s. jyothi · d. m. mamatha · suresh chandra satapathy · k

Learning and Analytics in Intelligent Systems 16

S. Jyothi · D. M. Mamatha · Suresh Chandra Satapathy · K. Srujan Raju · Margarita N. Favorskaya Editors

Advances in Computational and Bio-EngineeringProceeding of the International Conference on Computational and Bio Engineering, 2019, Volume 2

Learning and Analytics in Intelligent Systems

Volume 16

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S. Jyothi • D. M. Mamatha •

Suresh Chandra Satapathy •

K. Srujan Raju • Margarita N. FavorskayaEditors

Advances in Computationaland Bio-EngineeringProceeding of the International Conferenceon Computational and Bio Engineering, 2019,Volume 2

123

EditorsS. JyothiDepartment of Computer ScienceSri Padmavati Mahila VisvavidyalayamTirupati, Andhra Pradesh, India

D. M. MamathaDepartment of BioScience and SericultureSri Padmavati Mahila Visvavidyalayam(Women’s University)Tirupati, Andhra Pradesh, India

Suresh Chandra SatapathySchool of Computer EngineeringKIIT Deemed to be UniversityBhubaneswar, Odisha, India

K. Srujan RajuDepartment of Computer Science andEngineeringCMR Technical CampusHyderabad, Telangana, IndiaMargarita N. Favorskaya

Department of Informaticsand Computer TechniquesSiberian State Aerospace UniversityKrasnoyarskiy, Russia

ISSN 2662-3447 ISSN 2662-3455 (electronic)Learning and Analytics in Intelligent SystemsISBN 978-3-030-46942-9 ISBN 978-3-030-46943-6 (eBook)https://doi.org/10.1007/978-3-030-46943-6

© Springer Nature Switzerland AG 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

https://doi.org/10.1007/978-3-030-46943-6

Contents

Cloud Computing: A Study on Type of Data Stored in a Cloudand Its Security Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1V. Sireesha and M. Usha Rani

Smart Bag Using Electromagnet Zipper . . . . . . . . . . . . . . . . . . . . . . . . . 13M. Goutham Kumar, M. Suma, K. Kishore Reddy, and D. Ajitha

Analysis on Various Feature Extraction Methods for MedicalImage Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19S. Vani Kumari and K. Usha Rani

Prediction of Pest Generations Based on Future ClimateUsing Big Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33A. Swarupa Rani and S. Jyothi

Optimizing TCP Congestion Control Techniques for WirelessNetwork Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47K. Vasudha Rani and B. Kavitha

Comparative in Silico Studies for the Molecular Basis of LepidopteranInsect Pests Bio-Control Using Insect’s Own Enzymes . . . . . . . . . . . . . . 55V. Amardev Rajesh, D. M. Mamatha, and M. Bhaskar

Collaborative Cloud Computing for Resource Sharing Platformin Multiple Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65K. Saritha and B. Manorama Devi

Deep Learning of Paradigms: The Outlook . . . . . . . . . . . . . . . . . . . . . . 71Allu Jhansi, K. Lavanya, and Kavarakuntla Tulasi

Applications of Network Analysis in Bioinformatics . . . . . . . . . . . . . . . . 79P. Naga Deepthi and Raju Anitha

v

Identification of Clinical Variants Present in Skin MelanomaUsing Exome Sequencing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85Shivaleela Biradar, K. M. Kiran Kumar, M. Naveen Kumar,and R. L. Babu

A Cloud-Based Privacy Preserving e-Healthcare SystemUsing Whale Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97P. Ruthvik Reddy, G. Sri Sai Nikhil, K. C. Sreedhar, Meeravali Shaik,and M. Swathi

Short Term Price Forecasting of Horticultural Crops Using LongShort Term Memory Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . 111N. Prakash and G. Sumaiya Farzana

Computational Wear Analysis of Acetabular Cup for Various DailyActivities with Different Biomaterials . . . . . . . . . . . . . . . . . . . . . . . . . . . 119Lokeswar Patnaik, Saikat Ranjan Maity, and Sunil Kumar

Intelligent Data Mining for Collaborative Information Seeking . . . . . . . 129Abhinav Kumar, K. Chandrasekaran, Anupam Shukla, and D. Usha

Career Recommendation for the Undergraduate StudentsUsing Content Based Filtering Method . . . . . . . . . . . . . . . . . . . . . . . . . 137R. Santhosh Kumar and N. Prakash

LDA Based Approach for Topic Description from SpokenAudio Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145Ramesh M. Kagalkar, Shashank Simpi, Suyodh Kittur, Vishrut Nayak,and Kashinath

Internet Traffic Prediction in SDN Using RF and XGB . . . . . . . . . . . . . 153Sarika Nyaramneni, M. A. Saifulla, and Sankalp Singh Mehra

Development of Multigrain Flour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161D. Mounika and G. Sireesha

Artificial Intelligence and the Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169T. Sita Kumari

Review on Regulation of Heat Shock Protein 70 During Radiotherapyin Cancer Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175B. Sai Lalitha, M. Malini, M. Venkateswar Rao, and E. Mounika Sravani

Cognitive Agriculture—Novel Approach for Sustainability . . . . . . . . . . 183T. Manjula and T. Sudha

A Modified Algorithm for Power System State Estimation . . . . . . . . . . 189M. S. N. G. Sarada Devi and G. Yesuratnam

vi Contents

Noise Reduction Using OBNLM Filter and Deep Learning forPolycystic Ovary Syndrome Ultrasound Images . . . . . . . . . . . . . . . . . . . 203G. Vasavi and S. Jyothi

Plant Disease Detection Using Machine Learning Algorithms . . . . . . . . 213P. Prathusha, K. E. Srinivasa Murthy, and K. Srinivas

Identification of Roads from CARTOSAT-2 Satellite Images UsingLine Segment Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221Sravya Madala, K. Praveen Kumar, K. Suvarna Vani, and Kilari Rampriya

Lung Cancer Detection Using CT Scan Image . . . . . . . . . . . . . . . . . . . . 233Pranathi Jalapally, K. Suvarna Vani, K. Praveen Kumar,and Jahnavi Koduru

Efficiency of Neuro-Nutrient Therapy in the Treatmentof Neuropathologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243Swaroopa Maralla and D. Bharathi

Computational Approaches in Toxicity Testing: An Overview . . . . . . . . 255S. Nithya, M. Lalasa, K. Nagalakshmamma, and S. Archana

Application of Big Data in Forecasting the Travel Behaviourof International Tourists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263N. Padmaja, T. Sudha, and Sirpurkar Srinivas Saurab

Mapping of Disease Names to Standard Codes for Effective EHRSystem in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273Prashant Kanade and Arun Kumar

Text Mining to Understand Major Keywords Explaining Sentimentsof Travelers Using Travel Related Online Services in India . . . . . . . . . . 287Dashrath Mane and Prateek Srivastava

Hot Topic Extraction from News Websites . . . . . . . . . . . . . . . . . . . . . . 297J. Katyayani

Analysing Ground Water Quality in the Regions of Kadapa DistrictUsing Supervised Learning Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 305S. V. S. Ganga Devi

Computational Model for Integrating Sentimental Analysisin Tourism Information System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315M. Chandini and J. Katyayani

Classification of Brain Tumor in Mri Images Using Features . . . . . . . . 323Neelam Sobha Rani and N. V. Muthu Lakshmi

Removal of Heavy Metals from Industrial WastewatersUsing Low-Cost Adsorbents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331Ajitha Priya N. Jammala

Contents vii

Microbial Degradation of Fenitrothion (an Insecticide) Foundin Agriculture Soils—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339Suvarnalatha Devi Potireddy, Sudha Rani Thenepalli,Swetha Tejaswi Thumma, Rajaswi Devi Mandadi,and Ranjani Ramakrishnan

Pursuance of DWIRS Framework for Deep Web Explorationwith Indexing and Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347S. Suneetha and M. Usha Rani

Emerging Trends in Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . 357S. Madhuri Paradesi

Improvement of Serum Vitamin-A Levels Through NutritionEducation in Adolescent Girls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367N. Rajani and G. Sireesha

Performance of Rake Receiver for Maximal Codes . . . . . . . . . . . . . . . . 375M. Dileep Reddy and G. Sreenivasulu

Seeds Viability and Germination Variation Among the Populationof Withania somnifera (Ashwagandha). (L) Dunal . . . . . . . . . . . . . . . . . 383Neha Singh and Anita R. J. Singh

Application of Artificial Intelligence in Management InformationSystems—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395Balaji Venkatachalam and B. Bharathi

Geometric Feature Extraction for Detecting Carcinoma in ThreeDimensional MR Images Through Machine Learning Algorithms . . . . . 405F. Amul Mary and S. Jyothi

Smart Garbage Monitoring System Using IoT . . . . . . . . . . . . . . . . . . . . 421V. Vijeya Kaveri, V. Meenakshi, B. Bharathi, and J. Albert Mayan

Effects of Buoyancy, Activation Energy on the Stagnation Point Flowof a Chemically Reactive Magneto Radiative Non-NewtonianNanofluid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429K. Malleswari, K. Sreelakshmi, and G. Sarojamma

Law and Regulation of Artificial Intelligence in India—Advantagesand Pitfalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Sunitha Kanipakam

Enabling Accuracy Finding of Data Storage in Hybrid Cloud andRestricted Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447Gollapalli Sumana

viii Contents

Effect of Edible Oil on AMP-Activated Protein Kinase Pathwayin Rabbit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463Shaik Roqhayya, Lavanya Yaidikar, and Syed Rahamathulla

Prediction of NOX Concentration in the Vicinity of Cement IndustryEmploying AERMOD Dispersion Modeling . . . . . . . . . . . . . . . . . . . . . . 473S. Anand Kumar Varma, K. R. Manjula, and Jayato Nayak

Location Based Sentiment Analysis Using PC3E—PNN ConsensusBetween Classification and Clustering Ensembles—Technique in BigData Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489S. Pradeepa and K. R. Manjula

Contents ix

Cloud Computing: A Study on Typeof Data Stored in a Cloud and ItsSecurity Mechanisms

V. Sireesha and M. Usha Rani

Abstract Cloud computing is an “Internet-based computing”. It provides manyresources to Cloud Users based on demand without buying any infrastructure bymaking use of CSP (Cloud Service Providers) and the amount is charged based onthe usage of data. It made the day to day functions easier “in evolving and makingIT utilization for the consumers. The cloud is also used as” a channel to design anddeploy user applications including its storage space and database “without worryingabout the operating system used. An application can also run without considera-tion the infrastructure that is used. Also, the Cloud provides huge storage which isavailable for both data and databases. The storage of data on the Cloud is one ofthe main activities in Cloud computing which utilizes infrastructure spread acrossseveral geographical locations.” This paper gives a study about various type of datastored in a cloud and its security mechanisms.

Keywords Cloud computing · Scaling · Data security · Encryption

1 Introduction

“Cloud computing is that the delivery of computing services over the Internet. Thismodel permits access to computer resources and information that a network connec-tion is available” from anyplace. Cloud services allow individuals and businesses toutilize system software and hardware that are “managed by third parties access atremote locations”. “Examples of cloud services: webmail, online file storage, onlinebusiness applications and social networking sites.”

V. Sireesha (B)Research Scholar, Department of Computer Science, SPMVV, Tirupati, Indiae-mail: [email protected]

M. Usha RaniProfessor, Department of Computer Science, SPMVV, Tirupati, Indiae-mail: [email protected]

© Springer Nature Switzerland AG 2020S. Jyothi et al. (eds.), Advances in Computational and Bio-Engineering,Learning and Analytics in Intelligent Systems 16,https://doi.org/10.1007/978-3-030-46943-6_1

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2 V. Sireesha and M. Usha Rani

“Cloud computing makes available a shared pool of resources, together withdata storage space, networks, computer processing power, and user applications andspecialized corporate company”.

“Cloud computing is used as a model for usage in more convenient, on-demandnetwork services access to a shared pool of configurable computing resources likeservers, storage, applications, networks, and services which can be quickly used andreleased with less management effort or interaction between service providers.” Asa result of these benefits each and every organization are transmitting their data tothe cloud.

“Different types of data are stored in the cloud”. Therefore, there is a necessity toprotect that data against unauthorized access from anywhere, modification or denialof services, etc. The Cloud means that to secure storage (the Cloud provider hosteddatabases) and the treatments (calculations). Three important points are namely tosecure data. “They are Availability, Confidentiality, and Integrity.”

2 Type of “Data Stored in a Cloud”

We can store everything “in the cloud which we see and access on the Internet andcomputers like:

• installing business software on clouds.• Storing songs, images, client’s data, videos, backups,websites, content and almost

everything”.

“In Cloud storage, by using the Internet more than one user can store and accesstheir business applications from remote locations because it is a cloud computingservice model.”

“The list of data and information that we can store in the cloud is as follows:

1. We can store MS-Word files to OneDrive or Google drive, later we can accessthose files using mobile, laptop and desktop from anywhere with an internetconnection.”

2. “We can load our files on the cloud storage which we can share those files withour teams and they can edit or make the changes.”

3. “We can host our website or store the database of clients on cloud storage.”4. “We can upload the document that we want to share with freelancer if we hired

someone on remote location. So they can download and upload when work isdone.”

5. “We can upload and store our video files on cloud storage. After that, we canplay those videos on Internet-connected TV.”

6. “We can create daily progress report sheet and then share with our teammatesto update it frequently.”

7. “We can save our money by storing our back up data on the cloud storage whichwe don’t need to buy external hard drive or server.”

Cloud Computing: A Study on Type of Data Stored … 3

8. “We can work on the same page in online with our team with collaboration byusing Microsoft excel and for saving the data.”

9. “We can format our PC by uploading the data to the cloud by making use ofthe free space available on Google Drive, Dropbox, Box and One drive etc. itmeans we upload 50–60 GB for free.”

10. We can store the files on cloud from the pen drive which will save the data fromthe virus, corruption or loss etc., so it’s good to use the cloud storage instead ofcarrying the pen drive and virus infected data.

(a) How to use Cloud Storage?“The word cloud means Internet or networks of the computer. It is used by cloudstorage service providers. So cloud storage is a part of cloud clouding in which datais stored on clouds.”

“To store the data from mobile phones, tablet, computer etc., we can use cloudstorage. In case if our phone memory is full, we can upload the old data to cloudstorage service provider”, and we can use it.

“The idea behind cloud storage is that we can store our data on other computersthat are located in another physical location.” But we can access that from anywhere.

In other way we can say “cloud computing and cloud storage technology is usedto store, manage, access, calculate, manipulate, arrange, share, edits the data andinformation from any device like laptop, desktop, mobile, etc., from any location.”

“The main usage of cloud computing for companies is that they do not need toinstall the server on their own physical location and they do not need to install anysoftware or database management system.”

The common people and SME’s are using the cloud storage and cloud computingfor storing the data instead of carrying “the pen drive and memory card and do notneed to buy more than 1 TB hard disk drive because the data is now stored in thecloud or server or the internet which can be accessed from anywhere at any time.”

(b) Benefits of “cloud storage”The following are the benefits by using cloud storage: By using “cloud storage infuture we do not need to install any software in our pc such as today we install”“windows, MS-Office, Antivirus” and “many other things.” We will pay the amountbased on the usage of data in monthly or yearly basis for everything.

There is no need of “maintenance cost, no need to install and no need to buyany software, no need to update and upgrade antivirus etc.” We can play the songs,videos, movies etc., which we stored on the cloud using computer and mobile, whiledriving cars or trucks. Using cloud storage is “more secure than a hard disk and pendrives. During official work, more than one person can work and access the fileswhich will increase the productivity and speed of the company.”

In coming years, we will store all the data and applications online using “cloudstorage and cloud computing services” only.


3 Cloud Security Issues

“Cloud computing is used to run the services and applications with more robust as itprovidesmore resources in availability. It also allows apps to runwithmore resilienceby providing multiple replicas and “multiple layers of protection. Operating onthe cloud is also relatively more cost-efficient compared to running conventionalservers.”

As we are increasing the “storage of data in the cloud relatively increases theattacks on cloud cluster surfaces. This will make the security an even more importantaspect to focus onwhilewe are deploying to the cloud.Despite the growing awarenessof the importance of cloud security, there are still several common security issueslike”

(a) “App Vulnerabilities”Even if we are running our apps in a complex web of micro services architecture,“the cloud environment can rise a serious security threat to the entire cluster.” “Evenworse, many container-based platforms don’t really manage security out of the box.”

If the attack is caused from the code running inside, then “multiple firewalls,selective port monitoring, and other security measures will not work. During theworkflow development, we have to make sure that sufficient reviews and tests areperformed before new codes are committed and deployed.”

(b) “System Vulnerabilities”If we are using the cloud, we have to take the charge of the cloud environment also.Which means we have to take the “complete control over how the environment is setup and also we need to handle the system security by our self. It is also necessaryto take steps to sufficiently secure hosts, operating systems, and other componentssupporting the cloud environment.”

There are a lot of “tools, external security suites, and a large community of devel-opers helping us to secure our cloud environment at a system level. As more applica-tions are running in the cloud, we can usemore secure systems and security measuresat the system level.”

(c) “Account Hijacking and Unauthorized Access”This is a simple security issue called as “account hijacking. The main reason forintrusion and data breach is accessing the data by unauthorized parties using validuser accounts. This can be caused by “password theft, phishing attacks, and socialengineering.”

“There are no strong and secure password policies for implementing right now. Itmay sound primitive, but the statistics, conducted by Ponemon Institute shows that:

• 51% of survey respondents reuse passwords across the business and personalaccounts

• 2 out of 3 (69%) survey respondents share their passwords with work colleaguesto access accounts

• 67% of survey respondents do not use any form of two-factor authentication intheir personal life and 55% of respondents do not use it at work.”


(d) “Insider Threat”“The attack occurred within the organization or team is an insider threat. It is similarto unauthorized access, access by authorized parties for the purpose of harming thecloud environment which is incredibly dangerous.” “This type of attack is moredifficult to detect, and even more difficult to prevent.”

“If we access the cloud normally in a secure manner, it will not raise any problem.In fact, it is virtually undetectable until it is too late. Fortunately, by using bestinformation security methods and the use of detailed logging tools will effectivelylimit the insider threats.”

(e) Data Breaches and Data LossIf the “volume of the data stored in the cloud is increasing, substantially the attacksurface ofmost cloud environments increases. Unless we take suitable steps to securethe environment from external attacks, a data breach will occur.”

“A script with runtime access can be elevated beyond its intended use which weforget about it that leads to minor holes in the cloud environment. They can turn intoa catastrophic problem when not handled properly.”

“Another risk that we won’t mitigate properly is data loss. Cloud computing isdesigned to be resilient by nature, but that doesn’t mean the risk of data loss iscompletely eliminated. Servers can still fail, hardware can stop working, and ourfiles may be lost.”

This security risk can be managed by a “good backup routine and a disasterrecovery policy to go alongwith it to prevent data loss.We need tomaintain the online(remote) and offline backups of our cloud environment for maximum security.”

4 Cloud Security Mechanisms

Here we discuss a “set of fundamental cloud security mechanisms, which are usedto counter the security threats.”

(a) “Encryption”“This mechanism is a digital coding system dedicated for preserving the confiden-tiality and integrity of data. The encryptionmechanism is used for encoding plaintextdata into an unreadable format in a protected mode. When encryption is applied tothe plaintext data, it is paired with a string of characters which is called as encryptionkey. This key in turn used to decrypt the cipher text back into its original plaintextformat. There are two types of encryption forms.”

(1) “Symmetric key encryption”: it is also known as “secret key cryptographywhichuses the same key for both encryption and decryption”. It consists of the followingalgorithms:

(1.1) “Data Encryption Standard (DES)”: “It is published by the” “NationalInstitute of Standards and Technology (NIST)”. This algorithm uses single key orsecret key for both encryption and decryption. “It operates on 64-bit blocks of data


with 56 bits’ key.” “The round key size is 48 bits. Entire plaintext is divided intoblocks of 64bit size; last block is padded if necessary. Multiple permutations andsubstitutions are used throughout in order to increase the difficulty of performinga cryptanalysis on the cipher.”(1.2) “Advanced Encryption Standard (AES)”: “This is the most adopted sym-metric encryption which operates computation on bytes rather than bits.” It treats“128 bits of plaintext block as 16 bytes. These 16 bytes are arranged in fourcolumns and four rows” for processing as a matrix. “It operates on entire datablock by using substitutions and permutations.”

(2) “Asymmetric key encryption”: “here we use two different keys, one is a privatekey and another is a public key”.

“Rivest-Shamir-Adleman (RSA)”: “This is a public key cipher developed bythree” scientists “Ron Rivest, Adi Shamir and Len Adlemen” in 1977. “It is themost popular asymmetric key algorithm” used for cryptography. We use differentdata block size and various key sizes in this algorithm. It uses asymmetric keys forboth encryption and decryption. “To generate the public and private keys this algo-rithm uses two prime numbers. These two different keys are used for encryption anddecryption.”

(3) “Homomorphic algorithm”: “This is also an encryption algorithm which pro-vides remarkable computation facility over encrypted data and return encryptedresult. This algorithm is used to solve many issues related to security and confi-dentiality issues. It operates upon encrypted data where encryption and decryptiontakes place in client site and provider site. Here the plain text is hidden from theservice provider, where the provider operates upon cipher text only. This encryptionallows complex mathematical operations to be performed on encrypted data withoutusing the original data.”

(b) HashingThis method is used for cloud security from which we can “derive a hashing codeor message digest from a message, which is often of a fixed length and smaller thanthe original message. It is used for protecting one-way, non-reversible form of data.The message will be locked if once hashing is applied and no key is provided for themessage to be unlocked.”

“The hashing mechanism can be applied by the message sender to attach themessage digest to the message. The same hash function is applied to the message bythe receiver, to verify that the produced message digest is identical to the one thataccompanied the message. If any alteration is done to the original message,” thenentire message digest will change and indicates that tampering has occurred (Fig. 1).

(c) “Single Sign-On (SSO)”“In thismechanism, one cloud service consumer is authenticated by a security broker,which in turn establishes a security context that is persisted while the cloud serviceconsumer accesses other cloud services or cloud-based IT resources. Otherwise, thecloud service consumer would need to re-authenticate itself with every subsequentrequest.”


Fig. 1 Hashing security mechanism in cloud

“The single sign-on method essentially enables mutually independent cloudservices and IT resources to generate and circulate runtime authentication andauthorization information” (Fig. 2).

(d) “Digital Signature”The digital signature provides “data authenticity and integrity through authentica-

tion and non-repudiation. Here a message is transmitted after assigning with a digitalsignature, and it is rendered invalid if any unauthorized modifications are made.”

A “digital signature is created by using both the hashing and asymmetrical encryp-tion mechanisms, which is encrypted by a private key that exists as a message digestand is appended to the original message. The receiver verifies the signature valid-ity and uses the corresponding public key to decrypt the digital signature, whichproduces the message digest.”

(e) “Identity and Access Management”The “Identity andAccessManagement (IAM)mechanismconsists of the componentsand policies necessary to control and track user identities and access privileges forIT resources, environment and systems.”

“The IAM mechanism, specially used as systems that consists of 4 maincomponents. They are:”

(1) “Authentication: The IAM system manages the “username and password com-binations as a formof user authentication,which also supports digital signatures,biometric hardware, specialized software and locking user accounts to registeredIP or MAC addresses.”

(2) “Authorization: It defines the correct granularity for access controls and over-sees the relationships between identities, access control rights and IT resourceavailability.”

(3) “UserManagement:This program is responsible for creating new user identitiesand access groups, resetting passwords and managing privileges.”

(4) “Credential Management: It establishes identities and access control rules fordefined user accounts, which mitigates the threat of insufficient authorization.”


Fig. 2 Security broker service to authenticate the credentials using cloud service consumer

(f) “Public Key Infrastructure (PKI)”This security mechanism is used primarily to counter the threats that occuer dueto insufficient authorization. “It exists as a system of protocols, data formats andpractices to enable large-scale systems to securely use public key ryptography”. “Byusing this, we can associate the public keys with their corresponding key owners.”


“PKIs rely on the use of digital certificates, which are digitally signed datastructures that bind public keys to certificate owner identities, as well as to relatedinformation, such as validity periods.”

(g) “Hardened Virtual Server Images”“Hardening is the process of stripping unnecessary software from a system to limitpotential vulnerabilities that can be exploited by attackers which removes redundantprograms, closes unnecessary server ports and disabling unused services, internalroot accounts and guest access.”

A virtual service instance is created by using “hardened virtual server imagetemplate which is subjected to a hardening process that generally results in a virtualserver template. That derived template is significantly more secure than the originalstandard image.”

(h) “Cloud-Based Security Groups”“By allocating different type of physical IT resources to virtual machines, resourcesegmentation is done to enable virtualization. Cloud resource segmentation is a pro-cess by which separate physical and virtual IT environments are created for differentusers and groups.”

“Here two networks are maintained, one network is established with a resilientfirewall for external Internet access, while the another one is deployed without afirewall because its users are internal and unable to access the Internet” (Fig. 3).

(I) “Web Vulnerability Scanners”These are “automated tools that scan web applications, normally from the out-side, to look for security vulnerabilities such as Cross-site scripting, SQL Injection,Command Injection, Path Traversal and insecure server configuration.”

The hacker uses multiple techniques to attack web applications, so we have to usethe scanner which detects a significant number of vulnerabilities. We need to scanour website regularly to maintain continuous security.

The following are web vulnerability scanners in cloud, which we don’t need toinstall any software on our server.Vulnerability Scanners

• Acunetix• Netspaker• Detectify• ImmuniWeb• Qualys• Fortify• Scan My Server• Hacker Target.

(J) “Data loss prevention (DLP)”“This strategy is used tomake sure that the end users should not send the sensitive andcritical information outside the corporate network. This technology is also used to


Fig. 3 Cloud-based security group A encompasses virtual servers A and Dand is assigned to cloudconsumer A

describe software products that help a network administrator to control what data endusers can transfer.”

“DLP products is also referred to” as “data leak prevention, information lossprevention or extrusion prevention products.”

The DLP uses “business rules to classify and protect confidential and criticalinformation so that unauthorized end users cannot accidentally or maliciously sharedata whose disclosure could put the organization at risk. This technique is also usedto monitor and control endpoint activities. In addition, some DLP tools can also beused to filter data streams on the corporate network and protect data in motion.”

For example, “if an employee tried to forward a business email outside thecorporate domain or upload a corporate file to a consumer cloud storage servicelike Dropbox, the employee would be denied permission.”


(k) “Content Filtering”“Content filtering is also known as information filtering. In this technique, a programis used to screen and exclude objectionable web pages and e-mails availability. Con-tent filtering is used by corporations as part of Internet firewall computers and alsoby home computer owners, especially by parents to screen the content their childrenhave access to from a computer.”

It usuallyworks by “specifying character strings that, ifmatched, indicate undesir-able content that is to be screened out. Content is typically screened for pornographiccontent and sometimes also for violence- or hate-oriented content. Critics of contentfiltering programs point out that it is not difficult to unintentionally exclude desirablecontent.”

“Content filtering service products can be further divided intoWebfiltering, whichis used to screen the web sites and web pages, and e-mail filtering, used for screeningof e-mail for spam or other objectionable content.”

5 Conclusion

Cloud computing provides an extremely successful application for each and everyorganization “to store large amount of data” and enables the user to access the datafrom anyplace, anytime in a simple manner. Improved use of “cloud computing fordata storage” is increasing the ways to store data in the cloud in a secure manner.There are a number of security issues in the cloudwhichwe use differentmechanismsfor security prevention as described above. This study provides an overview about“cloud computing”, type of data stored in a cloud, cloud storage, security issues andcloud security mechanisms.

References

1. A. Venkatesh, M.S. Eastaff, A study of data storage security issues in cloud computing. Int. J.Sci. Res. Comput. Sci. Eng. Inf. Technol. © IJSRCSEIT 3(1) (2018), ISSN: 2456-3307

2. L.Wang, J. Tao,M. Kunze, A.C. Castellanos, D. Kramer,W.Karl, “Scientific cloud computing:early definition and experience” in 10th IEEE International Conference on High PerformanceComputing andCommunications, Dalian, China, Sep. (2018), pp. 825–830, ISBN: 978-0-7695-3352-0

3. S.A. El-Booz, G. Attiya, N. El-Fishawy, A secure cloud storage system combining time-basedonetime password and automatic blocker protocol, El-Booz et al. EURASIP J. Inf. Secur. 13(2016)

4. M. Subhashini, Dr. P. Srivaramangai, A study on cloud computing securities and algorithms.Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. © IJSRCSEIT 3(3) (2018), ISSN: 2456-3307

5. Dr.R. Sugumar,K.Raja, EDSMCCE: enhanceddata securitymethodology for cloud computingenvironment. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 9(2) (2018), www.thesai.ijacsa.org

6. D. Hyseni, U.H. Prizren, K.B. Selimi, The proposedmodel to increase security of sensitive datain cloud computing. © IJSRSET 4(1) (2018), Print ISSN: 2395-1990| Online ISSN: 2394-4099,(Themed Section: Engineering and Technology)

http://www.thesai.ijacsa.org


7. V. Suresh Babu, M.V. Maddali, M. Kumar, An efficient and secure data storage operations inmobile cloud computing. Int. J. Adv. Res. Eng. Sci. Technol. 4(5) (2017), e-ISSN: 2393-9877,p-ISSN: 2394-2444, May- All Rights Reserved, @IJAREST-2017 Impact Factor (SJIF): 4.54238 (2017)

8. J. vyas, P. Modi, Providing confidentiality and integrity on data stored in cloud storage by HashandMeta-Data Approach. Int. J. Adv. Res. Eng. Sci. Technol. 4(5) (2017), e-ISSN: 2393-9877,p-ISSN: 2394-2444, May- All Rights Reserved, @IJAREST-2017 Impact Factor (SJIF): 4.54238 (2017)

9. K. Sangeetam, Security issues in cloud computing. Jun. 07, 19 · Security ZoneAnalysis. Int. J.Appl. Eng. Res. 13(10) (2018), ISSN 0973-4562

10. S. Singh, T., Nafis, A. Sethi, Cloud computing: security issues & solution. Int. J. Comput.Intell. Res. 13(6), 1419–1429 (2017), ISSN 0973-1873, ©ResearchIndiaPublicationshttp://www.ripublication.com

11. B. Mahalakshmi, Assessment on security issues and classification in cloud computing. Int. J.Innov. Res. Appl. Sci. Eng. (IJIRASE) 1 (2018)

12. M. Mahendar, M. Anusha, Privacy-preserving public auditing for secure cloud storage. Int. J.Sci. Res. Comput. Sci. Eng. Inf. Technol. 3(1), 242–246 (2018)

13. M.K. Sarkar, S. Kumar, A survey on data storage security issues in cloud computing. Int. J.Appl. Eng. Res. 13(10), 8390–8406 (2018), ISSN 0973-4562, © Research India Publications,http://www.ripublication.com

14. O.O. Malomo, D.B. Rawat, M. Garuba, A survey on recent advances in cloud computingsecurity. J. Next Gener. Inf. Technol. (JNIT) 9(1) (2018)

15. P.S.V. Sainadh, U. Satish Kumar, S. Haritha Reddy, “Security issues in cloud computing”. Int.J. Mod. Trends Sci. Technol. 03(01), 125–130 (2017)

http://www.ripublication.com

http://www.ripublication.com

Smart Bag Using Electromagnet Zipper

M. Goutham Kumar, M. Suma, K. Kishore Reddy, and D. Ajitha

Abstract Technology incorporation make things more useful. Integrating technol-ogy into bag has changed the bag from being just a luggage carrier to an interactivedevice. Technology enables us to know the location of the bag, details of the items inthe bag etc. It can also be used to provide security to the bag and the items in it. In thispaper Electromagnetic Zipping is proposed to provide security to the items in the bag.Electromagnetic zipper consists of a fingerprint sensor which allows only authorizeduser to open the bag with the help of electromagnets on both the zip slides. A LithiumPolymer battery(LiPo) is used to give power supply to all the components. Differ-ent technologies incorporated in bags are studied and an attempt is made to presenta smart bag design with all the available technologies along with ElectromagneticZipper. Charging ports are also present to charge electronic gadgets. Electromag-netic Zipper, Global System for Mobile Communication(GSM), Global PositioningSystem(GPS), Radio Frequency Identification(RFID), Bluetooth technologies areintegrated in this design.

Keywords Electromagnets · Charging ports · Lipobattery · GSM · GPS · RFID ·Bluetooth module

M. Goutham Kumar (B) · M. Suma · K. Kishore Reddy · D. AjithaDepartment of Electronics and Communication Engineering, Sreenidhi Institute of Science andTechnology, Yamnampet, Ghatkesar, Hyderabad, Indiae-mail: [email protected]

M. Sumae-mail: [email protected]

K. Kishore Reddye-mail: [email protected]

D. Ajithae-mail: [email protected]


13






https://doi.org/10.1007/978-3-030-46943-6_2

14 M. Goutham Kumar et al.

1 Introduction

All the present day bags contain zipswith slidersmade up of iron. In this paper, slidersare made up of electromagnets. The copper turnings around it make them achievethis property. This electromagnetic locking system is linked with fingerprint sensor[1]. This sensor stores the biometrics of an individual and only he is considered tobe the authorised person to open the bag [2]. This gives more security to the bag andthe things in it. Along with it there is a pre-assumed schedule displayed on the screen(LCD) present on the bag. All the items needed on a particular day are scheduledin the application (App). Bluetooth module is present inside the bag [3]. Items aredisplayed on the screen while the owner is packing the bag through Bluetooth of theowner‘s phone. All the items being put in the bag have an RFID sticker. The bagcontains RFID reader [4]. These items are preloaded into the system according totheir tags. The list gets revised whenever an item is placed in the bag and new listdisplays on the screen. GPS module is used to find the location of the bag if thebag is misplaced or theft [5]. GSM helps in sending notifications to the owner inany unforeseen situation [6]. Charging ports are available to charge the electronicgadgets whenever needed. All these components are connected to the AtMega16microcontroller which is main component of the Smart bag [7]. The buzzer makesnoise when an unauthorised person tries to unlock the bag.

2 Proposed System

2.1 Block Diagram

The Fig. 1 is the block diagram of the smart bag. It contains all the componentspresent in the bag.

Atmel Microcontroller (AT89C51). In this paper, 8051 is used as main controllerto which all the components like GPS, GSM etc., are connected. The controller hasCentral Processing Unit(CPU) which controls the functions of other components.

Electromagnets. Electromagnets provide magnetic field when there is electric cur-rent passed through it and there is absence of magnetic field when there is no electricfield. In this bag, one zip slider is made up of an electromagnet and other is made upof a magnetic material. This helps in locking of the bag when needed.

Fingerprint Sensor. Fingerprint sensor uses receiver (Rx) and transmitter (Tx) pinof microcontroller to interact with it. One should save their biometrics in the memoryof the sensor to be considered as an authorised person.

GPS. In this Smart bag, GPS is used to detect the location of the bag if it getsmisplaced. This module takes the help of satellite to find the location of the bag.

Smart Bag Using Electromagnet Zipper 15

Fig. 1 Block diagram of smart bag

GSM module. GSM module is connected to the microcontroller. It requires SIMcard to set an active communication with the network. In our design, GSM is usedto send the GPS location of the bag to the owner’s mobile phone. It is also be usedto send a notification about the condition of the bag.

Bluetooth module. Bluetooth module is interfaced with the microcontroller. Blue-tooth module helps in transmission of data for shorter distances. In this paper we usethis module to display the scheduled items on the LCD screen placed on bag.

MAX232. It can be used as a hardware convertor for two systems to communicatewith each other at the same time.

Buzzer. The buzzer gives a beep sound when an unauthorised person tries to unlockthe sliders and also when the person’s fingerprint doesn‘t match.

3 Working

3.1 Methodology

The controller used in this design is 8051 microcontroller. All the functions in thebag take place on the command given by the controller. RFID reader is placed in thebag. Each item to be kept in the bag is having a RFID tag. There is an application

16 M. Goutham Kumar et al.

which stores the data about the items and their unique ids that are to be kept in thebag. The owner of the bag can pre-schedule the list and store it in the app.

There is a screen placed on the bag. The application sends the data to the controllerusing Bluetooth. This data is displayed on the LCD screen. Whenever a person putsan item in the bag the RFID reader placed in the bag reads the RFID tag placed onthat item. If the tag is matched automatically, the item on the screen gets disappeared.After placing all the items in the bag the screen displays a message that “you havesuccessfully placed all the items”. This way a person can take all the required itemswithout fail.

There is electromagnetic zip locking system which opens on authorized finger-print. The fingerprint sensor is placed at the side of the bag below the zip sliders.Initially authorized fingerprints are loaded into the system. After closing the zip thetwo sliders of the zip should be attached by hand for three seconds. Then the elec-tromagnetic slider gets supply and both the sliders get attached. This way the baggets locked. To open the bag authorized fingerprint is required. If the fingerprint ismatched then there will be no power supply to the electromagnetic sliders. So bothsliders get detached. Nowwe can open the bag. If the fingerprint doesn’tmatchwe getthe beep sound from the buzzer placed in the bag. If this sensor gets three unmatchedfingerprints as then there is a long beep sound. The notification containing location issent using GSM and GPS modules. This bag can also be incorporated with a powerbank for emergency charging of the mobile phones and laptops. This way it acts asa smart bag which can be used for all the purposes in this fast growing world.

The Fig. 2 explains the working in detail. It shows that bag only opens with anauthorised fingerprint. It also shows how the items are displayed on the screen.

4 Results

The electromagnetic zipper makes this bag very safe and secure. The fingerprintsensor allows only authorised person to open the bag. The GSM and GPS used inbag protect it from robbery. It gives location to the user if the bag is misplaced. Italso provides charging facility to the gadgets. It can be used as a travel bag, schoolbag, etc. The technology included in the bag makes it a modern bag which is suitablefor the present generation.

5 Conclusion

There is no end to technology. As the standard of living has increased there are newinnovations and inventions taking place every day. New Technologies are growingday by day very rapidly. Hence, there is a necessity to adopt them in our daily lifein an efficient way to make our living easy and secured. In this paper, a smart bag isproposed by utilizing the latest technologies so that the bag is very useful and secure.

Smart Bag Using Electromagnet Zipper 17

Fig. 2 Flow chart of smart bag

The features of the smart bag include electromagnetic zip locking system, locationtracking and sending notifications to the owner. It can be used as schoolbags, officebags etc. It can also be used for industrial and business purposes. In future, the samesystem can be used in packaging units in many industries.

References

1. R. Bonder, A.J. Fisher Jr., inventors; Nettel Tech Inc, assignee. Fingerprint identification securitysystem.US 6,078,265 United States patent (2000)

2. S.D. Cabouli, inventor; IWALLET Corp, assignee. Smart wallet. US 8,707,460 United Statespatent (2014)

3. H.C. Sim, inventor; Hyundai Motor Co, assignee. Bluetooth pairing system and method. US9,578,668 United States patent (2017)

4. N.M. Hashim, N.A. Ali, A.S. Jaafar, N.R. Mohamad, L. Salahuddin, N.A. Ishak, Smart orderingsystem via bluetooth. Int. J. Comput. Trends Technol. (IJCTT) 4, 2253–2256 (2014)

5. C.Q. Booth, inventor; Booth Cassius Q, assignee. Anti Theft Bag with Locator. US 12/537,308United States patent application (2011)

6. V. Varshney, P. Jha, M.N. Tiwari, D. Gupta, Solar powered smart bag, in Proceeding of the 12thINDIACom. (Bharati Vidyapeeth’s Institute of Computer Applications and Management, NewDelhi, 2018)

7. M. Shweta, P. Tanvi, S. Poonam, M. Nilashree, Multipurpose smart bag. Proc. Comput. Sci. 79,77–84 (2016)

Analysis on Various Feature ExtractionMethods for Medical Image Classification

S. Vani Kumari and K. Usha Rani

Abstract Soft Computing is an emerging technique of machine intelligence thatincludes methods like Neural Networks, Fuzzy Logic, Support Vector Machines andGenetic Algorithms. The aim of soft computing is to mimic the reasoning and deci-sion making of human. Neural networks are prominently being used in applicationsthat involve processing of voluminous data and hence it can be highly applicable tothe areas like image processing, stock market prediction and weather forecasting.Medical image processing involves four phases like Preprocessing, Segmentation,Feature Extraction and Classification. The aim of this work is to find the featureextraction method that is best for classifying the medical images. Local Binary Pat-terns (LBP),Gray-Level-Run-Length-Matrix (GLRM),CompletedLocalBinaryPat-terns (CLBP), Gray-Level Co-occurrence Matrix (GLCM) and Local Tetra Patterns(LTrP) are themost prominent feature extractionmethods for medical images and areconsidered in this study. Two well-known classifiers Multi-Layer Perceptron usingBackpropagation Network (MLPBPN) and Support VectorMachine (SVM) are usedto analyse the efficiency of above specified five feature extraction techniques. Fivedifferent medical image Datasets are considered for experimentation. The experi-mental results illustrate that GLCM method is the best method compared with theother four feature extraction methods for medical image classification.

Keywords Soft computing · Medical image processing · GLCM · GLRM · LBP ·CLBP · LTrP · MLPBPN · SVM

S. Vani Kumari (B)Department of Computer Applications, Government College for Women(A), Srikakulam, Indiae-mail: [email protected]

K. Usha RaniDepartment of Computer Science, Sri Padmavati Mahila Visvavidyalayam, Tirupati, Indiae-mail: [email protected]


19




https://doi.org/10.1007/978-3-030-46943-6_3

20 S. Vani Kumari and K. Usha Rani

1 Introduction

Soft Computing (SC) uses various computational methodologies that are used toexploit the tolerances in terms of imprecision and uncertainty in order to obtainvigorous, tractable and low cost solution. Since problems in real world are highlyinfluenced by impreciseness, uncertainties and categorical nature, SC is preferredto be applied in various applications such as pattern recognition, image processingespecially medical images, data mining etc. Neural Networks (NN), Probabilisticreasoning, Support Vector Machines, Fuzzy logic and Evolutionary Computationare the principal techniques of soft computing [1].

Medical image processing is utilized to enhance the interpretability of the gener-ated images and intensify the assessment of particular features for both automatedand manual data management [2]. For this reason, the medical research communitiesfocus on digital image processing to generate sufficient records.

Preprocessing being an important phase in medical image processing is forimproving the quality of the image as low contrast, unnecessary noise and weakboundaries are the general qualities of medical images. The preprocessing phaseincludes tasks like background removal and filtering. Contour methods can be usedto separate the background from the foreground in an image and to identify contin-uous boundaries of a medical image [3]. Active Contour Method is a well-knownContour method used for separating the necessary pixels from the background [4].After separating the original image from background, filtering is applied to improvethe quality of the image. Wiener Filter one the popular filtering technique used toremove Gaussian noise, salt and pepper noise and speckle noise from the medicalimages [5]. Discrete Fourier Transform (DFT) being a popular technique can be usedfor preserving the rotation invariance of an image. Image segmentation is used toidentify the segmented part of interest that contains the abnormalities in the medicalimages [6]. Watershed Algorithm is a popular segmentation method used for regionsegmentation and separates the overlapping images [7].

Texture of an image describes the arrangement of intensities and interesting char-acteristics of the image. Textural features play an important role in image processingand are therefore used in applications such as remote sensing, medical image pro-cessing and image retrieval based on content based image retrieval. Relevant featuresfrom the segmented part of interest are extracted during feature extraction.GrayLevelCo-occurrence Matrix (GLCM) is a well-known feature extraction method that usesco-occurrence or dependency matrices based on the gray level and distribution ofpixels [8]. The co-occurrence matrices are used to measure texture of the image.Gray Level Run Length Matrix (GLRM) a famous feature extraction method basedon the histogram of the image [9]. Run length is the number of adjacent pixels in aparticular direction with the same gray intensity

Local Binary Pattern (LBP) is a widely used approach for extracting features fromcomputer vision images [10]. LBP requires simple calculations and is also invariantto illumination. LBP is used in textural analysis of real time data inmany applications

Analysis on Various Feature Extraction Methods … 21

like face analysis and motion analysis. It concentrates on histogram statistics. LBPgives the information about the center pixel and its neighbors.

Completed Local Binary Patterns (CLBP) are popular texture feature extractionmethods based on neighborhood property [11]. Local Tetra Patterns (LTrP) is anotherpopular feature extraction method which encodes the neighboring pixels into threedifferent values based on the threshold and later on these neighboring pixels arecombined after thresholding [12].

The recent advancements in Computer-Aided Detection and Diagnosis studynecessitate the usage of classification methods to train the classifiers using medi-cal image Datasets to recognize the disease proficiently [13]. Classification is beingextensively used in statistics, machine learning etc. Some of the well-known classi-fiers are Support vector machines, Decision Trees, Neural Networks and Bayesianclassifier. Neural Networks and SVMs possess a unique ability of learning fromprevious experiences hence can being applied in medical diagnosis [14, 15].

In this study three popular methods Active Contour, Wiener Filter and DFT areapplied in the preprocessing phase. Five best feature extraction methods viz. GLCM,GLRM, LBP, CLBP and LTrP are considered for extracting the features frommedicalimages and finally two well-known classifiers MLPBPN and SVM are used forclassification.

Section 2 discusses related work, Sect. 3 addresses methods and methodology,Sect. 4 deliberates the experimental results and conclusion of paper is in Sect. 5.

2 Related Work

Oliveira et al. [4] reviewed various image segmentation that can be applied on skinlesions and discussed the importance of Active Contour Method for boundary detec-tion. Hemalatha et al. [16] discussed the importance of Active Contour Methodfor segmenting different medical images. Contours are a group of points obtainedby interpolation operation. Active Contour models are used in medical images forearly diagnosis and detection of abnormalities and are also used to separate theforeground from the background. George et al. [17] experimented five filtering tech-niques viz. Gaussian, Wiener, Adaptive, Mean and Median on mammogram imageDataset demonstrated that the Wiener Filter is best compared with the rest of theother techniques. Francesco et al. [6] proposed a method for normalization of rota-tion dependent features to rotation invariant features by applying Discrete FourierTransform on four textural Datasets.

Avinash et al. [7] developed an image processing method that uses Gabor filter forimage enhancement andWatershedMethod for segmentation on lung cancer Dataset.Nayak et al. [18] applied Watershed Algorithm to segment the images and identifythe points with minima in the surrounding region and develop catchment basins. Themethod is applied on lung cancer images to detect the abnormalities. The efficiencyof the segmentation method is evaluated based on right classification, overlappedarea and dice coefficient.

s. jyothi · d. m. mamatha · suresh chandra satapathy · k

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