Advances in Intelligent Systems and Computing

Volume 1133

Series Editor

Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences,Warsaw, Poland

Advisory Editors

Nikhil R. Pal, Indian Statistical Institute, Kolkata, India

Rafael Bello Perez, Faculty of Mathematics, Physics and Computing,Universidad Central de Las Villas, Santa Clara, Cuba

Emilio S. Corchado, University of Salamanca, Salamanca, Spain

Hani Hagras, School of Computer Science and Electronic Engineering,University of Essex, Colchester, UK

László T. Kóczy, Department of Automation, Széchenyi István University,Gyor, Hungary

Vladik Kreinovich, Department of Computer Science, University of Texasat El Paso, El Paso, TX, USA

Chin-Teng Lin, Department of Electrical Engineering, National ChiaoTung University, Hsinchu, Taiwan

Jie Lu, Faculty of Engineering and Information Technology,University of Technology Sydney, Sydney, NSW, Australia

Patricia Melin, Graduate Program of Computer Science, Tijuana Instituteof Technology, Tijuana, Mexico

Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro,Rio de Janeiro, Brazil

Ngoc Thanh Nguyen , Faculty of Computer Science and Management,Wrocław University of Technology, Wrocław, Poland

Jun Wang, Department of Mechanical and Automation Engineering,The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publicationson theory, applications, and design methods of Intelligent Systems and IntelligentComputing. Virtually all disciplines such as engineering, natural sciences, computerand information science, ICT, economics, business, e-commerce, environment,healthcare, life science are covered. The list of topics spans all the areas of modernintelligent systems and computing such as: computational intelligence, soft comput-ing including neural networks, fuzzy systems, evolutionary computing and the fusionof these paradigms, social intelligence, ambient intelligence, computational neuro-science, artificial life, virtual worlds and society, cognitive science and systems,Perception and Vision, DNA and immune based systems, self-organizing andadaptive systems, e-Learning and teaching, human-centered and human-centriccomputing, recommender systems, intelligent control, robotics and mechatronicsincluding human-machine teaming, knowledge-based paradigms, learning para-digms, machine ethics, intelligent data analysis, knowledge management, intelligentagents, intelligent decision making and support, intelligent network security, trustmanagement, interactive entertainment, Web intelligence and multimedia.

The publications within “Advances in Intelligent Systems and Computing” areprimarily proceedings of important conferences, symposia and congresses. Theycover significant recent developments in the field, both of a foundational andapplicable character. An important characteristic feature of the series is the shortpublication time and world-wide distribution. This permits a rapid and broaddissemination of research results.

** Indexing: The books of this series are submitted to ISI Proceedings,EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **

More information about this series at http://www.springer.com/series/11156

Niranjan N. Chiplunkar • Takanori FukaoEditors

Advances in ArtificialIntelligence and DataEngineeringSelect Proceedings of AIDE 2019

123

EditorsNiranjan N. ChiplunkarNMAM Institute of TechnologyUdupi, India

Takanori FukaoRitsumeikan UniversityShiga, Japan

ISSN 2194-5357 ISSN 2194-5365 (electronic)Advances in Intelligent Systems and ComputingISBN 978-981-15-3513-0 ISBN 978-981-15-3514-7 (eBook)https://doi.org/10.1007/978-981-15-3514-7

© Springer Nature Singapore Pte Ltd. 2021This 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, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721,Singapore

Organization

Chief Patron

Shri. N. Vinaya Hegde, Chancellor, Nitte (Deemed to be University); President,Nitte Education Trust, Mangalore

General Chairs

Niranjan N. Chiplunkar, NMAM Institute of Technology, NitteI. R. Mithanthaya, NMAM Institute of Technology, NitteB. R. Srinivas Rao, NMAM Institute of Technology, NitteSudesh Bekal, NMAM Institute of Technology, Nitte

Advisory Committee

N. R. Shetty, Chancellor, Central University, Karnataka, IndiaOmid Ansary, Penn State, Harrisburg, USAB. S. Sonde, ASM Technologies Limited, IndiaL. M. Patnaik, IISc, Bengaluru, IndiaK. Chidananda Gowda, Kuvempu University, IndiaP. Nagabhushan, IIIT, Allahabad, IndiaD. S. Guru, University of Mysore, Mysuru, IndiaNarayan Choudhary, Central Institute of Indian Languages, Mysuru, IndiaH. N. Prakash, RIT, Hassan, IndiaN. V. Subba Reddy, MIT, Manipal, IndiaMohan Hegde, NMAMIT, Nitte, India

v

M. Hanumanthappa, Bangalore University, Bengaluru, IndiaV. N. Manjunath Aradhya, JSSSTU, Mysuru, IndiaB. G. Satish, CSI Chairman, Bengaluru

Steering Committee

Shri. Vishal Hegde, Nitte (Deemed to be University), IndiaN. R. Shetty, Central University, Karnataka, IndiaB. S. Sonde, ASM Technologies Ltd., Bengaluru, IndiaG. Hemanth Kumar, University of Mysore, IndiaOmid Ansary, Penn State University, USASamson Ojawo, Ladoke Akintola University of Technology, NigeriaShripad T. Revankar, Purdue University, USAShuhaimi Mansor, Universiti Teknologi, Malaysia

Programme Chairs

K. R. Udaya Kumar Reddy, NMAM Institute of Technology, NitteD. S. Guru, University of Mysore, Mysuru

Organizing Chairs

K. R. Udaya Kumar Reddy, NMAM Institute of Technology, NitteSurendra Shetty, NMAM Institute of Technology, NitteB. H. Karthik Pai, NMAM Institute of Technology, Nitte

Conference Secretary

C. V. Aravinda, NMAMIT, Nitte

Track Chairs

Jyothi Shetty, NMAMIT, NitteP. S. Venugopala, NMAMIT, Nitte

vi Organization

Publication Chairs

Niranjan N. Chiplunkar, NMAMIT, Nitte, IndiaD. S. Guru, University of Mysore, Mysuru, IndiaTakanori Fukao, Ritsumeikan University, Japan

Publicity Chair

K. B. Sudeepa, NMAMIT, Nitte

Workshop Chair

D. Radhakrishna, NMAMIT, Nitte

Social Event Chair

Roshan Fernandes, NMAMIT, Nitte

Organizing Committee

Udaya Kumar K. ShenoyKaruna PanditD. K. SreekanthaSharada U. ShenoySarika HegdeB. AshwiniK. RajuMamatha BalipaPradeep KanchanVasudeva PaiT. Vijay MurariK. N. PallaviP. R. AnishaK. R. RaghunandanRamesh ShettigarShashank Shetty

R. BalasubramaniS. V. Aruna KumarK. AkshayaNikitha SourabhTanzila NargisSaritha ShettyB. RaviB. R. PuneethBalachandra RaoSrikanth BhatArahat JainAbhir BhandaryRanjan KumarM. S. SannidhanPawan HegdeR. P. Puneeth

Organization vii

Krishna Prasad RaoAnkitha NayakV. SanjuDevidasKrishnarajM. ShruthiShilpa KaregoudarRashmi NaveenAbhishek S. RaoPrathyakshiniS. SapnaPallavi ShettyRamyaA. G. MangalaR. AnandSampath KiniMahesh KiniB. C. Keerthana

Sunil Kumar AithalJason E. MartisAsmita PoojaryMinu P. AbhrahamSpoorthiDivya Jennifer D’SouzaSandeep Kumar HegdeDeepaSavithaRajalaxmi HegdeRajashreeShabari ShedthiG. K. ShwethaChinmai ShettyK. Shrikanth BhatAnushaAlaka AnanthAnantha Murthy

viii Organization

Preface

The editorial team is honoured to announce the publication of the conferenceproceedings of the International Conference on Artificial Intelligence and DataEngineering (AIDE 2019). The conference was organized by the Departments ofComputer Science and Engineering, Information Science and Engineering andMaster of Computer Applications of NMAM Institute of Technology, Nitte, India.

With the resurgence of machine intelligence, neural networks and huge data, theartificial intelligence and the data engineering have become extremely active areasof research. The focus of the conference was to understand the recent advancementsin the artificial intelligence and data engineering era of computing systems thatemulate the capability of human intelligent mind. The conference provided a forumfor researchers, practitioners, undergraduate and postgraduate students to presentand discuss the latest theoretical advances and real-world applications in all areasrelated to artificial intelligence and data engineering.

There were 360 papers submitted to AIDE 2019 from across globe, and they allunderwent a rigorous review process. Upholding the quality requirements, around35% of the papers received were selected for presentation.

The conference comprised of pre-conference workshop by Dr. Dinesh R. andDr. Manjunath S. as resource persons from Samsung Electro Mechanics,Bengaluru, as well as keynote talks by Prof. (Dr.) Ernest Cachia, University ofMalta, Malta; Dr. Vimala Balakrishnan, University of Malaya, Malaysia; Prof. (Dr.)D. S. Guru, University of Mysore, Mysuru, India; and Prof. (Dr.) Kavi Mahesh,Indian Institute of Information Technology, Dharwad, India.

We would like to gratefully acknowledge the support received from NMAMInstitute of Technology, Nitte, in organizing this conference. We would also like togratefully acknowledge the College of Science and Engineering, RitsumeikanUniversity, Japan, and the Department of Studies in Computer Science, Universityof Mysore, Mysuru, for their kind support in conducting this conference. We wouldlike to acknowledge all the researchers, reviewers, speakers, members of variouscommittees, international/national advisory committee members, general chairs,programme chairs, technical committee and organization committee for helping usattain the objectives of the conference. We are sure that this conference has given an

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excellent platform for all researchers to share their ideas to update themselves withthe latest developments, knowing how other groups are applying the technologyand exchange ideas with leading international experts in the fields of artificialintelligence, data engineering and its affiliated disciplines.

We hope that the conference book of abstracts will be inspiring to readers, whichis ideal for researchers, practitioners, academicians and students from artificialintelligence community and data engineering community.

Udupi, India Niranjan N. ChiplunkarShiga, Japan Takanori FukaoMysuru, India D. S. GuruUdupi, India K. R. Udaya Kumar ReddyUdupi, India Surendra ShettyUdupi, India B. H. Karthik Pai

x Preface

Acknowledgements

The organization of a conference is always a stressful adventure because of all thevery small things and all the very important issues that have to be planned andmanaged.

We would like first to express our deep gratitude to the NMAM Institute ofTechnology (NMAMIT), Nitte, in organizing this conference. We would like togratefully acknowledge the College of Science and Engineering, RitsumeikanUniversity, Japan, and the Department of Studies in Computer Science, Universityof Mysore, Mysuru, for their kind support in conducting this conference. We wouldalso like to extend our heartiest thanks to the Central Institute of Indian Languages(CIIL), Mysuru, Government of India, for sponsoring this conference under theplatinum category.

Our gratitude to the Computer Society of India (CSI) and the Indian Society ofTechnical Education (ISTE) for extending their technical support to the conference.

We would like to address a warm thank to all the members of the scientificcommittee for their participation and expertise in the preparation of the conference.

Our sincere gratitude is extended immensely to all the eminent scientists (frominternational and national level) who agreed to deliver a keynote speech and tooktheir precious time and joined us in sharing their invaluable inputs and expertisewith all the delegates at the conference. We also thank all the experts who playedthe role of moderators and chairs of sessions for taking their time and givingvaluable inputs to all author delegates.

We would like to acknowledge the entire organizing committee who workedintensively for months to manage the conference, such as website, call for proposal,answers, evaluations, to name a few.

We would like to acknowledge all the members of various committees, partic-ularly international/national advisory committee members, general chairs, pro-gramme chairs and technical committee for their work.

Our heartiest thanks go to all the reviewers for reviewing the papers promptlyand spending their valuable time and providing invaluable inputs to research papersdespite their busy schedule.

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Our heartiest greetings go to all the participants who proposed a paper as theirvaluable contribution and came to NMAM Institute of Technology, Nitte, to presentit in making this event a grand success.

Finally, we immensely thank Springer Nature and their team for theirhigh-quality support, coordination and cooperation.

Niranjan N. ChiplunkarTakanori Fukao

D. S. GuruK. R. Udaya Kumar Reddy

Surendra ShettyB. H. Karthik Pai

xii Acknowledgements

About the Conference

We are very much pleased to present this special issue of abstracts which consistsof the proceedings of the International Conference on Artificial Intelligence andData Engineering 2019 (AIDE 2019) organized by NMAM Institute of Technology,Nitte, India, in collaboration with the College of Science and Engineering,Ritsumeikan University, Japan, and the Department of Studies in ComputerScience, University of Mysore, Mysuru, India. The conference was held during23–24 May 2019.

The AIDE 2019 conference is aimed at providing an international forum fordiscussing the latest theoretical advances and real-world applications in all areasrelated to artificial intelligence (AI) and data engineering (DE) and its affiliateddisciplines. The addressed topics of interest are divided into four major groups:artificial intelligence, machine vision and robotics, ambient intelligence and dataengineering.

The goal of AIDE 2019 was to bring researchers across the following disci-plines: artificial intelligence, expert systems, machine learning, robotics, virtualreality, augmented reality, bioinformatics, intelligent systems, cognitive systems,computational intelligence, neural networks, evolutionary computation, speechprocessing, Internet of things, big data challenges, data mining, informationretrieval and natural language processing to establish an effective channel ofcommunication together with the AIDE community to discuss the latest theoreticalwork and experimental work, and to give AIDE attendees the opportunity to knowhow other groups are applying the technology and exchange ideas with leadinginternational experts in these areas of interest. The book is ideal for students,researchers, practitioners and academicians from artificial intelligence community,data engineering community and combination of these to create an approachtowards a global exchange of information on technological advances and newscientific innovations. Moreover, it is attractive in terms of being popular on arti-ficial intelligence tools, data engineering tools, techniques and methods that areapplicable to real-world problems.

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Salient Features

• Coverage of all aspects of theoretical and applied AI and DE research.• Coverage of invited talks on innovative applications of AI and DE.• Student, researcher, practitioner and academician learning is supported by the

cutting-edge technology along with the application of tools of both AI and DEand a combination of these.

The research papers of AIDE 2019 have been peer-reviewed by at least tworeviewers drawn from the scientific committee, external reviewers and editorialboard depending on the subject matter of the paper. Papers are selected on the basisof their scientific merit and relevance to the conference topics.

xiv About the Conference

Keynote Speaker

Dr. Ernest CachiaDean and Head of Department,Faculty of Information and Communication Technology,University of Malta, Msida (Europe)

Title of Talk: “Staying Smart in a Smarter World”

Brief BiodataProfessor Ernest Cachia currently holds the position ofHead of the Department of Computer InformationSystems, and for 12 consecutive years (2007–2019)held the position of Dean of the Faculty of Informationand Communication Technology. He is also theChairman of the Institute of Aerospace Technologies atthe University of Malta and Director of Malta AirTraffic Services (MATS). Furthermore, he is theUniversity of Malta representative on the MaltaCompetition and Consumer Affairs—sitting on theEU CEN e-Skills e-Competencies Common Framework

(e-CF) Standardisation Committee, he is a founding member of the Malta ITAgency e-Skills Alliance, founding member of the Malta Cloud Forum within theMalta Communications Authority, a core member of the e-Skills (Malta)Foundation and a founding member of (former) EuroCloud (Malta) organization.He acted as the Innovation and Technology Module Leader in the creation of a JointMasters programme for Border Management, coordinated by Frontex, the EuropeanBorder Guard and Border Management Agency. He is routinely engaged as acourt-appointed expert in ICT-related cases and was nationally recognized by beingnominated for the award of Worker of the Year 2011.

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He holds a doctorate in software engineering from the University of Sheffield,UK. He has worked in various electronics and ICT companies, and private edu-cational establishments, and since 1994 holds a tenured academic post at theUniversity of Malta. He was involved in various EU projects and was the projectleader for multi-million Euro ERDF project constructing and equipping the existingfaculty of ICT building. He has managed teams of developers working on both EUand internal projects. Academically, he is currently responsible for the softwareengineering and software project management streams in both B.Sc. IT (Hons)software development and B.Sc. IT (Hons) computing and business degree pro-grammes of the faculty of ICT at the University of Malta. He is also a memberof the IT Services Board of Directors of the University of Malta.

His native languages are Maltese and English. However, he is also completelyfluent in Italian and Russian, has working knowledge of French and Spanish and abasic understanding of Arabic. In 2006, he was knighted as a Knight of theSovereign Order of Saint John of Jerusalem (The Hereditary Order) by the thenGrandmaster of the Order himself—Michael Vella Haber.

Abstract

The modern ICT landscape is characterized by many issues that unless viewedholistically often lead to tunnel vision and isolated focus. The main researchdirections in today’s ICT scenario can be seen as Bio-Info-Nano-Cogno (BINC).Based on this road map, specific domains attracting interest and advancement canbe viewed as (1) big data (BD); (2) artificial intelligence (AI); and (3) Internet ofThings (IoT). We will visit and reason about many such issues (and questions theygive rise to) that many researchers often tend to tackle separately.

For instance, in a world that embraces, directly or indirectly, the proliferation,pervasiveness and permeation of smart devices, how can such smartness be con-trolled and exploited? Where are we now in terms of data generation and what doesthis mean to all aspects of development, social behaviour and business models? Isthe Cloud “old news” or still very relevant? Why are we in the situation we are inand what exactly is big data and how does it drive the rest of the modern ICTlandscape? Which are the current main business and social disruptors? Is there anew way to approach system functionality through data availability and valueextraction? How must modern developers think in terms of innovation and cre-ativity in a marketplace overflowing with data, and how should they appreciate themulti-faceted nature and fickle behaviour of data valorization? It is time to clearlydefine a new data-driven business model as well as new “job descriptions” such asthat of a data scientist and a data analyst? Last but definitely not least—What is“smartness”, where does it actually reside and how can it be controlled? This lastquestion will be discussed through the types, and management, of smart devices, aswell as, what I like to call, technology to handle technology.

xvi Keynote Speaker

We will analyse the evolution of technologies as well as today’s disruptivetechnologies as seen through the adaptation of Kondratiev Economic Waves andthe related “Hype Cycle”. In a way, this mapping can help predict what is char-acteristically unpredictable. A technology evolutionary scheme is proposed that,together with the Kondratiev Wave approach, may help discern technologicalevolutionary patterns.

The notion of the “more the merrier” can seem attractive; however, unlessproperly managed and integrated into digital solutions, it can easily lead to falsestates of reliability and security. In this regard, we will dissect the Cloud, itsservices and related issues. We will look at the key issues to proper and managedCloud adoption as well as Cloud and Computing Paradigm Drivers and Cloud andIoT interaction. Naturally, one cannot omit mentioning related risk and cybercrimeimplications together with blockchain reasoning.

Keynote Speaker xvii

Keynote Speaker

Dr. Vimala BalakrishnanSenior Lecturer, and Data Scientist,Faculty of Computer Science and Information Technology,University of Malaya, Malaysia

Title of Talk: “Machine Learning and Data Analyticsin Science”

Brief BiodataDr. Vimala Balakrishnan is Senior Lecturer and DataScientist affiliated with the Faculty of ComputerScience and Information Technology, University ofMalaya, since 2010. She obtained her Ph.D. in the fieldof ergonomics from Multimedia University, whereasher masters and bachelor’s degrees were fromUniversity of Science, Malaysia. Her main researchinterests are in data analytics and sentiment analysis,particularly related to social media. Her researchdomains include health care, education and socialissues such as cyberbullying. She has published

approximately 47 articles in top indexed journals and also serves as an associateeditor to the Malaysian Journal of Computer Science and as Associate Member forthe Global Science and Technology Forum. She is also a fellow for the Leadershipin Innovation programme, a prestigious award by the Royal Academy ofEngineering, UK, and a Fulbright Visiting Scholar.

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Abstract

Her keynote topic during the Artificial Intelligence and Data Engineering 2019would mainly focus on how artificial intelligence, particularly machine learning anddata analytics, can be used not only in the field of science, but also in the field ofsocial sciences. This, in fact, has been her research motivation for many years, asshown by her most recent works targeting detection mechanisms in cyberbullying,digital economy and electoral analysis, among others. Valuable data are availableaplenty, especially on social media. These data can be leveraged to identify variouspatterns and insights, providing useful information to various stakeholders. The useof machine learning algorithms to develop detection mechanisms is gainingmomentum not only in the Western world, but in the Asian countries as well.Dr. Balakrishnan will particularly highlight how and where AI and data engineeringhave been incorporated, by specifically focusing on her recent work on cyberbul-lying detection mechanism.

xx Keynote Speaker

Keynote Speaker

D. S. GuruProfessor,Department of Studies in Computer Science,University of Mysore, India

Title of Talk: “Interval Valued Feature Selection: FilterBased Approaches”

Brief BiodataD. S. Guru received his B.Sc., M.Sc. and Ph.D. incomputer science and technology from the Universityof Mysore, Mysuru, India, in 1991, 1993 and 2000,respectively. He is currently Professor in theDepartment of Studies in Computer Science, Universityof Mysore, India. He was a fellow of BOYSCAST anda visiting research scientist at Michigan StateUniversity. He is the first ARP recipient from VisionGroup of Science and Technology, KarnatakaGovernment. He has successfully guided 17 Ph.D.s andcurrently supervising 6. He has authored 68 journals

and 230 peer-reviewed conference papers at international and national levels. He isa co-author for three textbooks, editor of LNNS-43, LNNS-14 and LNEE-213. Heis a founder Trustee cum Treasurer of Maharaja Education Trust, Mysore, whichhas established a couple of educational Institutions in and around Mysore. His areaof research interest covers image retrieval, text mining, machine learning, objectrecognition, shape analysis, sign language recognition, biometrics and symbolicdata analysis.

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Abstract

This talk is intended to introduce an unconventional data analysis called symbolicdata analysis [1], in the field of pattern recognition and its allied areas [2–6]. Thetalk initially presents various types of feature representation and subsequentlyhighlights symbolic proximity measures in general and interval-valued proximitymeasures in particular [7, 8]. The problem of selectively choosing a subset ofinterval-valued features out of several available for compact representation of datawould be addressed. Various models belonging to three categories, viz. modelswhich transform interval data to crisp [9, 10], models which exploit conventionalstatistical measures directly on interval-valued data [11] and models whichaccomplish feature selection through feature clustering of interval-valued features[12, 13], shall be presented.

During the talk, necessary theory along with illustrative examples will be pre-sented to highlight the abilities of symbolic data in preserving the reality in solvingpattern recognition-related problems. Further, experimental results on some stan-dard datasets shall also be discussed.

Keywords Symbolic data analysis, Interval-valued data, Interval proximitymeasures, Interval-valued feature selection.

References

1. Billard L, Diday E (2006) Symbolic data analysis: conceptual statistics and data mining.Wiley Publishers

2. Guru DS, Nagendraswamy HS (2007) Symbolic representation of two-dimensional shapes.Pattern Recogn Lett 28(1):144–155

3. Guru DS, Prakash HN (2009) Online signature verification and recognition: an approachbased on symbolic representation. IEEE Trans Pattern Anal Mach Intell 31(6):1059–1073

4. Harish BS, Guru DS, Manjunath S, Dinesh R (2010) Cluster based symbolic representationand feature selection for text classification. In: Advanced data mining and applications.Lecture notes in computer science, vol 6441, pp 158–166

5. Guru DS, Kumar NV (2017) Interval valued feature selection for classification of logos. In:17th international conference on intelligent systems, design and applications 2017, vol 736,Springer AISC, pp 154–165

6. Guru DS, Manjunatha KS, Manjunath S, Somashekara MT (2017) Interval valued symbolicrepresentation of writer dependent features for online signature verification. Expert Sys Appl80:232–243

7. Guru DS, Kiranagi BB (2005) Multivalued type dissimilarity measure and concept of mutualdissimilarity value for clustering symbolic patterns. Pattern Recogn 38(1):151–156

8. Guru DS, Kiranagi BB, Nagabhushan P (2004) Multivalued type proximity measure andconcept of mutual similarity value useful for clustering symbolic patterns. Pattern Recog Lett25(10):1203–1213

9. Guru DS, Kumar NV (2016) Novel feature ranking criteria for interval valued featureselection. In: IEEE ICACCI 2016, pp 149–155

xxii Keynote Speaker

10. Kumar NV, Guru DS (2017) A novel feature ranking criterion for interval valued featureselection for classification. In: 14th IAPR international conference on document analysis andrecognition, IEEE, pp 71–76

11. Guru DS, Kumar NV (2018) Interval chi-square score (ICSS): feature selection of intervalvalued data. In: 18th international conference on intelligent systems, design and applications2018, vol 941, Springer AISC, pp 686–698

12. Guru DS, Kumar NV (2017) Class specific feature selection for interval valued data throughinterval k-means clustering. vol 701, Springer CCIS, RTIP2R 2016, pp 228–239

13. Guru DS, Kumar NV, Suhil M (2017) Feature selection for interval valued data throughinterval k-means clustering. In: International journal of computer vision and image processing(IJCVIP), vol 7(2), IGI-global, pp 64–80

Keynote Speaker xxiii

Keynote Speaker

Dr. Kavi MaheshDirector,Indian Institute of Information Technology,Dharwad, India

Title of Talk: “AI and ML: Can Data Lead to Intelligence?”

Brief BiodataDr. Kavi Mahesh is Director of the Indian Institute ofInformation Technology, Dharwad. Previously, he wasthe Dean of Research at PES University, Director of theWorld-Bank-funded Research Centre for KnowledgeAnalytics and Ontological Engineering—KAnOE andProfessor of computer science. His areas of interestare knowledge management, analytics, epistemology,ontology, classification studies, text processing andunstructured data management. He has three US patentsand has published two books, 16 chapters and 80papers which have received over 1500 citations.

Notable among these are the textbooks—Theory of Computation: AProblem-Solving Approach (Wiley India, 2012) and Ten Steps to Maturity inKnowledge Management (Elsevier Pub. UK, 2006). He was earlier with OracleCorporation, USA, and New Mexico State University and has consulted in the areaof knowledge management with Infosys, Hewlett Packard, United Nations andwww.EasyLib.com. He holds an M. Tech. in computer science from the IndianInstitute of Technology, Bombay (1989), an MS (1991) and a Ph.D. (1995) incomputer science from Georgia Institute of Technology, USA.

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Abstract

Artificial Intelligence (AI) has come back in a big way in the last few years.However, making computing machines smarter, giving them human-like intelli-gence and enabling them to learn to do tasks better have been tried in a variety ofways in the previous several decades:

• Knowledge-based systems were developed based on the premise that intelligentbehaviour requires significant amounts of knowledge; by providingwell-designed and well-organized knowledge representations to machines, theywere expected to carry out intelligent tasks.

• Rule-based expert systems were built to solve specific problems requiringdomain expertise such as diagnosis or troubleshooting.

• Logical reasoning as the basis for rational thought was explored to a great extentwith the development of higher-order, modal, temporal, uncertain, fuzzy,description, and other forms of logic.

• Neural networks were designed to automatically learn solutions to problems forwhich it was difficult to specify an algorithm.

• Cognitive models were built not only to make machines intelligent but also toserve as accurate models of how the human brain/mind appears to function.

• Other ideas were explored too, for example genetic algorithms and swarmintelligence.

• Of course, attempts were made to build robots to exhibit human-like physicalmotion and perceptual capabilities.

Today’s enthusiasm about AI and machine learning (ML, including deep learning)must be seen in the broader context of these earlier developments. This is a newstep, perhaps a very big and important step, in the ongoing journey to enhance thecapabilities of computing machines, that is, to make them smarter and more efficientin solving problems of importance to us humans. Clearly, there is a lot of potentialin the new AI equipped with deep ML in further easing our lives and reducing ourdependence on unreliable and expensive human resources for carrying out mundanetasks in optimal ways.

Scepticism on whether machines can, in fact, be made intelligent is rarely to beseen today. Can machines think, Alan Turing asked way back in 1950. He provideda most practical answer to the question in the famous Turing Test wherein we arebound to accept that a machine can think if its behaviour is indistinguishable fromthat of a competent human being at the end of a controlled experiment. While no AImachine has thus far passed the Turing Test, its very idea has also been challengedby interesting counterarguments such as Searle’s Chinese Room.

Leaving such philosophical issues aside for a minute, what has changed now thatwe all are so excited about the possibility of intelligent machines? More and moredata has become available; more and more computing ability to crunch all the datais available everywhere at ever-reducing costs; and lots and lots of new patternrecognition algorithms, literally hundreds of them, have been developed. Even

xxvi Keynote Speaker

meta-algorithmic techniques such as ensembles are employed effectively to attack avast variety of problems.

Yet, what grand challenge problems has AI solved? Unlike board games andquizzes where it has in fact done well against humans, machines do not yetunderstand natural languages, cannot yet replace a household maid or a medicalnurse, or grow food for us. Yes, they are assisting human experts in many sig-nificant ways and are beginning to operate other machines as in driving a driverlesscar. In fact, whether AI has already even crashed two airplanes killing human livesis a topic of much speculation and debate at present.

Let us not be too eager to celebrate the successes of AI. Let AI first demonstratea lot more of human-like decision-making capability during these exciting times inthe second wave of development of machine intelligence.

Keynote Speaker xxvii

Contents

Artificial Intelligence

NLP-Driven Ensemble-Based Automatic Subtitle Generationand Semantic Video Summarization Technique . . . . . . . . . . . . . . . . . . 3V. B. Aswin, Mohammed Javed, Parag Parihar, K. Aswanth, C. R. Druval,Anupam Dagar, and C. V. Aravinda

A Generalized Model for Cardiovascular Disease ClassificationUsing Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 15Ankita Naik and Nitesh Naik

Classification of Road Accidents Using SVM and KNN . . . . . . . . . . . . 27P. Joyce Beryl Princess, Salaja Silas, and Elijah Blessing Rajsingh

A Deep Convolutional Encoder-Decoder Architecture Approachfor Sheep Weight Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Nirav Alpesh Shah, Jaydeep Thik, Chintan Bhatt,and Aboul-Ella Hassanien

Supervised Machine Learning Model for Accent Recognitionin English Speech Using Sequential MFCC Features . . . . . . . . . . . . . . 55Dweepa Honnavalli and S. S. Shylaja

A Two-Level Approach to Color Space-Based Image SegmentationUsing Genetic Algorithm and Feed-Forward Neural Network . . . . . . . 67B. S. Sathish, P. Ganesan, L. M. I. Leo Joseph, K. Palani,and R. Murugesan

Braille Cell Segmentation and Removal of Unwanted DotsUsing Canny Edge Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79Vishwanath Venkatesh Murthy, M. Hanumanthappa, and S. Vijayanand

xxix

Real-Time Detection of Distracted Drivers Using a Deep NeuralNetwork and Multi-threading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89Ajay Narayanan, V. Aiswaryaa, Aswesh T. Anand,and Nalinadevi Kadiresan

Analysing the Practicality of Drawing Inferences in Automationof Commonsense Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Chandan Hegde and K. Ashwini

Segmentation and Detection of Glioma Using Deep Learning . . . . . . . . 109Navneeth Krishna, Mahammad Rumaan Khalander,Nandan Shetty, and S. N. Bharath Bhushan

Character Recognition of Tulu Script Using Convolutional NeuralNetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121Sachin Bhat and G. Seshikala

Exploring the Performance of EEG Signal Classifiersfor Alcoholism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Nishitha Lakshmi, Rani Adhaduk, Nidarsh Nithyananda, S. RashwinNonda, and K. Pushpalatha

Type-2 Tetradecagonal Fuzzy Number . . . . . . . . . . . . . . . . . . . . . . . . . 149A. Rajkumar and C. Sagaya Nathan Stalin

Critical Path Problem Through Intuitionistic Triskaidecagonal FuzzyNumber Using Two Different Algorithms . . . . . . . . . . . . . . . . . . . . . . . 159N. Jose Parvin Praveena, C. Sagaya Nathan Stalin, and A. Rajkumar

Genetic-Neuro-Fuzzy Controller for Indirect Vector-ControlledInduction Motor Drive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169B. T. Venu Gopal, H. R. Ramesh, and E. G. Shivakumar

Artificial Intelligence-Based Chatbot Frameworkwith Authentication, Authorization, and Payment Features . . . . . . . . . 179Deena Deepika Cutinha, Niranjan N. Chiplunkar, Shazad Maved,and Arun Bhat

Disease Recognition in Sugarcane Crop Using Deep Learning . . . . . . . 189Hashmat Shadab Malik, Mahavir Dwivedi, S. N. Omkar, Tahir Javed,Abdul Bakey, Mohammad Raqib Pala, and Akshay Chakravarthy

Deep Learning-Based Car Damage Classification and Detection . . . . . 207Mahavir Dwivedi, Hashmat Shadab Malik, S. N. Omkar,Edgar Bosco Monis, Bharat Khanna, Satya Ranjan Samal, Ayush Tiwari,and Aditya Rathi

Sparse Reflectance Map-Based Fabric Characterization . . . . . . . . . . . . 223Kayan K. Katrak, Rithvik Chandan, Sirisha Lanka,G. M. Chitra, and S. S. Shylaja

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A Risk Assessment Model for Patients Suffering from CoronaryHeart Disease Using a Novel Feature Selection Algorithmand Learning Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237Sujata Joshi and Mydhili K. Nair

Toward Artificial Social Intelligence: A Semi-supervised, SplitDecoder Approach to EQ in a Conversational Agent . . . . . . . . . . . . . . 251Shruthi Shankar, V. Sruthi, Vibha Satyanarayana, and Bhaskarjyoti Das

Matrix Factorization for Recommendation System . . . . . . . . . . . . . . . . 267T. Lekshmi Priya and Harikumar Sandhya

A Reinforcement Learning Approach to Inventory Management . . . . . 281Apoorva Gokhale, Chirag Trasikar, Ankit Shah, Arpita Hegde,and Sowmiya Raksha Naik

Human Resource Working Prediction Based on LogisticRegression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299Anusha Hegde and G. Poornalatha

Kansei Knowledge-Based Human-Centric Digital Interface DesignUsing BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307Huiliang Zhao, Jian Lyu, Xiang Liu, and Weixing Wang

DST-ML-EkNN: Data Space Transformation with Metric Learningand Elite k-Nearest Neighbor Cluster Formation for Classificationof Imbalanced Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Seba Susan and Amitesh Kumar

Classification Study and Prediction of Cervical Cancer . . . . . . . . . . . . 329Kaushik Suresh

English Transliteration of Kannada Words with Anusvaraand Visarga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349Savitha Shetty, Saritha Shetty, Sarika Hegde, and Karuna Pandit

An Ensembled Scale-Space Model of Deep Convolutional NeuralNetworks for Sign Language Recognition . . . . . . . . . . . . . . . . . . . . . . . 363Neena Aloysius and M. Geetha

A Survey on Deep Learning-Based Automatic Text SummarizationModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377P. G. Magdum and Sheetal Rathi

Automatic Multi-disease Diagnosis and Prescription System UsingBayesian Network Approach for Clinical Decision Making . . . . . . . . . 393P. Laxmi, Deepa Gupta, G. Radhakrishnan, J. Amudha, and Kshitij Sharma

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Artificial Intelligence Techniques for Predicting Type 2 Diabetes . . . . . 411Ramyashree, P. S. Venugopala, Debmalya Barh, and B. Ashwini

Predictive Analysis of Malignant Disease in Woman Using MachineLearning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431Akshaya, R. Pranam Betrabet, and C. V. Aravinda

Study on Automatic Speech Therapy System for Patients . . . . . . . . . . 439Supriya B. Rao, Sarika Hegde, and Surendra Shetty

Data Engineering

The Design of Multiuser BGN Encryption with CustomizedMultiple Pollard’s Lambda Search Instances to Solve ECDLPin Finite Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457Santosh Javheri and Uday Kulkarni

Internet Addiction Predictor: Applying Machine Learningin Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471S. N. Suma, Poornima Nataraja, and Manoj Kumar Sharma

An Approach Toward Stateless Chatbots with the Benefitof Tensorflow Over Spacy Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483Chaithra, Roshan Fernandes, Anisha P. Rodrigues, and Venkatesh

Enhanced Processing of Input Data in Clustering Techniques of DataMining Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497K. Sampath Kini and B. H. Karthik Pai

A Comparative Analysis of MFIs in India Using ANOVAand Logistic Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503M. G. Deepika and P. Sarika

Practical Analysis of Representative Models in Classifier:A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517Angela Mathew and Sangeetha Jamal

Exponential Cipher Based on Residue Number Systemand Its Application to Image Security . . . . . . . . . . . . . . . . . . . . . . . . . 529Sagar Ramesh Pujar, Achal Ramanath Poonja, and Ganesh Aithal

Using Machine Learning and Data Analytics for Predicting Onsetof Cardiovascular Diseases—An Analysis of Current State of Art . . . . 543P. R. Mahalingam and J. Dheeba

Analysis of the Nearest Neighbor Classifiers: A Review . . . . . . . . . . . . 559Yash Agarwal and G. Poornalatha

Analysis of Automated Log Template Generation Methodologies . . . . . 571Anoop Mudholkar, Varun Mokhashi, Deepak Nayak,Vaishnavi Annavarjula, and Mahesh Babu Jayaraman

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Fraud Detection in Online Transactions Using Machine LearningApproaches—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589H. Dhanushri Nayak, Deekshita, L. Anvitha, Anusha Shetty,Divya Jennifer D’Souza, and Minu P. Abraham

Encryption and Decryption for Network Security Using ReverseContext-Free Grammar Productions . . . . . . . . . . . . . . . . . . . . . . . . . . . 601Aishwarya R. Parab and Teslin Jacob

A Survey on State-of-the-Art Applications of Variable LengthChromosome (VLC) Based GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615Ravi Domala and Upasna Singh

A Multi-level Access Technique for Privacy-Preserving Perturbationin Association Rule Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631N. Komal Kumar and D. Vigneswari

LSB and RLE Based Approach for Increasing Payload and Securityof Stego Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647Rupali Sanjay Pawar

Adaptive MoD Chatbot: Toward Providing Contextual CorporateSummarized Document as Suggestions and Reported IssueTicket Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659Shiva Prasad Nayak, Archana Rai, Kiran Vankataramanappa,Jalak Arvindkumar Pansuriya, and Joerg Singler

Classification of Text Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675Pushpa B. Patil and Dakshayani M. Ijeri

Fine-Grained Sentiment Rating of Online Reviewswith Deep-RNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687Ramesh Wadawadagi and Veerappa Pagi

Analysis of Strategic Market Management in Light of StochasticProcesses, Recurrence Relation, Abelian Group and Expectation . . . . . 701Prasun Chakrabarti, Tulika Chakrabarti, Siddhant Bane, Biswajit Satpathy,Indranil SenGupta, and Jonathan Andrew Ware

Peer-to-Peer Distributed Storage Using InterPlanetary File System . . . 711A. Manoj Athreya, Ashwin A. Kumar, S. M. Nagarajath, H. L. Gururaj,V. Ravi Kumar, D. N. Sachin, and K. R. Rakesh

Knowledge Base Representation of Emails Using Ontologyfor Spam Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723V. Bindu and Ciza Thomas

Clinical Significance of Measles and Its Prediction Using Data MiningTechniques: A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737Abhishek S. Rao, Demian Antony D’Mello, R. Anand, and Sneha Nayak

Contents xxxiii

A Survey on Graphical Authentication System Resisting ShoulderSurfing Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761S. Arun Kumar, R. Ramya, R. Rashika, and R. Renu

Analysis of Stock Market Fluctuations Incidental to InternetTrends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 771Vinayaka R. Kamath, Nikhil V. Revankar, and Gowri Srinivasa

Pseudo Random Number Generation Based on Genetic AlgorithmApplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793V. Pushpalatha, K. B. Sudeepa, and H. N. Mahendra

Analysis of an Enhanced Dual RSA Algorithm Using Pell’s Equationto Hide Public Key Exponent and a Fake Modulus to AvoidFactorization Attack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809K. R. Raghunandan, Rovita Robert Dsouza, N. Rakshith, Surendra Shetty,and Ganesh Aithal

A New Approach on Advanced Encryption Standards to Improvethe Secrecy and Speed Using Nonlinear Output Feedback Mode . . . . . 825Dodmane Radhakrishna, Aithal Ganesh, and Shetty Surendra

Cyber-Bullying Detection: A Comparative Analysisof Twitter Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 841Jyothi Shetty, K. N. Chaithali, Aditi M. Shetty, B. Varsha, and V. Puthran

An Optimal Wavelet Detailed-Coefficient DeterminationUsing Time-Series Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857C. I. Johnpaul, Munaga V. N. K. Prasad, S. Nickolas, G. R. Gangadharan,and Marco Aiello

A Novel Data Hiding Technique with High ImperceptibilityUsing a 3-Input Majority Function and an Optimal PixelAdjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 873P. V. Sabeen Govind, M. Y. Shiju Thomas, and M. V. Judy

Designing and Testing of Data Acquisition System for SatelliteUsing MIL-STD-1553 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 883B. L. Lavanya and M. N. Srinivasa

Optimizing People Sourcing Through Semantic Matching of JobDescription Documents and Candidate Profile Using Improved TopicModelling Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899Lorick Jain, M. A. Harsha Vardhan, Ganesh Kathiresan,and Ananth Narayan

Mining Associations Rules Between Attribute Value Clusters . . . . . . . . 909Shankar B. Naik

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Machine Learning Approach to Stock Prediction and Analysis . . . . . . 919Bhal Chandra Ram Tripathi, T. Satish Kumar, R. Krishna Prasad,and Visheshwar Pratap Singh

A Novel Approach for Error Analysis in Classified Big Datain Health Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 929S. Kavitha, Mahesh S. Nayak, and M. Hanumanthappa

Multi-join Query Optimization Using Modified ACO with GA . . . . . . . 937Vikas Kumar and Mantosh Biswas

The Impact of Distance Measures in K-Means Clustering Algorithmfor Natural Color Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 947P. Ganesan, B. S. Sathish, L. M. I. Leo Joseph, K. M. Subramanian,and R. Murugesan

Designing an Adaptive Question Bank and Question PaperGeneration Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 965Pankaj Dwivedi, R. Tapan Shankar, B. Meghana, H. Sushaini,B. R. Sudeep, and M. R. Pooja

Securing Media Information Using Hybrid Transposition UsingFisher Yates Algorithm and RSA Public Key Algorithm Using Pell’sCubic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 975K. R. Raghunandan, Shirin Nivas Nireshwalya, Sharan Sudhir,M. Shreyank Bhat, and H. M. Tanvi

Analysis of Tuberculosis Disease Using Association Rule Mining . . . . . 995Ankita Mohapatra, Sangita Khare, and Deepa Gupta

Scalable Two-Phase Top-Down Specification for Big DataAnonymization Using Apache Pig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1009Anushree Raj and Rio D’Souza

Segmentation of Lip Print Images Using Clustering andThresholding Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1023S. Sandhya, Roshan Fernandes, S. Sapna, and Anisha P. Rodrigues

Filtering-Based Text Sentiment Analysis for Twitter Dataset . . . . . . . . 1035Hiran Nandy and Rajeswari Sridhar

A Comparative Analysis of Clustering Quality Based on InternalValidation Indices for Dimensionally Reduced Social Media Data . . . . 1047Shini Renjith, A. Sreekumar, and M. Jathavedan

Anomaly Detection for Big Data Using Efficient Techniques:A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1067Divya Jennifer D’Souza and K. R. Uday Kumar Reddy

Contents xxxv

Data Science and Internet of Things for Enhanced RetailExperience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1081Irfan Landge and Hannan Satopay

Machine Vision

An Experimental Study on the Effect of Noise in CCITT Group 4Compressed Document Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1101A. Narayana Sukumara, Mohammed Javed, D. K. Sreekantha,P. Nagabhushan, and R. Amarnath

A Neck-Floor Distance Analysis-Based Fall Detection SystemUsing Deep Camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1113Xiangbo Kong, Zelin Meng, Lin Meng, and Hiroyuki Tomiyama

An Introduction to Sparse Sampling on Audio Signal by ExploringDifferent Basis Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121A. Electa Alice Jayarani, Mahabaleswara Ram Bhatt, and D. D. Geetha

Retrieval of Facial Sketches Using Linguistic Descriptors:An Approach Based on Hierarchical Classification of FacialAttributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1131S. Pallavi, M. S. Sannidhan, and Abhir Bhandary

Simplified SVD Feature Construction in Multiangle Imagesto Identify Plant Varieties and Weed Infestation . . . . . . . . . . . . . . . . . 1151K. Ramesh and Andrews Samraj

Old Handwritten Music Symbol Recognition Using Radonand Discrete Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165Savitri Apparao Nawade, Rajmohan Pardeshi, Shivanand Rumma,and Mallikarjun Hangarge

Gender Recognition from Face Images Using SIFT Descriptorsand Trainable Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1173Sneha Pai and Ramesha Shettigar

Multiscale Anisotropic Morlet Wavelet for Texture Classificationof Interstitial Lung Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1187Manas Jyoti Das and Lipi B. Mahanta

A Review of Intelligent Smartphone-Based Object DetectionTechniques for Visually Impaired People . . . . . . . . . . . . . . . . . . . . . . . 1199R. Devakunchari, Swapnil Tiwari, and Harsh Seth

Stereo Vision-Based Depth Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 1209Zelin Meng, Xiangbo Kong, Lin Meng, and Hiroyuki Tomiyama

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A Dynamic Programming Algorithm for Energy-Aware Routingof Delivery Drones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1217Yusuke Funabashi, Atsuya Shibata, Shunsuke Negoro, Ittetsu Taniguchi,and Hiroyuki Tomiyama

Qualitative Approach of Empirical Mode Decomposition-BasedTexture Analysis for Assessing and Classifying the Severityof Alzheimer’s Disease in Brain MRI Images . . . . . . . . . . . . . . . . . . . . 1227K. V. Sudheesh and L. Basavaraj

Facial Image Indexing Using Locally Extracted Sparse Vectors . . . . . . 1255Vinayaka R. Kamath, M. Varun, and S. Aswath

Ambient Intelligence

Smart Agro-Ecological Zoning for Crop Suggestion and PredictionUsing Machine Learning: An Comprehensive Review . . . . . . . . . . . . . 1273R. Chetan, D. V. Ashoka, and B. V. Ajay Prakash

Preparedness in the Aftermath of a Natural Disaster Using MultihopAd hoc Networks—Drone-Based Approach . . . . . . . . . . . . . . . . . . . . . 1281Getzi Jeba Leelipushpam Paulraj, Immanuel Johnraja Jebadurai,and J. Jebaveerasingh

An IoT-Based Congestion Control Framework for Intelligent TrafficManagement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1287Md. Ashifuddin Mondal and Zeenat Rehena

Link Prediction on Social Attribute Network Using Lévy FlightFirefly Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1299P. Srilatha, R. Manjula, and C. Pavan Kumar

Secure and Energy-Efficient Data Transmission . . . . . . . . . . . . . . . . . . 1311H. V. Chaitra and G. K. RaviKumar

A Non-cooperative Game Theoretic Approach for ResourceAllocation in D2D Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1323Tanya Shrivastava, Sudhakar Pandey, Pavan Kumar Mishra,and Shrish Verma

IoT-Based Nursery Management System . . . . . . . . . . . . . . . . . . . . . . . 1335Mahendra S. Naik, Sreekantha Desai,K. V. S. S. S. S. Sairam, and S. N. Chaitra

Shortest Path Discovery for Area Coverage (SPDAC)Using Prediction-Based Clustering in WSN . . . . . . . . . . . . . . . . . . . . . 1345C. N. Abhilash, S. H. Manjula, R. Tanuja, and K. R. Venugopal

Contents xxxvii

Smart Mirror Using Raspberry Pi for Intrusion Detectionand Human Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1359Raju A. Nadaf and Vasudha Bonal

A Home Security Camera System Based on Cloud and SNS . . . . . . . . 1375Takuya Egashira, Lin Meng, and Hiroyuki Tomiyama

Design, Calibration, and Experimental Study of Low-CostResistivity-Based Soil Moisture Sensor for Detecting Moistureat Different Depths of a Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1383S. Sunil Kumar, Ganesh Aithal, and P. Venkatramana Bhat

An IoT-Based Predictive Analytics for Estimation of Rainfallfor Irrigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1399H. Shalini and C. V. Aravinda

Smart Watering System Using MQTT Protocol in IoT . . . . . . . . . . . . 1415Mukambikeshwari and Asmita Poojary

Internet of Things (IoT) Enabling Technologiesand Applications—A Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1425D. K. Sreekantha, Ashok Koujalagi,T. M. Girish, and K. V. S. S. S. S. Sairam

Evaluation of Standard Models of Content Placement in Cloud-BasedContent Delivery Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1443Suman Jayakumar, S. Prakash, and C. B. Akki

IoT-Based Data Storage for Cloud Computing Applications . . . . . . . . 1455Ankita Shukla, Priyatam Reddy Somagattu, Vishal Krishna Singh,and Mala Kalra

IoT-Based Heart Rate Monitoring System . . . . . . . . . . . . . . . . . . . . . . 1465Jagadevi N. Kalshetty, P. Melwin Varghese, K. Karthik, Randhir Raj,and Nitin Yadav

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About the Editors

Dr. Niranjan N. Chiplunkar is currently a Professorat the Department of Computer Science & Engineeringand Principal of NMAM Institute of Technology, Nitte,Udupi, India. With more than 32 years of teachingexperience, he has written one textbook on “VLSICAD” published by PHI Learning, in 2011 and hasedited two international and one national-level confer-ence proceedings volume. He was selected for the“Bharatiya Vidya Bhavan National Award for BestEngineering College Principal” for the year 2014 by theISTE New Delhi and for the “Excellent AchievementAward” by the Centre for International Cooperation onComputerization, Government of Japan, in 2002. Prof.Chiplunkar’s major interests include CAD for VLSI,wireless sensor networks, and multicore architectureand programming. He is a Fellow of the Institution ofEngineers (India), Senior Member of the IEEE, and amember of several other professional bodies, e.g. theComputer Society of India, ISSS, and ISTE. He hassuccessfully completed two funded research projects—one from the AICTE, New Delhi, and another fromthe DST, Government of India, on “Network onChip Architecture Design” and “Multicore SoftwareFramework Development,” respectively.

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Dr. Takanori Fukao is currently a Professor at theDepartment of Electrical and Electronics Engineering,College of Science and Engineering, RitsumeikanUniversity, Japan. His primary research interestsinclude perceptual information processing, intelligentrobotics, automated driving, platooning, parking ofautomobiles, flight control for blimp robots and drones,automated driving of agricultural vehicles, activecontrol including active suspension systems, and 3Dmodel generation using motion stereo or stereo cam-eras. He heads the research laboratory IntelligentVehicle Systems. He has published 47 research papersin reputed international journals, and three books withleading publishers. He has received several awards forhis research and teaching achievements. Currently, he isan editor of IEEE Transactions on Intelligent Vehicles.

xl About the Editors