documentation Control.

UNIT -V SQA IN PROJECT MANAGEMENT: 10 Hours Project progress control – costs – quality management standards – project process standards –

management and its role in SQA – SQA unit.



CO1: Explain different quality metrics for various softwares

CO2: Illustrate usage of quality metrics to analyse the product Quality.

CO3: Evaluate the test plan and various testing methods.

CO4: Assess software quality standards.

CO5:Develop new quality metrics for software to assure quality

References

1. Daniel Galin, “Software Quality Assurance – from Theory to Implementation”,

Pearson Education, 2009

2. Yogesh Singh, "Software Testing", Cambridge University Press, 2012

3. Aditya Mathur, “Foundations of Software Testing”, Pearson Education, 2008

4. Ron Patton, “Software Testing” , Second Edition, Pearson Education, 2007

SE-41

33

5. Srinivasan Desikan, Gopalaswamy Ramesh, “Software Testing – Principles and

Practices”, Pearson Education, 2006

6. Alan C Gillies, “Software Quality Theory and Management”, Cengage Learning,

Second Edition, 2003.

7. Robert Furtell, Donald Shafer, and Linda Shafer, "Quality Software Project

Management", Pearson Education Asia, 2002.


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 3


SE-42

34

Course Code 18SE2E1A M.Tech(Software Engineering)


Course title SOFTWARE TEST AUTOMATION

Scheme and

Credits

No. of Hours/Week Semester – I I

L T P SS Credit

s

4 - 0 - 3

CIE Marks:

50


100


Prerequisites (if any): Fundamentals of software engineering.

COURSE OBJECTIVES


1. Understand the basics of test automation

2. Appreciate the different aspects of test tool evaluation and test automation approach

selection

3. Analyse the role played by test planning and design in test execution

4. Evaluate various testing tools for testing varied applications

5. Create test automation for given case studies


Fundamentals of test automation Management issues technical issues Background

on software testing Automated test life cycle methodology (ATLM) –Test Maturity

Model – Test Automation Development – Overcoming false expectations of

automated testing – benefits – test tool proposal

UNIT -II TEST AND AUTOMATION FRAMEWORK 10 Hours

Test Tool Evaluation and selection – organisations‗ system engineering environment–

tools that support the testing life cycle – test process analysis – test tool consideration

Test framework – Test Library Management – selecting the test automation approach –

test team management

UNIT -III TEST PLANNING AND DESIGN: 10 Hours Test planning – Test program scope – Test requirements management – Test Events,

Activities and Documentation – Test Environment – Evolving a Test plan Test analysis

and design – Test requirements analysis – Test program design – Test procedure design –

Test development architecture – guidelines – automation infrastructure – test execution

and review – test metrics

UNIT -IV TESTING THE APPLICATIONS: 10Hours Testing Web Applications – Functional Web testing with Twill – Selenium – Testing a

simple Web Application – Testing Mobile Smartphone Applications – Running

automated test scripts – Test tools for Browser based applications – Test Automation

with Emulators

UNIT -V CASE STUDIES: 9 Hours

Test automation and agile project management – database automation – test automation

in cloud – Mainframe and Framework automation – Model based test case generation

– Model based testing of Android applications – exploratory test automation


units

SE-43

35


CO1: Identify the different automation testing tools

CO2: Demonstrate usage of available testing tools to test software applications

CO3: Analyse test metrics based on functionality or features used

CO4: Assess test scripts for automating test execution

CO5: Design test cases and execute them

REFERENCES

1. Elfriede Dustin, Jeff Rashka, “Automated software testing: Introduction,

Management and Performance”, Pearson Education, 2008

2. C. Titus Brown, Gheorghe Gheorghiu, Jason Huggins, “An Introduction to

Testing Web Applications with twill and Selenium”, O'Reilly Media, Inc.,

2007

3. Dorothy Graham, Mark Fewster, “Experiences of Test Automation: Case

Studies of Software Test Automation”, illustrated Edition, Addison-Wesley

Professional, 2012

4. Kanglin Li, Mengqi Wu, “Effective Software Test Automation: Developing

an Automated Software Testing Tool”,John Wiley & Sons, 2006

5. Linda Hayes, “The Automated Testing Handbook”, Software testing Inst.,

1995

6. Julian Harty, “A Practical Guide to Testing Mobile Smartphone Applications,

Vol. 6 of Synthesis Lectures on Mobile and Pervasive Computing Series”,

Morgan & Claypool Publishers, 2009

7. Mark Fewster, Dorothy Graham, “Software Test Automation”, Addison

Wesley, 1999


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


SE-44

36

Course Code 18WT2E1B M.Tech(Software Engineering)

Category Theory-Professional Integrated

Course title USER INTERFACE DESIGN AND EVALUVATION

Scheme and

Credits

No. of Hours/Week Semester – I I

L T P SS Credits

3 - 2 - 4



1. Overview of user-centred design field.

2. Describing requirements.

3. Importance of Evaluation.

COURSE OBJECTIVES


1. Understand the benefits of user centred approach to the design of software,

computer systems and websites.

2. Developing interaction design from user requirements and user interface design

evaluation.

3. Evaluate the user interface design

4. Analyze an established Human computer interaction topics like visibility,

affordance, feedback, metaphors and mental models

5. Apply the design evaluation for the real world applications.


Overview of the user-interface design. Designing for users, Knowledge needed for UI

designs.

UNIT -II REQUIRMENTS FOR DESIGN EVALUVATION: 10 Hours

How to gather requirements; Users and the domain; Tasks and work; Thinking about and

describing requirements; Case study on requirements;

UNIT -III DESIGN: 10 Hours

Work reengineering and conceptual design; Design rationale and Principles; Interaction

design; Interaction Styles; Choosing interaction devices; Hardware; Choosing interaction

elements; Software components; Case study on design; Style guides; guidelines and user-

centred design; Designing GUI; Designing for web; Design embedded computer systems

and small devices.

UNIT -IV EVALUATIONS: 10 Hours Why Evaluation?; deciding on what to evaluate, the strategy; Planning; Analysis and

Interpretation of user-observation evaluation data; Inspections of the user Interface;

Variations and More Comprehensive evaluations; .

UNIT -V PERSUVASION: 09Hours

Communication and using findings; Winning and Maintaining support for user-centred

Design.


units

UNIT-VII (Practical)- Lab exercise using a suitable software to the topics studied in

UNIT-I, UNIT-II, UNIT-III, UNIT-IV and UNIT-V 24 Hours

REFERENCES

1. Ben Shneiderman and Catherine Plaisant, “Designing the User Interface: Strategies

for Effective Human-Computer Interaction”, 5th

Edition, 2014, Pearson

Publications, ISBN:0321537351.

2. Debbie Stone, Caroline Jarrett, Mark woodroffe, Shailey Minocha, “User Interface

Design and Evaluation”,1st Edition Elsevier, 2005.

3. Wilbert O Galitz, ““The essential guide to user interface design”, Wiley, 3rd

Ed,

SE-45

37


PO1 PO2 PO3

CO1 3

CO2 3

CO3 2

CO4 3

CO5 3


2007, ISBN:978-0-471-27139-0.

4. Prece, Rogers and Sharps, “Interaction Design”, 3rd

Edition, 2011, Wiley,

ISBN:978-1-119-02075-2.

5. Alan Dix, Janet Fincay, GRe Goryd, Abowd, Russel Bealg, “Human-Computer

Interactio”, Pearson 3rd Edition, 2004, ISBN 0-13-046109-1.

COURSE OUTCOMES


CO1. Identify the benefits of user centred approach to the design field.

CO2: List out the requirements for design evaluvation.

CO3: Illustrate the need of user interface design

CO4: Evaluate the importance of evaluvation and user interface design

CO5: Design Case Study on user interface Design.


CIE -

Practical



Total: Marks

50

Lab Test: Test =20, Record=20 and Vice-Voce=10

Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks







SE-46

38

Course Code 18SE2E1C M.Tech (Software Engineering)


Course title ENTERPRISE RESOURCE PLANNING

Scheme and

Credits


L T P SS Credits

4 - - - 4



COURSE OBJECTIVES


1. Understand concepts of ERP.

2. Execute procurement, production, and sales business processes using ERP software.

3. Emphasis need of business process knowledge and its application to the business

environment.

4. Evaluate ERP Implementation Success & Failure for an application.

5. Create appreciate ERP in various public and private sector.

UNIT -I INTRODUCTION: 09 Hours Overview – Benefits of ERP – ERP and Related Technologies – ERP Risks – Benefits -

Data Warehousing – Data Mining – On–line Analytical Processing – Data Migration –

ERP, Internet and WWW

UNIT-II ERP IMPLEMENTATION: 09 Hours Implementation Life Cycle – cost model - Implementation Methodology – Hidden Costs –

Organizing Implementation – Vendors, Consultants and Users – Contracts – ERP Project

Management and Monitoring - Business case and ROI analysis - ERP and business process

reengineering..

UNIT -III BUSINESS MODULES: 10 Hours Finance Management – Manufacturing Management – Human capital Management –

Procurement and Inventory Management – Supplier Relationship Management – Supply

chain planning & Management - Logistics Management - Plant Maintenance – Materials

Management – Quality Management – Sales and Distribution – Enterprise Asset

Management Product Lifecycle Management.

UNIT -IV ERP MARKET: 10 Hours ERP & E-business – ERP & CRM - ERP Market Place – SAP–ERP financials – Auditing

ERP – ERP Business Intelligence and Performance Management – ERP for manufacturing:

Auto, Pharma, Consumer Products, Mining – ERP for service sector: Retail, Healthcare,

Telecom, Banking, Insurance, Educational Institutions.

UNIT -V ERP – APPLICATIONS: 10 Hours Lean manufacturing and ERP - Turbo Charge the ERP System – EIA Study of ERP

selection process – Big Bang ERP implementation – Impact of ERP systems on

organizational effectiveness – Knowledge management for enterprise systems – Managing

ERP security


units


CO1: Give examples for an ERP.

CO2: Explain the structure of an ERP system

CO3: Illustrate procurement, production, and sales business processes using ERP software.

CO4: Recommend ERP suitable to Industry and Information Technology Companies

CO5: Design ERP for Retail, Healthcare, Telecom, Banking, Insurance, Educational

Institutions.

SE-47

39

References

1. Alexis Leon, “Enterprise Resource Planning”, Tata McGraw Hill, 2nd Edition,

2008

2. Ray, “Enterprise Resource Planning”, Tata McGraw Hill, 2011

3. Veena Bansal, “Enterprise Resource Planning”, Pearson Education India. 2013

4. Marianne Bradford, “Modern ERP – Select, Implement and Use” – Today‟s

Advanced Business Systems, North Carolina State University, Second Edition,

2010

5. V. Narayanan, “Implementing SAR-ERP Financials – A configuration Guide”,

Tata McGraw Hill, 2010

6. Joseph A. Brady, Ellen F. Monk, Bret J. Wangner, “Concepts in Enterprise

Resource Planning”, Thomson Learning, 2001.


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 3


SE-48

40

Course Code 18SE2E2A M.Tech(Software Engineering)


Course title SOFTWARE AGENTS

Scheme and

Credits


L T P SS Credits

4 0 - - 4



COURSE OBJECTIVES The course will enable the students to:

1. Have an overview of the agent systems and software agents.

2. Understand the basic concepts of intelligent software agents.

3. Explore the use of software agents

4. Analyse and share information to coordinate activities of the agents for the

purpose of group problem solving.

5. Design recurred systems using agents

UNIT - I INTRODUCTION TO AGENTS: 9 hours Introduction to software agent, Applivations, uses and classification of software agent;

Agent Programming Paradigms, Agent Vs Object, Aglet, Mobile Agents, Agent

Frameworks, Agent Reasoning.

UNIT - II JAVA AGENTS: 9 hours Processes, Threads, Daemons, Components, Java Beans, ActiveX, Sockets, RPCs,

Distributed Computing, Aglets Programming, Jini Architecture, Actors and Agents, Typed

and proactive messages.

UNIT – III MULTIAGENT SYSTEMS: 10 hours Interaction between agents, Reactive Agents, Cognitive Agents, Interaction protocols,

Agent oordination, Agent negotiation, Agent Cooperation, Agent Organization, Self-

Interested agents in Electronic Commerce Applications.

UNIT- IV INTELLIGENT SOFTWARE AGENTS: 10 hours Interface Agents, Agent Communication Languages, Agent Knowledge Representation,

Agent Adaptability, Belief Desire Intension, Mobile Agent Applications.

UNIT- V AGENTS AND SECURITY: 10 hours

Agent Security Issues, Mobile Agents Security, Protecting Agents against Malicious Hosts,

Untrusted Agent, Black Box Security, Authentication for agents, Security issues for

Aglets.


units


CO1: Interpret the basics of agents

CO2: Create / develop an agent based system for a particular task.

CO3: Design an application that uses different security issues for intelligent agents.

CO4: Effectively apply agent-based technologies in distributed systems

CO5:Validate the application of distributed information systems that use software agents.

SE-49

41

REFERENCES 1. Bradshaw, " Software Agents ", MIT Press, 2010

2. Russel, Norvig, "Artificial Intelligence: A Modern Approach", Second Edition, Pearson

Education, 2003

3. Richard Murch, Tony Johnson, "Intelligent Software Agents", Prentice Hall, 2000

4. Gerhard Weiss, Multi Agent Systems, A Modern Approach to Distributed Artificial

Intelligence, MIT Press, 2000.

5. Bigus&Bigus, " Constructing Intelligent agents with Java ", Wiley, 1997


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 1 2

CO3 2

CO4 1 2 2

CO5 2


SE-50

42

Course Code 18SE2E2B M.Tech(Software Engineering)


Course title SOFTWARE SECURITY

Scheme and

Credits


L T P SS Credits

4 0 - - 4



COURSE OBJECTIVES

The course will enable the students to

1. Understand the basics of secure programming.

2. Describe most frequent programming errors leading to software vulnerabilities.

3. Analyze security problems in software.

4. Evaluate security threats and software vulnerabilities.

5. Effectively design secure software system.

UNIT -I INTRODUCTION TO SECURITY: 9 Hours

Introduction to Security: Need for security, Security approaches, Principles of security,



Symmetric & Asymmetric Key Cryptography: DES,RSA.

UNIT -II INTRODUCTION TO SOFTWARE SECURITY: 10 Hours Managing software security risk, Selecting software development technologies, An open

source and closed source, Guiding principles for software security, Auditing software,

Buffet overflows, Access control, Race conditions, Input validation, Password

authentication

UNIT-III SECURE RISK MANAGEMENT: 9 Hours Anti-tampering, Protecting against denial of service attack, Copy protection schemes,

Client-side security, Database security, Applied cryptography, Randomness and

determinism

UNIT- IV SECURITY TESTING: 10 Hours Buffer Overrun, Format String Problems, Integer Overflow, and Software Security

Fundamentals SQL Injection, Command Injection, Failure to Handle Errors, and Security

Touchpoints

UNIT- V ADVANCED SOFTWARE SECURITY 10 Hours Cross Site Scripting, Magic URLs, Weak Passwords, Failing to Protect Data, Weak

random numbers, improper use of cryptography Information Leakage, Race Conditions,

Poor usability, Failing to protect network traffic, improper use of PKI, trusting networ

k name resolution


units

REFERENCES 1. J. Viega, G. McGraw. Building Secure Software, Addison Wesley -2011

2. Theodor Richardson, Charles N Thies, Secure Software Design, Jones & Bartlett-

2012

3. Kenneth R. van Wyk, Mark G. Graff, Dan S. Peters, Diana L. Burley, Enterprise

Software Security, Addison Wesley. -2010

SE-51

43

COURSE OUTCOMES At the end the student will be able to

CO1: Identify various risk in the softwares.

CO2: illustrate security problems in the open source software.

CO3: Relate security and software engineering.

CO4: Assess real-time software and its vulnerabilities

CO5: Investigate security flaws in software


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 2 2

CO3 2

CO4 3

CO5 3


SE-52

44

Course Code 18SE2E2C M.Tech(Software Engineering)


Course title SOFTWARE ENGINEERING FOR WEB APPLICATIONS

Scheme and

Credits


L T P SS Credits

4 0 - - 4



COURSE OBJECTIVES:


1. Know the importance of web technologies for the real world applications

2. Learn appropriate scripting languages

3. Know the testing techniques to test the product

4. Gain the skills and project based experience needed for entry into web design and

development careers

5. To use a variety of strategies and tools to create websites

UNIT-I SITE ORGANIZATION AND NAVIGATION: 9 Hours

User centered design, Web medium, Web design process, Evaluating process, Site types

and architectures, Navigation theory, Basic navigation practices, Search, Site maps

UNIT-II ELEMENTS OF PAGE DESIGN: 10 Hours

Browser compatible design issues Pages and Layout, Templates, Text, Color, Images,

Graphics and Multimedia GUI Widgets and Forms, Web Design patterns

UNIT- III SCRIPTING LANGUAGES: 10 Hours

Client side scripting: XHTML, DHTML, JavaScript, XML Server side scripting: Perl,

PHP,ASP/JSP Designing a Simple web application

UNIT -IV PREPRODUCTION MANAGEMENT: 9 Hours

Principles of Project Management, Web Project Method, Project Road Map, Project

Clarification, Solution Definition, Project Specification, Content, Writing and Managing

content

UNIT -V PRODUCTION, MAINTENANCE AND EVALUATION: 10Hours Design and Construction, Testing, Launch and Handover, Maintenance, Review and

Evaluation, Case Study

UNIT -VI Recent advances and research being done in the topics mentioned above

units

REFERENCES

1. Soumen Chakrabarti, Mining the Web, Morgan Kaufmann Publishers, Reprint 2016

2. Bing Liu, Web Data Mining: Exploring Hyperlinks, Contents and Usage Data,

Springer, Second Edition, 2011

3. Paulraj Ponniah, “Data Warehousing Fundamentals”, John Wiley, 2012

4. Jiawei Han and Micheline Kamber, Data Mining, Concepts and Techniques, Elsevier

Publication, 2nd

Edition, 2011

COURSE OUTCOMES


CO1. Understand the importance of Web technologies for the real world applications.

CO2. Apply various scripting languages for the development of web applications

CO3. Discuss the Web design standards.

CO4. Develop websites for local community organizations.

CO5. Verify and analyse the web applications.

SE-53

45


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 1 3

CO3 2 3

CO4 3 3

CO5 3 3


SE-54

46

Course Code 18CS2L01 M.Tech(Software Engineering)

Category Practical

Course title ADVANCED DATS STRUCTURES AND ALGORITHMS

LAB

Scheme and

Credits


L T P SS Credits

0 0 4 0 2




4. Java Programming


1. acquire the knowledge of using advanced data structures

2. acquire the knowledge of sorting and balancing the tree structure

3. understand the usage of graph structures and string matching





following topics:


2. AVL Tree














SE-55

47

COURSE OUTCOMES


CO1. Design and implement basic and advanced data structures extensively.

CO2. Design and apply graph structures for various applications.

CO3. Design and develop efficient algorithms with minimum complexity using design

techniques.

CO4: Design and develop advanced string matching and NP Complete problems


For examination an experiment shall be set

Continuous Internal

Evaluation(Lab=50)


Performance of the student in

the lab every week

20 Write-Up 20




Note: *=SEE shall be conducted for 100 marks and marks obtained shall be reduced for

50 marks


PO1 PO2 PO3

CO1 2 2

CO2 2

CO3 2

CO4 2

CO5 2 2


SE-56

48




same effectively.







GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50





Scheme and Credits

No. of Hours/Week


0 0 2 0 1



SE-57

49









Report : 25%


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2


SE-58

50

Course Code 18CS2M01 M.Tech (Software Engineering)




L T P SS Credits

2 0 - - 1



COURSE OBJECTIVES










UNIT- II THEMATIC OVERVIEW: 3 Hours Pedagogical practices are being used by teachers in formal and informal classrooms in developing


UNIT- III PEDAGOGICAL PRACTICES: 6 Hours Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage: quality




and pedagogical approaches. Teachers‟ attitudes and beliefs and Pedagogic strategies.

UNIT- IV PROFESSIONAL DEVELOPMENT: 6 Hours Professional development: alignment with classroom practices and follow-up support Peer support



UNIT- V RESEARCH GAPS AND FUTURE DIRECTIONS: 3 Hours Research design Contexts Pedagogy Teacher education Curriculum and assessment Dissemination



REFERENCES


(2): 245-261.










6. Chavan M (2003) Read India: A mass scale, rapid, „learning to read‟ campaign

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51


COURSE OUTCOMES











CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


SE-60

52

SEMISTER-III

SE-61

53

Course Code 18IT3E1A M.Tech(Software Engineering)



Scheme and

Credits


L T P SS Credits

4 - - - 4



COURSE OBJECTIVES This course will enable students to





UNIT-I INTRODUCTION: 9 Hours Introduction to Web - Limitations of current Web – Development of Semantic Web –





UNIT- II MODELING AND VISUALIZATION: 10 Hours Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph





UNIT- III SOCIAL NETWORK ANALYSIS TECHNIQUES: 10 Hours Framework - Tracing Smoothly Evolving Communities - Models and Algorithms for












UNIT- V TEXT AND OPINION MINING: 10 Hours Text Mining in Social Networks -Opinion extraction – Sentiment classification and



topics over time


units

SE-62

54

REFERENCES

1. Charu C. Aggarwal, “Social Network Data Analytics”, Springer; 2011

2. Peter Mika, “Social Networks and the Semantic Web”, Springer, 1st edition, 2007.

3. Borko Furht, “Handbook of Social Network Technologies and Applications”,


4. Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social Networking –

Techniques and applications”, Springer, 1st edition, 2011.

5. Giles, Mark Smith, John Yen, “Advances in Social Network Mining and Analysis”,

Springer, 2010.

6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, “Computational Social

Network Analysis: Trends, Tools and Research Advances”, Springer, 2009.

7. Toby Segaran, “Programming Collective Intelligence”, O‟Reilly, 2012

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 2

CO3 1 3

CO4 1 3

CO5 1 1 3


SE-63

55

Course Code 18SE3E1B M.Tech (Software Engineering)


Course title BUSINESS INTELLIGENCE

Scheme and

Credits


L T P SS Credits

4 0 - - 4



COURSE OBJECTIVE


1. Understand the enormous opportunities that currently exists in providing business

intelligence services

2. Apply key data mining methods of classification, prediction, data reduction and

exploration

3. Analyse the strategies of modern enterprise decision makers

4. Evaluate competences in information systems, web science, decision science,

software engineering, and innovation and entrepreneurship.

5. Create BI architecture

UNIT– I INTRODUCTION: 09 Hours

BI Basics – Meeting the BI challenge – BI user models – Basic reporting and querying –

BI Markets - BI and Information Exploitation – Value of BI – BI cycle – Bridging the

analysis gap – BI Technologies – BI Decision Support Initiatives – BI Project Team.

UNIT- II BI BIG PICTURE: 10 Hours Advanced Emerging BI Technologies – Human factors in BI implementations – BI design

and development – OO Approach to BI - BI Environment – BI business process and

information flow – Identifying BI opportunities – Evaluating Alternatives - BI solutions –

BI Project Planning.

UNIT- III BI ARCHITECTURE 10 Hours Components of BI Architecture – BI Design and prototyping – Importance of Data in

Decision Making - Data requirements Analysis - Using OLAP for BI – Data warehouse

and Technical BI Architecture – Business Rules – Data Quality – Data Integration – High

performance BI - BI 2.0 – GoOLAP Fact Retrieval Framework.

UNIT -IV BI TECHNOLOGIES: 10 Hours

Successful BI – LOFT Effect – Importance of BI Tools – BI standardization - Creating

business value through location based intelligence – Technologies enabling BI –

technologies for information integration - Building effective BI Systems – Strategic,

Tactical, Operational and Financial Intelligence.

UNIT -V FUTURE OF BI: 09 Hours

Knowledge Discovery for BI – Markov Logic Networks – BI Search and Text Analytics –

Advanced Visualisation – Semantic Web Technologies for building BI - Service oriented

BI – Collaborative BI - Evaluating BI – Stakeholder model of BI.


units

REFERENCES

1. CindiHowson,"Successful Business Intelligence”, Tata McGraw-Hill Education,

2007

2. David Loshin, “Business Intelligence: The Savvy Manager's Guide”, Morgan

Kaufmann, 2nd Edition, Newnes Publishers, 2012

3. Elizabeth Vitt, Michael Luckevich, Stacia Misner, “Business Intelligence”,

O'Reilly Media, Inc., 2010.

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56

4. Larissa Terpeluk Moss, S. Atre, “Business Intelligence Roadmap: The Complete

Project Lifecycle for Decision-Support Applications, Addison-Wesley Information

Technology Series”, illustrated edition, Addison-Wesley Professional, 2003

5. Marie - Aude Aufaure, Esteban Zimány, “Business Intelligence”, First European

Summer School eBISS, 2011.

6. Murugan Anandarajan, Asokan Anandarajan, Cadambi A. Srinivasan, “Business

Intelligence Techniques: A Perspective from Accounting and Finance”, illustrated

Springer, 2003

7. Rajiv Sabherwal, Irma Becerra-Fernandez, “Business Intelligence”, illustrated

Edition, John Wiley & Sons, 2010

COURSE OUTCOMES


CO1: Explain the business intelligence potential of today data rich environment

CO2: Determine when to use which technique

CO3: Analyse techniques using Excel add-ins

CO4: Assess the intellectual capital required to provide business analytics services.

CO5: Develop BI architecture

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3

CO4 3

CO5 3


SE-65

57

Course Code 18SE3E1C M.Tech (Software Engineering)


Course title SOFTWARE PROJECT MANAGEMENT

Scheme and

Credits


L T P SS Credits

4 - - - 4



1. Fundamentals of Software Engineering

COURSE OBJECTIVES


1. Understand software project management, project life cycle and effort estimation

2. Apply different techniques for software cost estimation and activity planning.

3. Analyse software projects plan and sequencing and scheduling

4. Evaluate a software project and perform project planning.

5. Create the different activity planning models and analyze risk

UNIT -I PROJECT EVALUATION AND PROJECT PLANNING: 9 Hours

Importance Of Software Project Management, Activities Methodologies, Categorization

Of Software Projects, Setting Objectives, Management Principles, Management Control,

Project Portfolio Management, Cost-Benefit Evaluation Technology, Risk Evaluation,

Strategic Program Management, Stepwise Project Planning.

UNIT -II PROJECT LIFE CYCLE AND EFFORT ESTIMATION: 10 Hours Software Process And Process Models, Choice of Process Models, Mental Delivery, Rapid

Application Development, Agile Methods, Extreme Programming, SCRUM, Managing

Interactive Processes, Basics of Software Estimation, Effort and Cost Estimation

Techniques, COSMIC Full Function Points, COCOMO II A Parametric Productivity

Model, Staffing Pattern.

UNIT -III ACTIVITY PLANNING AND RISK MANAGEMENT: 10 Hours

Objectives of Activity Planning, Project Schedules, Activities, Sequencing and Scheduling,

Network Planning Models, Forward Pass and Backward Pass Techniques, Critical Path

(CRM) Method, Risk Identification, Assessment, Monitoring, PERT Technique, Monte

Carlo Simulation, Resource Allocation, Creation of Critical Patterns, Cost Schedules

UNIT -IV PROJECT MANAGEMENT AND CONTROL: 9 Hours

Framework for Management and Control, Collection of Data Project Termination,

Visualizing Progress, Cost Monitoring, Earned Value Analysis, Project Tracking, Change

Control, Software Configuration Management, Managing Contracts, Contract Management

UNIT -V STAFFING IN SOFTWARE PROJECTS: 10 Hours

Managing People, Organizational Behaviour, Best Methods of Staff Selection,

Motivation, The Oldham-Hackman Job Characteristic Model, Ethical and Programmed

Concerns, Working In Teams, Decision Making, Team Structures, Virtual Teams,

Communications Genres, Communication Plans.


units

REFERENCES

1. Bob Hughes, Mike Cotterell and Rajib Mall: Software Project Management, Tata

McGraw Hill, V Edition, 2012.

2. Robert K. Wysocki “Effective Software Project Management”, Wiley Publication,

2011.

3. Walker Royce, “Software Project Management”, Addison-Wesley, 1998.

4. Gopalaswamy Ramesh, “Managing Global Software Projects”, McGraw Hill

Education, 2013.

SE-66

58

COURSE OUTCOMES


CO1: Explain project life cycle and effort estimation

CO2: Apply and practice project management principles while developing Software.

CO3: Verify software projects plan and sequencing and scheduling

CO4: Asses a software project and perform project planning.

CO5: Develop the different activity planning models and analyze risk

management techniques.


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


SE-67

59

Course Code 18CS3P1A M.Tech (Software Engineering)



Scheme and

Credits


L T P SS Credits

4 - - - 4



COURSE OBJECTIVES







UNIT-I INTRODUCTION: 9 Hours Introduction Definition Future of Artificial Intelligence Characteristics and Problem Solving











UNIT-IV REASONING I: 10 Hours Symbolic Logic under Uncertainty , Non-monotonic Reasoning, Logics for non-monotonic






units

REFERENCES:









Press, 2009.


SE-68

60

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 2

CO4 2

CO5 2 2


SE-69

61

Course Code 18CS3P1B M.Tech(Software Engineering)



Scheme and

Credits


L T P SS Credits

4 - - - 4



COURSE OBJECTIVES





organization.




UNIT -I BUSINESS ANALYTICS: 10 Hours Overview of Business analytics, Scope of Business analytics, Business Analytics Process,




overview

UNIT -II TRENDINESS AND REGRESSION ANALYSIS: 9 Hours Modelling Relationships and Trends in Data, simple Linear Regression. Important




10 Hours






UNIT -IV FORECASTING TECHNIQUES: 10 Hours Qualitative and Judgmental Forecasting, Statistical Forecasting Models, Forecasting










units

REFERENCES




SE-70

62

COURSE OUTCOMES










CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


SE-71

63

Course Code 18CS3P1C M.Tech(Software Engineering)



Scheme and

Credits


L T P SS Credits

4 0 - - 4



COURSE OBJECTIVES:



techniques






























REFERENCES


2005.







SE-72

64

COURSE OUTCOMES










CIE –

50

marks


marks

Unit-VI AAT=15

marks

Total:50


SEE

– 100

marks







marks.


PO1 PO2 PO3

CO1 2

CO2 3

CO3 3

CO4 3

CO5 3 2


SE-73

65




same effectively.







GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50





Scheme and Credits

No. of Hours/Week


0 0 2 0 1



SE-74

66









Report : 25%


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2


SE-75

67

INTERNSHIP






become workforce ready before entering the job market as a graduate. Provide an opportunity to

select the topic of dissertation work by evaluating the requirement of organisation.


GUIDELINES

1. Student has to approach the concerned heads of various Industries/organization, which are related

to the field of specialization of the M. Tech program.







Institution.










Business Partners, Financials, Manpower, Societal Concerns, Professional Practices, Activities of the

Department where internship is done, Tasks Performed and summary of the tasks performed.

specific technical and soft skills that student has acquired during internship, References and

Annexure.

Course Code 18SE3I01 M.Tech (Software Engineering)



Scheme and Credits

No. of Hours/Week


--- --- 10 --- 5

CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs for Batch

of Six(06) students


SE-76

68

COURSE OUTCOMES:








CIE

1.Marks awarded by guide (Internal examiner) = 50 marks

2.Marks awarded by the department internship monitoring

committee = 50 marks

50*

Marks

SEE Presentation of internship in the presence of Guide (Internal examiner) and

external examiner=100 Marks

50**

Marks



Rubrics for CIE:




4. Document = 40%


Rubrics for SEE:




4. Document = 40%



PO1 PO2 PO3

CO1 3 2

CO2 2 2

CO3 3


SE-77

69

MINI PROJECT













COURSE OUTCOMES:






Rubrics of CIE


Sl.

no


of CIE


50*



25% 25




Total 100% 100

SE-78

70

Rubrics of Scheme for Semester End Evaluation (SEE):


The following weightage would be given for the examination. Evaluation shall be done in batches,

not exceeding 6 students.

Sl.

no


of SEE


50**



Discussion

30% 30

4 Report 25% 25

5 Viva Voce 20% 20

Total 100% 100

Note:** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50

marks.


PO1 PO2 PO3

CO1 3 3

CO2 2 3

CO3 2 3


SE-79

71



specifications of the project work, identify the set of feasible solutions, prepare, and execute project

plan considering professional, cultural and societal factors. Identify the problem-solving


2. Develop experimental planning and select appropriate techniques and tools to conduct experiments

to Evaluate and critically examine the outcomes followed by concluding the results and identifying

relevant applications. Preparation of synopsis, preliminary report for approval of topic selected

along with literature survey, objectives and methodology.


GUIDELINES


and should involve scientific research, design, generation/collection and analysis of data, determining

solution and must preferably bring out the individual contribution.

The Dissertation work will have to be done by only one student and the topic of dissertation must

be decided by the guide and the student. The dissertation work shall be carried out, on-campus or in

an industry or in an organisation with prior approval from the Chairperson of the Department. The

student has to be in regular contact with the guide atleast once in a week.




Conclusion of literature survey, Objectives and Scope of Dissertation, Methodology to be followed,

Experimental requirements, References and Annexure.

The preliminary results (if available) of the problem of Dissertation work may also be discussed

in the report.

Course Code 18SE3D01 M.Tech (Software Engineering)



Scheme and Credits

No. of Hours/Week


0 0 10 0 5



SE-80

72

COURSE OUTCOME:




of Dissertation.






2.Marks awarded by the department dissertation monitoring committee = 50 marks 50*

Marks


examiner) and external examiner=100 marks

50**

Marks



marks.







Rubrics for SEE:







PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3


SE-81

73

SEMISTER-IV

SE-82

74




effectively.

2. Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic





GUIDE LINES






COURSE OUTCOMES:





inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage

fear during the presentation.


CIE – 50 marks Phase -1 Marks =10 Seminar Report

Marks =20

Total:50


Course Code 18SE4S01 M.Tech ( Software Engineering )



Scheme and Credits

No. of Hours/Week


0 0 2 0 1



SE-83

75





Rubrics for CIE:




Report of seminar : 25%


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2


SE-84

76







phase I and II.

GUIDELINES









COURSE OUTCOMES:




hypothesis.

2. Investigate and assess the results obtained within the scope of experiments conducted followed

by conclusions.

3. Prepare detailed technical document present and defend the contents of Dissertation work in

presence of technically qualified audience effectively.

Course Code 18SE4D01 M.Tech ( Software Engineering)



Scheme and Credits

No. of Hours/Week


--- --- 30 --- 15

CIE Marks: 50 SEE Marks: 50

Total Max. Marks: 100


SE-85

77


CIE




100

marks

50*

marks

SEE


2. Dissertation evaluation by external examiner=100 marks



300

marks

50**

marks


** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for 50 marks.


PO1 PO2 PO3

CO1 3 2 3

CO2 2 2 3

CO3 3 3 3


Rubrics for CIE:






Rubrics for SEE:




4. Methodology, experimentation /Theoretical modelling = 10%



7. Acceptance/Publication technical paper in Journals/Conference = 20%

SE-86







Specialization: Web Technologies

WT1


VISION

―To strive for excellence in education for the realization of a vibrant and

inclusive society through knowledge creation and dissemination‖

MISSION



Pursue academic excellence through high quality research and

publications


Inculcate right values among students while encouraging

competitiveness to promote leadership qualities




WT2




University Visvesvaraya College of Engineering (UVCE) was started as a School of

Mechanical Engineering by Bharat Ratna Sir. M. Visvesvaraya in the year 1913 to meet the

needs of the State for skilled workers with S V Setty as its Superintendent. Later, it was

converted to a full-fledged Engineering College in the year 1917 under the name Government

Engineering College and was affiliated to the University of Mysore. It is the fifth Engineering

College to be established in the country.

After the formation of Bangalore University in 1964, UVCE became one of the

Constituent Colleges of Bangalore University. This is one of the oldest Institutions in the

country imparting technical education leading to B.E., M.E, B.Arch., M.Sc. (Engineering),

M.Arch. and Ph.D. degrees in various disciplines of Engineering and Architecture. The

Institution currently offers 7 Undergraduate (B.E. / B.Arch.) Full-time, three Undergraduate

(B.E.) Part-time and 24 Postgraduate (M.E. / M.Arch.) Programmes.

VISION

The vision of UVCE is to strive for excellence in advancing engineering education through

path breaking innovations across the frontiers of human knowledge to realize a vibrant,

inclusive and humane society.

MISSION

The mission of UVCE is to prepare human resource and global leaders to achieve the above

vision through discovery, invention and develop friendly technologies to promote scientific

temper for a healthy society. UVCE shapes engineers to respond competently and confidently

to the economic, social and organizational challenges arising from globally advancing

technical needs.

WT3



M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme -

2K18

Specialization: Web Technologies


Strive for Centre of Excellence in advancing Computer Science and Engineering education to

produce highly qualified human resources to meet local and global requirement.


CSM1. Implementing effectively, the outcome based education by imparting knowledge of basics

and advances in Computer Science and Engineering and other allied disciplines.




interdisciplinary research, online courses, interaction with institutions of higher learning and

industries, R and D laboratories of national importance, alumni, employers and other internal &

external stake holders.

CSM4. Imbibing awareness of entrepreneurship, ethics, honesty, credibility, social and

environmental consciousness and providing opportunity to the faculty and technical staff for

continuous academic improvement and to equip them with then latest trends in Software

Engineering and thereby inculcate the habit of continuous learning in faculty, staff and

students.

WT4

Program Outcomes

Web Technologies Graduates will be able to:

WTPO1: An ability to independently carry out research/investigate and development work to


WTPO2: An ability to write and present a substantial technical report/document

WTPO3: Students should be able to demonstrate a degree of mastery over the area as per the

specialization of the problem. The mastery should be at a level higher than the

requirements in the appropriate bachelor degree


The post graduates of M.Tech in Computer Networking will provide the knowledge and skill

to:

WTPEO1:Develop core competence in the field of web technologies and develop

themselves as effective professionals by solving real problems with

attention to creativity, Inquisitiveness, critical thinking, effective

communication, and team work.

WTPEO2:Acquire strong knowledge about the principles and concepts of web

technologies and involve in research to analyze, design, and

synthesize data to produce novel solutions.

WTPEO3:Demonstrate ability to adapt to a rapidly changing environment by

having learned and applied new skills and new technologies to

become global leaders in the field of web technologies.

WT5

BANGLORE UNIVERSITY


M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (WEB TECHNOLOGIES) under CBCS Scheme

– 2K18

Semester I



Teaching scheme

Hrs/Week Teaching

DPT

Total

Hrs/week

CIE

Marks

*SEE

Marks Credits

L T P S



3 18WT1C03 Web Design and Services 4 0 0 0 CSE 4 50 50 4

4

18CS1E1A Cloud Computing

4 0 0 0

CSE

CSE

CSE

4 50 50 4 18WT1E1B Recommender System

18WT1E1C Service Oriented Architecture

5

18IT1E2C Web Engineering 3 0 2 0 CSE

CSE

CSE

4 50 50 4 18WT1E2B Web Intelligence 4 0 0 0

18WT1E2A Ethical Hacking

6 18WT1L01 Web Application Development Lab 0 0 4 0 CSE 4 50 50 2


8 18WT1S01 Seminar - I 0 0 2 0 CSE 2 50 -- 1

9 18CS1M02 Audit Course - I ( Technical Paper Writing) 2 0 0 0 English 2 50 -- 1

Total 30 450 350 26

WT6

Semester II



Teaching scheme

Hrs/Week Teaching

DPT

Total

Hrs/week

CIE

Marks

*SEE

Marks Credits

L T P S



3 18WT2C03 Semantic Web 4 0 0 0 CSE 4 50 50 4

4

18WT2E1A Data Warehousing and Web Mining 4 0 0 0 CSE

CSE

CSE

4 50 50 4 18WT2E1B User Interface Design and Evaluation 3 0 2 0

18WT2E1C Trust Management in E-Commerce 4 0 0 0

5

18SE2E2A Software Agents

4 0 0 0

CSE

CSE

CSE

4 50 50 4 18SE2E2B Software Security

18CS2E2C Web Security


7 18WT2S01 Seminar - II 0 0 2 0 CSE 2 50 -- 1

8 18CS2M01 Audit Course - II (Pedagogy Studies) 2 0 0 0 CSE 2 50 -- 1

Total 28 400 300 24

WT7

Semester III



Teaching scheme

Hrs/Week Teaching

DPT

Total

Hrs/week

CIE

Marks

*SEE

Marks Credits

L T P S

1

18IT3E1A Social Network 4 0 0 0

CSE 4 50 50 4 18CS3E1B Big Data Analytics 3 0 2 0

18IT3E1C Information Retrieval Systems 4 0 0 0

2 Open Elective 4 0 0 0 CSE 4 50 50 4

3 18WT3S01 Seminar - III 0 0 2 0 CSE 2 50 1

4 18WT3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5

5 18WT3D01 Dissertation Phase - I 0 0 10 0 CSE 10 50 50 5

Total 30 250 200 19

Open Elective




Teaching

Dept.

Total hrs

/ week

CIE

Marks

*See

Marks Credits

L T P S

1




2




18CV3P1D Remote Sensing and Geographic Information System



4




5




WT8

COURSE TYPE

Semester IV



Teaching scheme

Hrs/Week Teaching

DPT

Total

Hrs/week

CIE

Marks

SEE

Marks Credits

L T P S

1 18CN4S01 Seminar - IV 0 0 2 0 CSE 2 50 1

2 18CN4D01 Dissertation Phase - II 0 0 30 0 CSE 30 50 50 15

Total 32 100 50 16



CS: COMPUTER SCIENCE AND ENGINEERING WT: WEB TECHNOLOGY C: PROFESSIONAL CORE

E: PROFESSIONAL ELECTIVE P: OPEN ELECTIVE M: MANDATORY AUDIT

L: LABORATORY S: SEMINAR I: INTERNSHIP/ MINI PROJECT

D: DISSERTATION



NOTE:

1) In any course (Program Core or Program Elective), if self study of 4 hrs per week for students is allocated, then the teaching scheme of

such courses will be 3-0-0-4 and the total credits will be 4.

2) * = SEE shall be conducted for 100 marks and the marks obtained shall be reduced to 50 marks.

3) # = The CIE test of the lab component of integrated course shall be conducted with the external examiner for 50 marks and shall be

reduced to 25 marks.

I Semester

WT9

Course Code 18CS1C01 M. Tech (Web Technologies)



Scheme and

Credits


L T P S Credits

3 1 - - 4





COURSE OBJECTIVES






set.

















(K/FIFO).






Application.

UNIT -IV RECURRENCE RELATIONS: 09 Hours

Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of



WT10









REFERENCES









Press, 1982.

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 1

CO2 2

CO3 1 1

CO4 1

CO5 2


WT11




Scheme and

Credits

No. of

Hours/Week

Semester – I

L T P SS Credits

4 0 - - 4

CIE Marks:

50


Hrs


COURSE OBJECTIVE











UNIT II- DATA LINK LAYER 10 Hours

Multiple Access Control Protocols(MAC), ALOHA, CSMA/CD (802.3), Data Link



UNIT III- ROUTING AND CONGESTION CONTROL 10 Hours

Design issues, Routing Algorithms, Distance Vector Routing, Link State Routing, Routing
















Networks(SDN).

WT12

REFERENCES



2. Andrew S. Tanenbaum and David J. Wetherall, ―Computer Networks‖, Pearson


3. William Stallings, ―High Speed Networks and Internet‖, , Second Edition, 2012.


printing 2012.



edition 2010.

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 3 2 2

CO4 3 2

CO5 2 2 2






WT13

Course Code 18WT1C03 M. Tech (Web Technologies)


Course title Web Design and Services


L T P S Credits

4 0 - - 4



COURSE OBJECTIVES

1. Understand the concept of web, protocols and role of web organizations.

2. Acquire the concept of web design principle

3. Create web page using web tools.

4. Discuss the web service, architecture and protocols.

5. Create web services and UDDI registry.

UNIT I - Web Foundations: 09 Hours

The Evolution of the Web, Internet Applications, Networks, Internet Address Architecture,

IPv6, Higher Level Protocols FTP, Telnet, SMTP, IMAP, MIME, HTTP, Important

Components of the Web, Web Search Engines, Web Servers, Application Server, Internet

Organizations'-Internet Society, Internet Engineering Task Force, Internet Engineering steering

Group, Internet Assigned Numbers Authority, Internet Architecture Board, Internet Research

Task Force,

UNIT II Web Design Principles: 10 hours

Layout and Composition- web page Anatomy, Grid Theory, Balance, unity, Emphasis, Bread

and Butter layout ,Resizing, Screen Resolution, Color- The color psychology, color

Temperature, Chromatic value, Color Theory, color Scheme, color Tools and Resources,

Texture- point, line ,shape ,volume and Depth, pattern, Building Texture, Typography-Text

Image, web fonts, anatomy of letter form.

UNIT III - Web Design Technologies: 09 Hours

HTML, Cascading Style Sheets, XML, XML Schema, XSLT, Xpath.

UNIT IV - Web Services: 10 Hours Introduction, Server side Architecture-Mainframe Architecture, Client/Server Architecture

,Distributed Architecture, Internet and WWW, Client side Architecture-Dumb Terminals,

Browser-based Clients, Service Oriented Architecture and web services, web services

Applications- Supply Chain Management and Logistics, Customer Relations management,

Education,. Simple object Access Protocol- Message Envelope, Encoding Rules, RPC

Connection, Binding with underlying Protocol.

UNIT V - Web Services language and Registry : 10 Hours

Web service Invocation and Web Service Description Language,-Service Creation,

Description, Service Registration, Service Discovery, Service Invocation, Web services

Description and services through WSDL, Universal Description Discovery and Integration-

Business Information and Taxonomy, Specification and Services, Public and Private

Registries, UDDI nomenclature-Node API sets, UDDI Node UDDI Registries, Data Structure,

WT14

Information Model, Core UDDI-Business Entity, Business Service Binding Template tModel,

Service Publication, Service discovery.

UNIT VI

Emerging Trends in web designing and the Service-Oriented Architectures and

Enterprise to support mobility systems, Internet of Things, Ubiquitous Computing,

collaborative and adaptive business processes, Big Data, and Cloud ecosystems.

REFERENCES

1. Web Technology: Theory and Practice, By: M. Srinivasan, Pearson Education India, 2012

2. Web Services: An Introduction, B V Kumar, S V Subramanya, Tata McGRAW Hill, 2008

3. Web Services Essentials, By: Ethan Cerami, Publisher: O'Reilly Media, Inc., 2002

4. Web Design in a Nutshell, Jennifer Niederst Robbins, O‘Reilly, 3rd

Edition, 2001

5. Web Services: Theory and Practice, AnuraGuruge, Digital Press, 2004

COURSE OUTCOMES

On completion of the course, the students will be able to:

CO1: Summarise the web concepts and organization.

CO2: Outline web design principle for layout and composition of web pages.

CO3: Design web pages using web tools.

CO4: Describe the service-oriented architecture and SOAP.

CO5: Construct bind and unbind services in UDDI.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE –

100

marks




marks




marks.


PO1 PO2 PO3

CO1 1

CO2 3

CO3 2 3

CO4 2

CO5 3


WT15

Course Code 18CS1E1A M. Tech (Web Technologies)




L T P S Credits

4 0 - - 4





COURSE OBJECTIVES





4. Acquire the knowledge of cloud resource management and scheduling mechanism

5. Learn various security issues in cloud computing.







UNIT- II CLOUD COMPUTING: APPLICATIONS AND PARADIGMS 09 Hours

Challenges for Cloud Computing, Existing Cloud Applications and New Application





UNIT-III CLOUD VIRTUALIZATION 10 Hours

Virtualization, Layering and Virtualization, Virtual Machine Monitors, Virtual Machines,





UNIT-IV CLOUD RESOURCE MANAGEMENT AND SCHEDULING 10 Hours

Policies and Mechanisms for Resource Management, Applications of Control Theory to Task








WT16

UNIT-V CLOUD SECURITY 10 Hours

Cloud Security Risks, Security: The Top Concern for Cloud Users, Privacy and Privacy Impact



OS.


cloud computing.

REFERENCES





3. Rajkumar Buyya, James Broberg and Andrzej M. Goscinski , ―Cloud Computing:




COURSE OUTCOMES








CIE –

50

marks


marks

Unit VI(AAT)=15

marks

Total:50


SEE

– 100

marks




Total:100

marks




marks.


PO1 PO2 PO3

CO1 2 2

CO2 2

CO3 1 2

CO4 2 1

CO5 2 2


WT17

Course Code 18WT1E1B M. Tech (Web Technologies)


Course title RECOMMENDER SYSTEM


L T P S Credits

4 0 - - 4




1. Understand the concepts of recommender system

2. Apply recommendations techniques like non-personalized, content-based, and

collaborative filtering

3. Choose a variety of choice-making strategies with the goal of providing affordable,

personal, and high-quality recommendations.

4. Evaluate the recommender system using evaluation metrics.

5. Compare the different types of recommender systems.

UNIT I - INTRODUCTION: 09 Hours

Overview of Information Retrieval, Retrieval Models, Search and Filtering Techniques:

Relevance Feedback, User Profiles, Recommender system functions, Matrix operations,

covariance matrices, Understanding ratings, Applications of recommendation systems, Issues

with recommender system.

UNIT II - CONTENT-BASED FILTERING: 10 Hours

High level architecture of content-based systems, Advantages and drawbacks of content based

filtering, Item profiles, Discovering features of documents, pre-processing and feature

extraction, Obtaining item features from tags, Methods for learning user profiles, Similarity

based retrieval, Classification algorithms.

UNIT III - COLLABORATIVE FILTERING: 09 Hours User-based recommendation, Item-based recommendation, Model based approaches, Matrix

factorization, Attacks on collaborative recommender systems.

UNIT IV - HYBRID APPROACHES: 10 Hours

Opportunities for hybridization, Monolithic hybridization design: Feature combination, Feature

augmentation, Parallelized hybridization design: Weighted, Switching, Mixed, Pipelined

hybridization design: Cascade Meta-level, Limitations of hybridization strategies.

UNIT V – EVALUATING RECOMMENDER SYSTEM: 10 Hours Introduction, General properties of evaluation research, Evaluation designs: Accuracy,

Coverage, confidence, novelty, diversity, scalability, serendipity, Evaluation on historical

datasets, Offline evaluations. Types of Recommender Systems: Recommender systems in

personalized web search, knowledge-based recommender system, Social tagging recommender

systems, Trust-centric recommendations, Group recommender systems.–

UNIT VI -

Recent Trends in recommender systems and web search.

WT18

REFERENCES

1. Jannach D., Zanker M. and FelFering A., Recommender Systems: An Introduction,

Cambridge University Press, 1st edition, 2011.

2. Charu C. Aggarwal, Recommender Systems: The Textbook, Springer, 1st ed, 2016

3. Ricci F., Rokach L., Shapira D., Kantor B.P., Recommender Systems Handbook,

Springer, 1st Edition , 2011.

4. Manouselis N., Drachsler H., Verbert K., Duval E., Recommender Systems for

Learning, Springer (2013), 1st ed.

COURSE OUTCOMES


CO1. Design recommendation system for a particular application domain

CO2. Use non-personalized, content-based, and collaborative filtering recommendations

techniques.

CO3. Apply various choice-making strategies for recommendation.

CO4. Evaluate recommender systems on the basis of metrics such as accuracy, rank accuracy,

diversity, product coverage, and serendipity.

CO5. Differentiate the various recommender systems.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE –

100

marks




marks




marks.


marks.


PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


WT19

Course Code 18WT1E1C M. Tech (Web Technologies)


Course title SERVICE ORIENTED ARCHITECTURE


L T P S Credits

4 0 - - 4




1. Interpret various architecture for application development

2. Demonstrate the importance of SOA in Application Integration

3. learn web service and SOA related tools and understand various case studies

4. Learn implementation details of SOA.

5. Evaluate SOA through deployment and integration.

UNIT I - SOA BASICS : 09 Hours

Software Architecture – Types of IT Architecture – SOA – Evolution – Key components –

perspective of SOA – Enterprise-wide SOA – Architecture – Enterprise Applications –

Solution Architecture for enterprise application – Software platforms for enterprise

Applications – Patterns for SOA – SOA programming models.

UNIT II - SOA ANALYSIS AND DESIGN: 10 Hours

Service-oriented Analysis and Design – Design of Activity, Data, Client and business

process services – Technologies of SOA – SOAP – WSDL – JAX – WS – XML WS for .NET

– Service integration with ESB – Scenario – Business case for SOA – stakeholder

OBJECTIVES – benefits of SPA – Cost Savings.

UNIT III - SOA GOVERNANCE: 09 Hours SOA implementation and Governance – strategy – SOA development – SOA governance –

trends in SOA – event-driven architecture – software as a service – SOA technologies – proof-

of-concept – process orchestration – SOA best practices

UNIT IV - SOA IMPLEMENTATION: 10 Hours

SOA based integration – integrating existing application – development of web services –

Integration - SOA using REST – RESTful services – RESTful services with and without JWS

– Role of WSDL,SOAP and Java/XML mapping in SOA – JAXB Data binding.

UNIT V - APPLICATION INTEGRATION: 10 hours

JAX –WS 2.0 client side/server side development – Packaging and Deployment of SOA

component – SOA shopper case study –WSDL centric java WS with SOA-J – related software

– integration through service composition (BPEL) – case study - current trends.

UNIT VI

Emerging Trends in the Service-Oriented Architectures and Enterprise

1. Shankar Kambhampaly, ―Service–Oriented Architecture for Enterprise

Applications‖,Wiley 2008.

2. Mark D. Hansen, ―SOA using Java Web Services‖, Practice Hall, 2007.

WT20

3. Waseem Roshen, ―SOA-Based Enterprise Integration‖, Tata McGraw-HILL, 2009.

COURSE OUTCOMES


CO1. Compaer different IT architecture.

CO2. Analyze and design of SOA based applications.

CO3. Implement web service and realize of SOA.

CO4. Design and implement of SOA based application.

CO5. Integration using BPEL.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE –

100

marks




marks



Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced

for 50 marks.


PO1 PO2 PO3

CO1 2

CO2 2

CO3 2 3

CO4 2 3

CO5 2 3


WT21

Course Code 18IT1E2C M. Tech (Web Technologies)


Course title WEB ENGINEERING


L T P S Credits

3 - 2 - 4




1. Understand the concepts of Web engineering and requirement engineering.

2. Apply the architecture and models for Web applications.

3. Verify and analyse the Web applications.

4. Provide the knowledge on CGI Programming to implement various Web applications.

5. Design Embedded Web applications using PHP.

UNIT I - INTRODUCTION TO WEB ENGINEERING AND REQUIREMENTS

ENGINEERING: 10 Hours

The need for Web engineering, Categories of Web Applications, Characteristics of Web

Applications. Evolution of Web Engineering, Requirement Engineering and modeling in web

engineering: RE specifics in Web Engineering, principles, modeling requirements. Methods

and Tools for modeling in Web Engineering, Designing a Web application.

UNIT II - WEB APPLICATION ARCHITECTURES AND MODELING WEB

APPLICATIONS: 10 Hours

Introduction- Categorizing Architectures, Specifics of Web Application Architectures,

Components of a Generic Web Application Architecture, Layered Architectures: 2-Layer and

N-Layer Architectures, Data-aspect Architectures, Database-centric Architectures,

Architectures for Web Document Management, Architectures for Multimedia Data. Web

application design, Model based web application development: OOHDM method, W2000

method

UNIT III - TESTING WEB APPLICATIONS: 09 Hours

Introduction, Fundamentals, Test approaches, Test methods and techniques, Test driven

development, Test Automation, Test tools.

UNIT IV - CGI PROGRAMMING: 10 Hours Structural- Apache web server, Apache configuration, MySQL- introduction, Database

independent interface, Loading and Dumping a Database. CGI Programming: Dynamic-

Introduction CGI.pm, Information received by the CGI Program, Form widget Methods, CGI

security considerations.

UNIT V – EMBEDDED WEB APPLICATION 09 Hours Introduction, Security considerations, PHP-introduction, Embedding PHP into HTML,

Configuration, Quick examples, Built-in PHP functions.

UNIT VI

Recent Trends in Web engineering and Web application tools

UNIT – VII (Lab Programs)

1. Write a Perl script to read in a string from the console and print:

(a) The length and reverse of the string

(b) The upper and lower case version of the string

WT22

2. a) Write a Perl program to extract Log file information using regular expression.

b) Write a perl script to compute the nth

power of a given number.

3. a) Write a Perl program to display various Server Information like Server Name, Server

Software, Server protocol, CGI Revision etc.

b) Write a Perl program to accept UNIX command from a HTML form and to display

the output of the command executed.

4. a) Write a Perl Program to check whether the given number is Armstrong number or

not.

b) Write a Perl program to insert name and age information entered by the user into a

table created using MySQL and to display the current contents of this table.

5. Write a Perl program to accept the User Name and display a greeting message

randomly chosen from a list of 4 greeting messages.

6. Write a Perl program to keep track of the number of visitors visiting the web page and

to display this count of visitors, with proper headings.

7. Write a Perl program to display a digital clock which displays the current time of the

server.

8. Write a PHP program to store current date-time in a COOKIE and display the ‗Last

visited on‘ date-time on the web page upon reopening of the same page.

9. Write a PHP program to store page views count in SESSION, to increment the count on

each refresh, and to show the count on web page.

10. Using PHP and MySQL develop a program to accept book information viz. Accession

number, title, authors, edition and publisher from a web page and store the information

in a database and to search for a book with the title specified by the user and to display

the search results with proper headings.

REFERENCES

1. Web Engineering: The Discipline of Systematic Development of Web Applications by

Kappel et al., John Wiley, 2006

2. Web Engineering by Emilia Mendes and Nile Mosley, 1st Edition, Springer, 2006

3. Open Source Web Development with LAMP-using Linux, Apache, MySQL, perl and

PHP by James Lee and Brent Ware, Addison Wesley/Pearson Inc 2003.

COURSE OUTCOMES


CO1. Discuss Web engineering and requirement engineering concepts.

CO2. Make use of the Architecture and various modeling techniques for Web applications.

CO3. Discuss design issues involved in Web application development.

CO4. Validate and use testing process specific to Web applications.

CO5. Develop the Web applications using CGI Programming.


CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50 Test I (unit I,II, & III)-15 Quiz/AAT=05 Marks Total: Marks

WT23

Marks Test II (Unit IV & V) -15 Unit-VI(AAT)=15 Marks 50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks








PO1 PO2 PO3

CO1 3

CO2 3 3

CO3 2

CO4 2 2

CO5 3 3


WT24

Course Code 18WT1E2B M. Tech (Web Technologies)


Course title WEB INTELLIGENCE


L T P S Credits

4 0 - - 4




1. Acquire the importance of qualitative data, get insights and techniques

2. Develop customer-centric approach in dealing with data

3. Understand the principles, tools and methods of Web intelligence

4. Apply analytics for business situations.

5. Building intelligence in the Web.

UNIT I - WEB ANALYTICS 09 Hours

Basics, Traditional Ways, Expectations, Data Collection, Clickstream Data, Weblogs, Beacons,

JavaScript Tags, Packet Sniffing, Outcomes data, Competitive data, Search Engine Data

UNIT II - QUALITATIVE ANALYSIS 10 Hours

Customer Centricity, Site Visits, Surveys, Questionnaires, Website Surveys, Post visits,

Creating and Running- Benefits of surveys, Critical components of successful strategy

UNIT III - WEB ANALYTIC CONCEPTS 10 Hours

URLS, Cookies, Time on site, Page views, Understand standard reports, Website content

quality, Navigation reports (top pages, top destinations, site overlay), Search Analytics,

Internal search, SEO and PPC, Measuring Email and Multichannel Marketing, Competitive

intelligence and Web 2.0 Analytics, Segmentation, Connectable reports

UNIT IV - GOOGLE ANALYTICS 09 Hours

Analytics, Cookies, Accounts vs Property, Tracking Code, Tracking Unique Visitors,

Demographics, Page Views & Bounce Rate Acquisitions, Custom Reporting

UNIT V 10Hours

Goals & Funnels, Filters, Ecommerce Tracking, Real Time Reports, Customer Data Alert,

Adwords Linking, Adsense Linking, Attribution Modeling, Segmentation, Campaign

Tracking, Multi-Channel Attribution

UNIT VI

Recent in Data warehousing and Web Mining, Web Search and Information Retrieval system

REFERENCES

1. Avinash Kaushik, ―Web Analytics 2.0: The Art of Online Accountability and Science Of

Customer Centricity ―, 1st edition, Sybex, 2009.

2. Michael Beasley, ―Practical Web Analytics for User Experience: How Analytics can help

you Understand your Users‖, Morgan Kaufmann, 2013.

3. Magy Seif El-Nasr, Anders Drachen, Alessandro Canossa, eds., ―Game Analytics:

Maximizing the Value of Player Data‖, Springer, 2013.

4. Bing Liu, ―Web Data Mining: Exploring Hyperlinks, Content, and Usage Data‖, 2 nd

Edition, Springer, 2011.

5. Justin Cutroni, ―Google Analytics‖, O‘Reilly, 2010.

6. Eric Fettman, Shiraz Asif, Feras Alhlou , ―Google Analytics Breakthrough‖, John Wiley &

sons, 2016.

WT25

COURSE OUTCOMES


CO1. Explain the concepts and terminologies related to Web analytics.

CO2. Apply various parameters used for Web analytics and their impacts.

CO3. Make use of tools and techniques of Web analytics.

CO4. Get experience on Websites, Web data insights and conversions.

CO5. Design intelligence of Web.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE –

100

marks




marks




marks.


PO1 PO2 PO3

CO1 3

CO2 3 2

CO3 3 2

CO4 3

CO5 2 2


WT26

Course Code 18WT1E2A M. Tech (Web Technologies)


Course title Ethical Hacking


L T P S Credits

4 0 0 - 4



Course Objectives:


1. know the concepts of Ethical Hacking

2. Learn about different tools and techniques in Ethical hacking and security

3. Learn backtrack Linux for ethical hacking.

4. Practically apply using some of the tools.

5. Analyse client side browser exploits and vulnerabilities.

UNIT I – 09 hours

Ethics of Ethical Hacking, Enemy‘s Tactics, Recognizing the Gray Areas in Security,

Vulnerability Assessment, Penetration Testing, The Dual Nature of Tools, Recognizing

Trouble, Emulating the Attack, Proper and Ethical Disclosure, Different Teams and Points of

View, CERT‘s Current Process, Full Disclosure Policy—the RainForest Puppy Policy,

Organization for Internet Safety (OIS), Discovery, Notification, Validation, Resolution,

Release.

UNIT II 09 hours

Physical Penetration Attacks, Conducting a Physical Penetration, Reconnaissance, Mental

Preparation, Common Ways into a Building, The Smokers‘ Door, Manned Checkpoints,

Locked Doors, Physically Defeating Locks, Defending Against Physical Penetrations, Insider

Attacks, Conducting an Insider Attack, Tools and Preparation, Orientation, Gaining Local

Administrator Privileges, Disabling Antivirus, Raising Cain, Defending Against Insider

Attacks.

UNIT III 10 hours

Using the BackTrack Linux Distribution, Using the BackTrack ISO Directly Within a Virtual

Machine, Creating a BackTrack Virtual Machine with VirtualBox, Booting the BackTrack

LiveDVD System, Starting Network Services, Creating a New ISO with Your One-time

Changes, Using a Custom File that Automatically Saves and Restores Changes, Exploring the

BackTrack Boot Menu, Updating BackTrack, Using Metasploit, Metasploit: Getting

Metasploit, Using the Metasploit Console to Launch Exploits, Exploiting Client-Side

Vulnerabilities with Metasploit.

UNIT IV – 10 hours

Passive Analysis, Ethical Reverse Engineering and Considerations, Source Code Analysis,

Auditing , and Utility. Manual Source Code Auditing, Automated Source Code Analysis,

Binary Analysis, Manual Auditing of Binary Code, Automated Binary Analysis Tools,

Advanced Static Analysis with IDA Pro, Static Analysis Challenges, Stripped Binaries,

WT27

Statically Linked Programs and FLAIR, Data Structure Analysis, Extending IDA Pro Scripting

, IDA Pro Plug-In Modules and the IDA Pro SDK.

UNIT V – 10 hours

Client-Side Browser Exploits, Client-Side Vulnerabilities - Bypass Firewall Protections,

Client-Side Applications, Privileges, Targets, Internet Explorer Security Concepts , ActiveX

Controls, Internet Explorer Security Zones, History of attacks and latest Trends, Finding New

Browser-Based Vulnerabilities, Heap Spray to Exploit, Internet Exploiter, Security for

Vulnerabilities, Windows Access Control , Security Identifier, Access Token, Security

Descriptor, The Access Check, Tools for Analyzing Access Control Configurations,

Analyzing Access Control for Elevation of Privilege.

UNIT VI -

Recent trends in ethical hacking, Case study of vulnerability of cloud and mobile platforms.

REFERENCES

1. Shon Harris, Allen Harper, Chris Eagle and Jonathan Ness, Gray Hat Hacking: The Ethical

Hackers' Handbook, TMH Edition

2. Jon Erickson, Hacking: The Art of Exploitation, SPD

COURSE OUTCOMES


CO 1: Appreciate the Cyber Law, ethics and impact of hacking.

CO2 :Understand ethics behind hacking and vulnerability disclosure.

CO3:Understand the core concepts related to malware, hardware and software

vulnerabilities and their causes.

CO4 :Exploit the vulnerabilities related to computer system and networks using state of

the art tools and technologies.

CO5: Differentiate client side and browser based vulneratilities.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE –

100

marks




marks




marks.

WT28


PO1 PO2 PO3

CO1 2

CO2 3

CO3 3 2

CO4 3

CO5 2


WT29

Course Code 18WT1L01 M. Tech (Web Technologies)

Category Laboratory

Course title Web Application Development Lab

Scheme and

Credits


L T P S Credits

- - 4 - 2

CIE Marks:

50


100



1. HTML

COURSE OBJECTIVES


1. Outline the design of web pages using web tools.

2. Design valid XML web document.

3. Construct web service using web service tools.

4. Understand various operations performed on web applications.

5. Develop an web application using web tools.

PART – A

1. Prepare step-wise snapshot to configure Tomcat Web Server/ Apache Web

Server/ IIS Web Server for an application and configure browser for security

settings

2. Develop a home page of an organization using HTML, CSS and Java Script,

having navigational menus etc.

3. Write an XML file which will display the Book information which includes

the following:

Title of the book

Author Name

ISBN number

Publisher name

Edition

Price

Write a Document Type Definition (DTD) to validate the above XML file and

use XSL and CSS to display the page content.

4. Calendar Creation and Display all months using JavaScript/JSP.

5. Write a program to implement WSDL Service (HelloService.WSDL File)

6. Write a program to implement to create a simple web service that converts the

temperature from Fahrenheit to Celsius (using HTTP Post Protocol)

7. Create a photo slide show using JQuery/Javascript.

8. iMovie Exercise - apply basic video editing concepts like cropping, splitting

clips, adding audio, transitions.

PART – B

Development of an application using Web 2.0/ any relevant Computer Science Tool

Note:

Student should execute one program from Part A and Demonstrate web application

from Part B

WT30

COURSE OUTCOMES


CO1: Develop web pages using web tools.

CO2: Demonstrate valid web document.

CO3: Create the web service using WSDL.

CO4: Demonstrate various operations performed in web applications.

CO5: Device an web application using web tool.


The student has to write and implement two programs selecting ONE from each part

Continuous Internal


Marks)




the Lab every week

20 Write up 10



70


Total 100





PO1 PO2 PO3

CO1 1 3

CO2 1 3

CO3 2

CO4 1 3

CO5 1 2 3


WT31

Course Code 18CS1M01 M. Tech (Web Technologies)




L T P SS Credits

2 0 - - 2



COURSE OBJECTIVES















Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of research



Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological research,



UNIT- V PATENT RIGHTS: 6 Hours

Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases. Geographical

Indications.


Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer Software


REFERENCES

1. Stuart Melville and Wayne Goddard, ―Research methodology: an introduction for science &

engineering students‘‖


3. Ranjit Kumar, 2nd Edition, ―Research Methodology: A Step by Step Guide for beginners‖

Halbert, ―Resisting Intellectual Property‖, Taylor & Francis Ltd ,2007.




WT32

7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, ― Intellectual Property in New



COURSE OUTCOMES







in particular.







CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4

CO5 3 3


WT33



1. Carry out the literature review of general research area/current topic and analyse

the same effectively.




presentation. Interact with peers to acquire the qualities of thoughtfulness,

friendliness, adaptability, responsiveness, and politeness in-group discussion.


GUIDE LINES






COURSE OUTCOMES:




3. Make an effective presentation with seamless flow of content within the time

allocated. Overcome inhibition in interacting with peers and hence develop the spirit

of team work. Overcome stage fear during the presentation.


CIE – 50

marks


Marks =20

Total:50


Course Code 18WT1S01 M. Tech (Web Technologies)



Scheme and Credits

No. of Hours/Week


0 0 2 0 1



WT34


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2










Report : 25%

WT35





L T P SS Credits

2 0 - - 1



COURSE OBJECTIVES



















skills are needed when writing the Methods, skills needed when writing the Results, skills are needed

when writing the Discussion, skills are needed when writing the Conclusions.



REFERENCES




Highman‘sbook.



COURSE OUTCOMES




WT36





CIE -50

Marks



Quiz/AAT=20 Marks

Total: Marks

50


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


SEMESTER II

WT37




Scheme and

Credits


L T P S Credits

4 0 - - 4



COURSE OBJECTIVE


















method. .








UNIT VI


structures

REFERENCES





Edition,

Pearson, 2014.




Silicon Pr, 2007.


WT38


COURSE OUTCOMES





problems





CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 2

CO2 2 2

CO3 2 2

CO4 2

CO5 2 2


WT39




Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES





























UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE: 10 Hours

Issues in Load Distribution, Components of a Load Distributing Algorithms, Load Distributing






WT40






REFERENCES


Mcgraw Hill, 2002.


and Sons, 2018.


Hall India, 2017.




COURSE OUTCOMES












CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*2=40

Marks

Total:


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 1

CO2 1 2

CO3 1 2

CO4 1 3

CO5 3 2 2


WT41

Course Code 18WT2C03 M. Tech (Web Technologies)


Course title SEMANTIC WEB

Scheme and

Credits


L T P S Credits

4 0 0 - 4

CIE Marks:

50


100




1. Understand the concept of Ontology and Semantic web

2. Develop web documents by applying semantic web Technologies.

3. Explain stages of Ontology learning

4. Understand the Ontology development methods.

5. Design Ontologies using Ontology tools and tool suites.

UNIT I INTRODUCTION 9 Hours

Philosophical Background ,Component, Types of Ontology , Ontological Commitments

& Categories, Principles for the Design of Ontologies, , Top Level Ontologies,

Linguistic Ontologies, Domain Ontologies , Semantic Web : web to Semantic web with

examples, semantic web technologies, Layers, Architecture.

UNIT II - SEMANTIC WEB AND ONTOLOGY TECHNOLOGIES 09 Hours

Structured Web Documents in XML, Web Resource Description RDF: Overview, XML

based Syntax, RDF-Schema , web Ontology Languages OWL.

UNIT III - ONTOLOGY LEARNING FOR SEMANTIC WEB 10 Hours

Taxonomy for Ontology Learning, Layered Approach, Phases of Ontology Learning,

Importing and Processing Ontologies and Documents, Ontology Learning Algorithms -

Evaluation

UNIT IV ONTOLOGICAL ENGINEERING 10 Hours

Overview, constructing Ontologies manually, reusing, semiautomatic ontology

acquistion, ontology mapping, on-to-knowledge semantic web architecture, ontological

class, constraints. Ontology development methods and methodologies,

METHONTOLOGY- Ontology cross life cycle and conceptual modeling, Comparing

methods and methodology.

UNIT V –. TOOLS AND TOOL SUITES 10 Hours

Evolution, Development of Tools and Tool Suites , Language dependent Ontology-

Ontolingua server, Language independent Ontology- Protege2000, OntoEdit, Ontology

Merge Tools- Prompt plug-in, Chimaera, Glue, FCA-Merge tool set, Ontology based

Annotation Tools- COHSE, SHOE knowledge annotator.

UNIT VI –

Recent Trends in Semantic Web Programming.

WT42

REFERENCES

1. Asuncion Gomez-Perez, Oscar Corcho, Mariano Fernandez-Lopez “Ontological

Engineering: with examples from the areas of Knowledge Management, eCommerce

and the Semantic Web” Springer, 2004

2. Grigoris Antoniou, Frank van Harmelen, “A Semantic Web Primer (Cooperative

Information Systems)”, The MIT Press, 2004.

3. Alexander Maedche, “Ontology Learning for the Semantic Web”, Springer; 1

edition, 2002

4. John Davies, Dieter Fensel, Frank Van Harmelen, “Towards the Semantic Web:

Ontology – Driven Knowledge Management”, John Wiley & Sons Ltd., 2003.

5. John Davies (Editor), Rudi Studer (Co-Editor), Paul Warren (Co-Editor) “Semantic

Web Technologies: Trends and Research in Ontology-based Systems”Wiley

Publications, Jul 2006

6. Dieter Fensel (Editor), Wolfgang Wahlster, Henry Lieberman, James Hendler,

“Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential”,

The MIT Press, 2002

7. Michael C. Daconta, Leo J. Obrst, Kevin T. Smith, “The Semantic Web: A Guide to

the Future of XML, Web Services, and Knowledge Management”, Wiley, 2003

8. Steffen Staab (Editor), Rudi Studer, “Handbook on Ontologies (International

Handbooks on Information Systems)”, Springer 1st edition, 2004

9. Dean Allemang (Author), James Hendler (Author) “Semantic Web for the Working

Ontologist: Effective Modeling in RDFS and OWL” (Paperback), Morgan

Kaufmann, 2008

COURSE OUTCOMES


CO1: Summarise the concept of Ontology and Semantic web.

CO2: Design web documents using semantic web tools.

CO3: Summarise the stages of Ontology learning.

CO4: Distinguish Ontology development methods and methodology

C05: Develop Ontologies using tools and tool suites.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.

WT43


PO1 PO2 PO3

CO1 2

CO2 3

CO3 2

CO4 3

CO5 3


WT44



Course title DATA WAREHOUSING AND WEB MINING

Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100




1. Understand the concepts of Data warehousing and Web Mining

2. Model the architecture and infrastructure of Data warehouse.

3. Compare the Information access and Delivery to classes of users in Data

Warehouse

4. Evaluate the Web search and Information retrieval system using the performance

metrics

5. Design and develop the various algorithms of clustering and classification

techniques

UNIT I - INTRODUCTION TO DATA WAREHOUSING 09 Hours

Need for data warehousing, Basic elements of data warehousing, Trends in data

warehousing. Planning and Requirements: Project planning and management, Collecting

the requirements –

UNIT II - ARCHITECTURE AND INFRASTRUCTURE 10 Hours

Architectural components, Infrastructure and metadata. Data Design and Data

Representation: Principles of dimensional modeling, Dimensional modeling advanced

topics, data extraction, transformation and loading, data quality.

UNIT III - INFORMATION ACCESS AND DELIVERY 10 Hours

Matching information to classes of users, OLAP in data warehouse, Data warehousing

and the web. Implementation and Maintenance: Physical design process, data warehouse

deployment, growth and maintenance.

UNIT IV - INTRODUCTION TO WEB MINING 09 Hours

Types of Web Mining, Crawling and Indexing, Hyperlink Analysis, Resource Discovery

and Vertical portals, Structured and unstructured data mining. Crawling the Web: Basics,

Engineering large scale crawlers, Putting together a crawler. Web Search and Information

Retrieval: Boolean Queries and the Inverted Index, Relevance Ranking

UNIT V – SIMILARITY AND CLUSTERING 10 Hours

Similarity Search, Introduction to Clustering, Formulations and Approaches, Bottom-up

and top-down partitioning paradigms, Clustering and visualization, Probabilistic

Approaches to clustering, Collaborative Filtering, Supervised Learning: Scenario,

Overview of Classification, Evaluating text classifiers, Nearest neighbor learners, Feature

selections, Bayesian, Discriminator and Hypertext Classification.

WT45

UNIT VI -

Recent Trends in Data warehousing and Web Mining, Web Search and Information

Retrieval system.

REFERENCES

1. Soumen Chakrabarti, Mining the Web, Morgan Kaufmann Publishers, Reprint 2016

2. Bing Liu, Web Data Mining: Exploring Hyperlinks, Contents and Usage Data,

Springer, Second

Edition, 2011

3. Paulraj Ponniah, ―Data Warehousing Fundamentals‖, John Wiley, 2012

4. Jiawei Han and Micheline Kamber, Data Mining, Concepts and Techniques, Elsevier

Publication, 2nd

Edition, 2011

COURSE OUTCOMES


CO1. Define the concepts of data warehousing and web mining

CO2. Choose Architecture and Infrastructure od Data Warehouse.

CO3. Usage of Data warehouse to Information access and delivery

CO4. Assess the Web search and Information retrieval using metrics.

CO5. Develop the algorithms of classification and clustering techniques on web data.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 3

CO2 2 1

CO3 1 2

CO4 3

CO5 3


WT46

Course Code 18SE2E1B M. Tech (Web Technologies)


Course title USER INTERFACE DESIGN AND EVALUVATION

Scheme and

Credits


L T P S Credits

3 - 2 - 4



1. Overview of user-centred design field.

2. Describing requirements.

3. Importance of Evaluation.

COURSE OBJECTIVES


1. Understand the benefits of user centred approach to the design of software,

computer systems and websites.

2. Developing interaction design from user requirements and user interface design

evaluation.

3. Evaluate the user interface design

4. Analyze an established Human computer interaction topics like visibility,

affordance, feedback, metaphors and mental models

5. Apply the design evaluation for the real world applications.


Overview of the user-interface design. Designing for users, Knowledge needed for UI

designs.

UNIT -II REQUIRMENTS FOR DESIGN EVALUVATION: 10 Hours

How to gather requirements; Users and the domain; Tasks and work; Thinking about and

describing requirements; Case study on requirements;

UNIT -III DESIGN: 10 Hours

Work reengineering and conceptual design; Design rationale and Principles; Interaction

design; Interaction Styles; Choosing interaction devices; Hardware; Choosing interaction

elements; Software components; Case study on design; Style guides; guidelines and user-

centred design; Designing GUI; Designing for web; Design embedded computer systems

and small devices.

UNIT -IV EVALUATIONS: 10 Hours

Why Evaluation?; deciding on what to evaluate, the strategy; Planning; Analysis and

Interpretation of user-observation evaluation data; Inspections of the user Interface;

Variations and More Comprehensive evaluations; .

UNIT -V PERSUVASION: 09Hours

Communication and using findings; Winning and Maintaining support for user-centred

Design.


units

UNIT-VII (Practical)- Lab exercise using a suitable modelling and analysis package

of the topics studied in UNIT-III, UNIT-IV and UNIT-V 24 Hours

WT47

REFERENCES

1. Ben Shneiderman and Catherine Plaisant, ―Designing the User Interface: Strategies

for Effective Human-Computer Interaction‖, 5th

Edition, 2014, Pearson

Publications, ISBN:0321537351.

2. Debbie Stone, Caroline Jarrett, Mark woodroffe, Shailey Minocha, ―User Interface

Design and Evaluation‖,1st Edition Elsevier, 2005.

3. Wilbert O Galitz, ――The essential guide to user interface design‖, Wiley, 3rd

Ed,

2007, ISBN:978-0-471-27139-0.

4. Prece, Rogers and Sharps, ―Interaction Design‖, 3rd

Edition, 2011, Wiley,

ISBN:978-1-119-02075-2.

5. Alan Dix, Janet Fincay, GRe Goryd, Abowd, Russel Bealg, ―Human-Computer

Interactio‖, Pearson 3rd Edition, 2004, ISBN 0-13-046109-1.

COURSE OUTCOMES


CO1. Identify the benefits of user centred approach to the design field.

CO2: List out the requirements for design evaluvation.

CO3: illustrate the need of user interface design

CO4: Evaluate the importance of evaluvation and user interface design

CO5: Design Case Study on user interface Design.


CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks







WT48


PO1 PO2 PO3

CO1 3

CO2 3

CO3 2

CO4 3

CO5 3


WT49

Course Code 18WT2E1C M. Tech (Web Technologies)


Course title TRUST MANAGEMENT IN E-COMMERCE

Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



COURSE OBJECTIVES


1. Understand fundamental principles of E-Commerce

2. Illustrate technologies & tools for E-Commerce with emphasis on Security

3. Identify best techniques & practices for different types of legacy & partner

requirements

4. Handle & address risk management

5. Evaluate trusted platform.

UNIT I: 10 Hours

Introduction to E-Commerce: Network and E-Commerce, Types of E-Commerce.

Ecommerce Business Models: B2C, B2B, C2C, P2P and M-commerce business models.

Ecommerce Payment systems: Types of payment system, Credit card E-Commerce

transactions, B2C E-Commerce Digital payment systems, B2B payment system.

UNIT II: 09 Hours

Security and Encryption: E-Commerce Security Environment, Security threats in

Ecommerce environment, Policies, Procedures and Laws.

UNIT III - 10 Hours Inter-organizational trust in E-Commerce: Need, Trading partner trust, Perceived

benefits and risks of E-Commerce, Technology trust mechanism in E-Commerce,

Perspectives of organizational, economic and political theories of inter-organizational

trust, Conceptual model of inter-organizational trust in E-Commerce participation.

UNIT IV - 10 Hours

Introduction to trusted computing platform: Overview, Usage Scenarios, Key

components of trusted platform, Trust mechanisms in a trusted platform.

UNIT V - 09 Hours

Trusted platforms for organizations and individuals: Trust models and the E-Commerce

domain.

UNIT VI -

Recent trends in Trust, Trust management, Business to Business relations.

WT50

REFERENCES

1. Kenneth C. Laudon and Carol Guercio Trave, Study Guide to E-Commerce Business

Technology Society, Pearson Education, 2005.

2. Pauline Ratnasingam, Inter-Organizational Trust for Business-to-Business E-

Commerce,IRM Press, 2005.

3. Siani Pearson, et al, Trusted Computing Platforms: TCPA Technology in Context,

Prentice Hall PTR, 2002.

COURSE OUTCOMES

On completion of the course, the students should be able to:

CO1:Explain the types of E-Commerce, E-Commerce business models and E-commerce

payment systems.

CO2:Illustrate the Policies, Procedures and Laws and Security threats in E-Commerce

environment.

CO3:Analysis issues, risks and challenges in inter-organisational trust in ECommerce

CO4:Explain the Key components and Trust mechanisms of trusted computing platform.

C05:Describe the trusted platform for organizations and individuals.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.


PO1 PO2 PO3

CO1 1 2

CO2 1 3

CO3 3 1 3

CO4 2 1 3

CO5 2 1 3


WT51

Course Code 18SE2E2A M. Tech (Web Technologies)


Course title SOFTWARE AGENTS

Scheme and

Credits


L T P S Credits

4 0 - - 4



COURSE OBJECTIVES


1. Have an overview of the agent systems and software agents.

2. Understand the basic concepts of intelligent software agents.

3. Explore the use of software agents

4. Analyse and share information to coordinate activities of the agents for the

purpose of group problem solving.

5. Design recurred systems using agents

UNIT - I INTRODUCTION TO AGENTS: 9 hours

Introduction to software agent, Applivations, uses and classification of software agent;

Agent Programming Paradigms, Agent Vs Object, Aglet, Mobile Agents, Agent

Frameworks, Agent Reasoning.

UNIT - II JAVA AGENTS: 9 hours

Processes, Threads, Daemons, Components, Java Beans, ActiveX, Sockets, RPCs,

Distributed Computing, Aglets Programming, Jini Architecture, Actors and Agents, Typed

and proactive messages.

UNIT – III MULTIAGENT SYSTEMS: 10 hours

Interaction between agents, Reactive Agents, Cognitive Agents, Interaction protocols,

Agent oordination, Agent negotiation, Agent Cooperation, Agent Organization, Self-

Interested agents in Electronic Commerce Applications.

UNIT- IV INTELLIGENT SOFTWARE AGENTS: 10 hours

Interface Agents, Agent Communication Languages, Agent Knowledge Representation,

Agent Adaptability, Belief Desire Intension, Mobile Agent Applications.

UNIT- V AGENTS AND SECURITY: 10 hours

Agent Security Issues, Mobile Agents Security, Protecting Agents against Malicious Hosts,

Untrusted Agent, Black Box Security, Authentication for agents, Security issues for

Aglets.


units

WT52

COURSE OUTCOMES


CO1: Interpret the basics of agents

CO2: Create / develop an agent based system for a particular task.

CO3: Design an application that uses different security issues for intelligent agents.

CO4: Effectively apply agent-based technologies in distributed systems

CO5:Validate the application of distributed information systems that use software agents.

REFERENCES

1. Bradshaw, " Software Agents ", MIT Press, 2010

2. Russel, Norvig, "Artificial Intelligence: A Modern Approach", Second Edition, Pearson

Education, 2003

3. Richard Murch, Tony Johnson, "Intelligent Software Agents", Prentice Hall, 2000

4. Gerhard Weiss, Multi Agent Systems, A Modern Approach to Distributed Artificial

Intelligence, MIT Press, 2000.

5. Bigus&Bigus, " Constructing Intelligent agents with Java ", Wiley, 1997


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 1 2

CO3 2

CO4 1 2 2

CO5 2


WT53

Course Code 18SE2E2B M. Tech (Web Technologies)


Course title SOFTWARE SECURITY

Scheme and

Credits


L T P S Credits

4 0 - - 4



COURSE OBJECTIVES

The course will enable the students to

1. Understand the basics of secure programming.

2. Describe most frequent programming errors leading to software vulnerabilities.

3. Analyze security problems in software.

4. Evaluate security threats and software vulnerabilities.

5. Effectively design secure software system.

UNIT -I INTRODUCTION TO SECURITY: 9 Hours

Introduction to Security: Need for security, Security approaches, Principles of security,



Symmetric & Asymmetric Key Cryptography: DES,RSA.

UNIT -II INTRODUCTION TO SOFTWARE SECURITY: 10 Hours Managing software security risk, Selecting software development technologies, An open

source and closed source, Guiding principles for software security, Auditing software,

Buffet overflows, Access control, Race conditions, Input validation, Password

authentication

UNIT-III SECURE RISK MANAGEMENT: 9 Hours Anti-tampering, Protecting against denial of service attack, Copy protection schemes,

Client-side security, Database security, Applied cryptography, Randomness and

determinism

UNIT- IV SECURITY TESTING: 10 Hours Buffer Overrun, Format String Problems, Integer Overflow, and Software Security

Fundamentals SQL Injection, Command Injection, Failure to Handle Errors, and Security

Touchpoints

UNIT- V ADVANCED SOFTWARE SECURITY 10 Hours Cross Site Scripting, Magic URLs, Weak Passwords, Failing to Protect Data, Weak

random numbers, improper use of cryptography Information Leakage, Race Conditions,

Poor usability, Failing to protect network traffic, improper use of PKI, trusting networ

k name resolution


units

REFERENCES 1. J. Viega, G. McGraw. Building Secure Software, Addison Wesley -2011

2. Theodor Richardson, Charles N Thies, Secure Software Design, Jones & Bartlett-

2012

3. Kenneth R. van Wyk, Mark G. Graff, Dan S. Peters, Diana L. Burley, Enterprise

Software Security, Addison Wesley. -2010

WT54

COURSE OUTCOMES At the end the student will be able to

CO1: Identify various risk in the softwares.

CO2: illustrate security problems in the open source software.

CO3: Relate security and software engineering.

CO4: Assess real-time software and its vulnerabilities

CO5: Investigate security flaws in software


CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3

CO2 2 2

CO3 2

CO4 3

CO5 3


WT55

Course Code 18CS2E2C M. Tech (Web Technologies)



Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



COURSE OBJECTIVES










Core Defense Mechanisms: Handling User Access Authentication, Session

Management, Access Control, Handling User Input, Varieties of Input Approaches to

Input Handling, Boundary Validation.



UNIT II: WEB APPLICATION TECHNOLOGIES 09 Hours

The HTTP Protocol, HTTP Requests, HTTP Responses, HTTP Methods, URLs, REST,





UNIT III: MAPPING THE APPLICATION 10 Hours

Enumerating Content and Functionality, Web Spidering, User-Directed Spidering,



Identifying Server-Side Technologies, Identifying Server-Side Functionality, Mapping

the Attack Surface.






Usernames, Predictable Usernames, Predictable Initial Passwords, Insecure Distribution

of Credentials. Attacking Access Controls.

WT56



Basic Vulnerability Injecting into Different Statement Types, Finding SQL Injection

Bugs, Fingerprinting the Database, The UNION Operator, Extracting Useful Data,

Extracting Data with UNION, Bypassing Filters, Second-Order SQL Injection, Advanced

Exploitation Beyond SQL Injection: Escalating the Database Attack, Using SQL

Exploitation Tools, SQL Syntax and Error Reference, Preventing SQL Injection.

UNIT VI


REFERENCES

1. Defydd Stuttard, Marcus Pinto , The Web Application Hacker's Handbook: Finding

And Exploiting Security, Wiley Publishing, Second Edition.



1st Edition





COURSE OUTCOMES


CO1:Achieve Knowledge of web application‘s vulnerability and malicious attacks.






CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks




50 marks.

WT57


PO1 PO2 PO3

CO1 1 3

CO2 2 1 3

CO3 1 3

CO4 3 1 3

CO5 1 3


WT58

Course Code 18CS2L01 M. Tech (Web Technologies)

Category Laboratory


LAB

Scheme and

Credits


L T P S Credits

0 0 4 0 4

CIE Marks:

50


100




2. Java Programming










following topics:


2. AVL Tree














COURSE OUTCOMES





techniques.


WT59


Continuous Internal


Marks)




the Lab every week

20 Write up 10

Test at the end of the semester 20 Experiment 70


Total 100





PO1 PO2 PO3

CO1 2 2

CO2 2

CO3 2

CO4 2

CO5 2


WT60





L T P SS Credits

2 0 - - 1



COURSE OBJECTIVES












Pedagogical practices are being used by teachers in formal and informal classrooms in developing



Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage: quality




and pedagogical approaches. Teachers‘ attitudes and beliefs and Pedagogic strategies.


Professional development: alignment with classroom practices and follow-up support Peer support




Research design Contexts Pedagogy Teacher education Curriculum and assessment Dissemination



REFERENCES


(2): 245-261.





WT61








COURSE OUTCOMES











CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50


PO1 PO2 PO3

CO1 3

CO2 3

CO3 3

CO4 3

CO5 3


WT62



1. Carry out the literature review of general research area/current topic and analyse

the same effectively.




presentation. Interact with peers to acquire the qualities of thoughtfulness,

friendliness, adaptability, responsiveness, and politeness in-group discussion.


GUIDE LINES






COURSE OUTCOMES:





Scheme and Credits

No. of Hours/Week


0 0 2 0 1



WT63



3. Make an effective presentation with seamless flow of content within the time

allocated. Overcome inhibition in interacting with peers and hence develop the spirit

of team work. Overcome stage fear during the presentation.


CIE – 50

marks


Marks =20

Total:50



PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2










Report : 25%

SEMESTER III

WT64




Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES







Introduction to Web - Limitations of current Web – Development of Semantic Web –





UNIT- II MODELING AND VISUALIZATION: 10 Hours

Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph





UNIT- III SOCIAL NETWORK ANALYSIS TECHNIQUES: 10 Hours

Framework - Tracing Smoothly Evolving Communities - Models and Algorithms for












UNIT- V TEXT AND OPINION MINING: 10 Hours

Text Mining in Social Networks -Opinion extraction – Sentiment classification and


WT65


topics over time


units

REFERENCES

1. Charu C. Aggarwal, ―Social Network Data Analytics‖, Springer; 2011

2. Peter Mika, ―Social Networks and the Semantic Web‖, Springer, 1st edition, 2007.

3. Borko Furht, ―Handbook of Social Network Technologies and Applications‖,


4. Guandong Xu , Yanchun Zhang and Lin Li, ―Web Mining and Social Networking –

Techniques and applications‖, Springer, 1st edition, 2011.

5. Giles, Mark Smith, John Yen, ―Advances in Social Network Mining and Analysis‖,

Springer, 2010.

6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, ―Computational Social

Network Analysis: Trends, Tools and Research Advances‖, Springer, 2009.

7. Toby Segaran, ―Programming Collective Intelligence‖, O‘Reilly, 2012

COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2 3

CO2 2

CO3 1 3

CO4 1 3

CO5 1 1 3


WT66

Course Code 18CS3E1B M. Tech (Web Technologies)



Scheme and

Credits


L T P S Credits

3 - 2 - 4

CIE Marks:

50


100










analytics.


data, web analytics, big data and marketing, fraud and big data, risk and big data, credit risk

management, big data and algorithmic trading, big data and healthcare, big data in medicine,

advertising and big data, big data technologies, introduction to Hadoop, open source

technologies, cloud and big data, mobile business intelligence, Crowd sourcing analytics,

inter and trans firewall analytics.








design of Hadoop distributed file system (HDFS), HDFS concepts, Java interface, data flow,

Hadoop I/O, data integrity, compression, serialization, Avro, file-based data structures


MapReduce job run, classic Map-reduce, YARN, failures in classic Map-reduce and YARN,

job scheduling, shuffle and sort, task execution, MapReduce types, input formats, output

formats.


Hbase, data model and implementations, Hbase clients, Hbase examples, praxis. Cassandra,

Cassandra data model, Cassandra examples, Cassandra clients, Hadoop integration, Pig,

Grunt, pig data model, Pig Latin, developing and testing Pig Latin scripts. Hive, data types

and file formats, HiveQL data definition, HiveQL data manipulation, HiveQL queries.

UNIT VI -

Recent advances in Big data analytics

WT67

UNIT - VII (Lab Programs)







o Retrieving files

o Deleting files




(b) Performing a MapReduce Job for word search count (look for specific keywords in a

file)


Input:



Output:





good candidate for analysis with MapReduce, since it is semi structured and record-oriented.



(b) Filter the readings of a set based on value of the measurement, Output the line of

input files associated with a temperature value greater than 30.0 and store it in a

separate file.





Toys

Consumer Electronics



Reno

Toledo

Chandler


7. Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter your

data.






Scala.

WT68

REFERENCES

1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:

Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,

2013.


World of Polyglot Persistence", Addison-Wesley Professional, 2012.







COURSE OUTCOMES







analytics.

Scheme of Examination:

CIE -

Practical



Total: Marks

50


Marks(50##

)

CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks







WT69


PO1 PO2 PO3

CO1 3 1

CO2 2

CO3 3 2

CO4 1 2

CO5 3


WT70

Course Code 18IT3E1C M. Tech (Web Technologies)


Course title INFORMATION RETRIEVAL SYSTEMS

Scheme and

Credits


L T P S Credits

4 0 - - 4

CIE Marks:

50


100



Course Objectives


1. Understand the taxonomy and models of Information retrieval system.

2. Discuss the retrieval evaluation methods.

3. Acquire learning techniques for text classification and clustering.

4. Design the search engine

5. Experiment web content structure searching in search engine.

UNIT I-INTRODUCTION 10 Hours Motivation, Basic concepts, Past, present, and future, The retrieval process. Modelling:

Introduction, A taxonomy of information retrieval models, Retrieval: Adhoc and filtering, A

formal characterization of IR models, Classic information retrieval, Alternative set theoretic

models, Alternative algebraic models, Alternative probabilistic models, Structured text

retrieval models, Models for browsing.

UNIT II- RETRIEVAL EVALUATION 10 Hours Introduction, Retrieval performance evaluation, Reference collections. Query

Languages: Introduction, keyword-based querying, Pattern matching, Structural

queries, Query protocols. Query Operations: Introduction, User relevance feedback,

Automatic local analysis, Automatic global analysis.

UNIT III - TEXT AND MULTIMEDIA LANGUAGES AND PROPERTIES 09 Hours

Introduction, Metadata, Text, Markup languages, Multimedia. Text Operations:

Introduction, Document pre-processing, Document clustering, Text compression,

Comparing text compression techniques

UNIT IV – USER INTERFACES AND VISUALIZATION 10 Hours Introduction, Human-Computer interaction, The information access process, Starting

pints, Query specification, Context, Using relevance judgments, Interface support for

the search process. Searching the Web: Introduction, Challenges, Characterizing the

web, Search engines, Browsing, Meta searchers, Finding the needle in the haystack,

Searching using hyperlinks.

UNIT V - Indexing and Searching 09 Hours Introduction; Inverted Files; Other indices for text;Boolean queries; Sequential searching;

Pattern matching; Structural queries;Compression. Parallel and Distributed IR: Introduction,

Parallel IR, Distributed IR.

UNIT VI -–

Recent trends in information retrieval systems

WT71

REFERENCES

1. Ricardo Baeza-Yates, Berthier Ribeiro-Neto: Modern Information Retrieval, Pearson,

1999.

2. David A. Grossman, Ophir Frieder: Information Retrieval Algorithms and Heuristics, 2nd

Edition, Springer, 2004

COURSE OUTCOMES


CO1: Summarize taxonomy and models of information retrieval system.

CO2: Design the various components of an information retrieval system

CO3: Design text classification and clustering applying machine learning technique.

CO4: Demonstrate the functions of search engine.

CO5: Analyse web content structure for efficient information retrieval.


CIE –

50

marks


marks

Unit-VI = 15 marks

Total:50


SEE

– 100

marks




marks

(c) Units which have 10 Hours shall

have internal choice 20*3= 60 Marks


marks.


PO1 PO2 PO3

CO1 2

CO2 3

CO3 2

CO4 3

CO5 2 3


WT72

Course Code 18CS3P1A M. Tech (Web Technologies)



Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES












UNIT-II KNOWLEDGE REPRESENTATION ISSUES: 9 Hours








UNIT-IV REASONING I: 10 Hours







units

REFERENCES:








WT73


Press, 2009.


COURSE OUTCOMES








CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 2

CO2 2

CO3 2

CO4 2

CO5 2 2


WT74

Course Code 18CS3P1B M. Tech (Web Technologies)



Scheme and

Credits


L T P S Credits

4 - - - 4



COURSE OBJECTIVES





organization.




UNIT -I BUSINESS ANALYTICS: 10 Hours





overview

UNIT -II TRENDINESS AND REGRESSION ANALYSIS: 9 Hours





10 Hours






UNIT -IV FORECASTING TECHNIQUES: 10 Hours







WT75





units

REFERENCES




COURSE OUTCOMES










CIE -50

Marks



Quiz/AAT=05 Marks


Total: Marks

50

SEE-

100

Marks


choice

20*3=60

Marks

Total:

Marks 100


internal choice

20*2=40

Marks


for 50 marks


PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3

CO4 3 3

CO5 3 3


WT76

Course Code 18CS3P1C M. Tech (Web Technologies)


Course title SYSTEM SIMULATION AND MODELING

Scheme and

Credits


L T P S Credits

3 1 - - 4



COURSE OBJECTIVES:



techniques






























WT77

REFERENCES


2005.







COURSE OUTCOMES










CIE –

50

marks


= 10 marks

Total:50


SEE

– 100

marks




(d) Units which have 10 Hours shall

have internal choice 20*3= 60 Marks


marks.


PO1 PO2 PO3

CO1 2

CO2 3

CO3 3

CO4 3

CO5 3 2


WT78




same effectively.







GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50





Scheme and Credits

No. of Hours/Week


0 0 2 0 1



WT79


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2










Report : 25%

WT80

INTERNSHIP





2. Pay close attention to all the steps that go onto completing a job, thereby, help students

to become workforce ready before entering the job market as a graduate.Provide an

opportunity to select the topic of dissertation work by evaluating the requirement of

organisation.


GUIDELINES

1. Student has to approach the concerned heads of various Industries/organization, which

are related to the field of specialization of the M. Tech program.

2. If any student gets internship, he/she has to submit the internship offer letter duly signed

by the concerned authority of the company to the Chairperson of the Department.

3. The internship on full time basis will be after the examination of II semester and during

III semester for a period of 8 weeks without affects regular class work.

4. The progress has to be reported periodically to the faculty or to the Guide assigned by

the Chairperson as per the format acceptable to the respective industry /organizations

and to the Institution.


6. Students are advised to use ICT tools such as Skype to report their progress and

submission of periodic progress reports to the faculty in charge or guide.

7. Duly signed report from internal supervisor (faculty incharge or guide) and external

supervisor from the organization where internship is offered has to be submitted to the

Chairperson of the Department for his/her signature and further processing for

evaluation.

The broad format of the internship final report shall contain Cover Page, Certificate from

College, Certificate from Industry / Organization of internship, Acknowledgement,

Synopsis, Table of Contents, chapters of Profile of the Organization - Organizational

structure, Products, Services, Business Partners, Financials, Manpower, Societal Concerns,

Professional Practices, Activities of the Department where internship is done, Tasks

Performed and summary of the tasks performed. specific technical and soft skills that

Course Code 18WT3I01 M. Tech (Web Technologies)



Scheme and Credits

No. of Hours/Week


--- --- 10 --- 5


batch of 6 students


WT81

student has acquired during internship, References & Annexure.

COURSE OUTCOMES:



problems what

engineers ready do.

2. Get equipped with experience required before entering the job market.Explore the

possibility of formulating the dissertation problem.





50*

Marks



50**

Marks


marks.


marks.


PO1 PO2 PO3

CO1 3 2

CO2 2 2

CO3 3


Rubrics for CIE:




4. Document = 40%


WT82

Rubrics for SEE:




4. Document = 40%


MINI PROJECT













COURSE OUTCOMES:









50*

Marks



50**

Marks


marks.


marks.

WT83

Rubrics for CIE


marks.

Rubrics for SEE:

The SEE shall be done by two examiners out of which one examiner is the guide of mini

project. The following weightage would be given for the examination. Evaluation shall be done

in batches, not exceeding 6 students.

Sl.

no


marks of

SEE


50**



Discussion

30% 30

4 Report 25% 25

5 Viva Voce 20% 20

Total 100% 100

Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for

50 marks.


PO1 PO2 PO3

CO1 3 3

CO2 2 3

CO3 3


Sl.

no


marks of

CIE


50*



25% 25




Total 100% 100

WT84


1. Choose a problem applying relevant knowledge and skills acquired during the course.

Formulate the specifications of the project work, identify the set of feasible solutions,

prepare, and execute project plan considering professional, cultural and societal factors.

Identify the problem-solving methodology using literature survey and present the same.


experiments to Evaluate and critically examine the outcomes followed by concluding the

results and identifying relevant applications. Preparation of synopsis, preliminary report

for approval of topic selected along with literature survey, objectives and methodology.


GUIDELINES

The Dissertation work will start in III semester and should be a problem with research

potential and should involve scientific research, design, generation/collection and analysis

of data, determining solution and must preferably bring out the individual contribution.

The Dissertation work will have to be done by only one student and the topic of

dissertation must be decided by the guide and the student. The dissertation work shall be

carried out, on-campus or in an industry or in an organisation with prior approval from

the Chairperson of the Department. The student has to be in regular contact with the guide

atleast once in a week.



Nomenclature, Chapters of Introduction including motivation to choose topic, Literature

survey, Conclusion of literature survey, Objectives and Scope of Dissertation,

Methodology to be followed, Experimental requirements, References and Annexure.



COURSE OUTCOME:



National/International reference journals, articles and contact resource persons for

selected topics of Dissertation.


Course Code 18WT3D01 M. Tech (Web Technologies)



Scheme and Credits

No. of Hours/Week


0 0 10 0 5



WT85

3. Present and defend the contents of Dissertation work phase I in front of technically

qualified audience effectively.




50*

Marks


examiner) and external examiner

50**

Marks


marks.

**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for

50 marks.







Rubrics for SEE:







PO1 PO2 PO3

CO1 3 3

CO2 3 3

CO3 3 3


SEMESTER IV

WT86




effectively.






GUIDE LINES






COURSE OUTCOMES:








CIE – 50

marks


Marks =20

Total:50





Scheme and Credits

No. of Hours/Week


0 0 2 0 1



WT87


Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall comprise

of Chairman of the Department, Faculty/Guide and one more faculty member nominated by Chairman. The






Report : 25%


PO1 PO2 PO3

CO1 2 2 3

CO2 2 3 3

CO3 2


WT88


1. Apply/Use different experimental techniques, equipments, software/ Computational/

Analytical /Modelling and Simulation tools required for conducting tests and generate other

relevant data. Students will also be able to design and develop an experimental setup/test rig.


3. Create a detailed technical document as per format based on the outcome of dissertation

work phase I and II.

GUIDELINES

Dissertation work phase II is the continuation of project work started in III semester. The

report of Dissertation work that includes the details of Dissertation work phase I and

phase II should be presented in a standard format. The candidate shall prepare a detailed

report of dissertation that includes Cover Paper, Certificate from

College/Industry/Organisation, Acknowledgement, Abstract, Table of contents, List of

Figures and Table, Nomenclature, Chapter of Introduction, Literature survey, Conclusion

of literature survey, Objectives and Scope of dissertation work, Methodology,

Experimentation, Results, Discussion, Conclusion, Scope for future work, References,

Annexure and full text of the publication (submitted or published)

COURSE OUTCOMES:


1. Conduct experiments/ implement the capabilities of different Software

/Computational / Analytical/

Modelling and simulation tools individually and generate data for validation of

hypothesis.

2. Investigate and assess the results obtained within the scope of experiments conducted

followed by conclusions.

3. Prepare a detailed technical document, Present and defend the contents of Dissertation

work in presence of technically qualified audience effectively.

Course Code 18WT4D01 M. Tech (Web Technologies)



Scheme and Credits

No. of Hours/Week


--- --- 30 --- 15

CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE:


WT89


CIE




100

marks

50*

marks

SEE


2. Dissertation evaluation by external examiner = 100 marks



300

marks

50**

marks


marks.

** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for

50 marks.

Rubrics for CIE:






Rubrics for SEE:









PO1 PO2 PO3

CO1 3 2 3

CO2 2 2 3

CO3 3 3 3
