Computational intelligence in data mining

Himansu Sekhar Behera Department of Computer Science and Engineering & Information Technology Veer Surendra Sai University of Technology Sri Sivani College of Engineering SSCE Srikakulam

Advances in Intelligent Systems and Computing 711

Himansu Sekhar Behera

Janmenjoy Nayak

Bighnaraj Naik

Ajith Abraham Editors

Computational Intelligence in Data Mining

Proceedings of the International

Conference on CIDM 2017

Advances in Intelligent Systems and Computing

Volume 711

Series editor

Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

e-mail: kacprzyk@ibspan.waw.pl

The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, arti ficial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia.

The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings

of important conferences, symposia and congresses They cover signi ficant recent developments in the field, both of a foundational and applicable character An important characteristic feature of the series is the short publication time and world-wide distribution This permits a rapid and broad dissemination of research results.

Himansu Sekhar Behera

Himansu Sekhar Behera

Department of Computer Science and

Engineering & Information Technology

Veer Surendra Sai University of Technology

Sri Sivani College of Engineering (SSCE)

Srikakulam, Andhra Pradesh

India

Bighnaraj Naik

Department of Computer Application

Veer Surendra Sai University of Technology

Sambalpur, Odisha

India

Ajith AbrahamMachine Intelligence Research (MIR) LabAuburn, WA

USAandTechnical University of OstravaOstrava

Czech Republic

ISSN 2194-5357 ISSN 2194-5365 (electronic)

Advances in Intelligent Systems and Computing

ISBN 978-981-10-8054-8 ISBN 978-981-10-8055-5 (eBook)

https://doi.org/10.1007/978-981-10-8055-5

Library of Congress Control Number: 2017964255

© Springer Nature Singapore Pte Ltd 2019

This book was advertised with a copyright holder The Editor(s)/The Author(s) in error, whereas the publisher holds the copyright.

This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part

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In the next decade, the growth of data both structured and unstructured will presentchallenges as well as opportunities for industries and academia The present sce-nario of storage of the amount of data is quite huge in the modern database due tothe availability and popularity of the Internet Thus, the information needs to besummarized and structured in order to maintain effective decision-making With theexplosive growth of data volumes, it is essential that real-time information that is ofuse to the business can be extracted to deliver better insights to decision-makers,understand complex patterns, etc When the quantity of data, dimensionality andcomplexity of the relations in the database are beyond human capacities, there is arequirement for intelligent data analysis techniques, which could discover usefulknowledge from data While data mining evolves with innovative learning algo-rithms and knowledge discovery techniques, computational intelligence harnessesthe results of data mining for becoming more intelligent than ever In the presentscenario of computing, computational intelligence tools offer adaptive mechanismsthat enable the understanding of data in complex and changing environments.The Fourth International Conference on “Computational Intelligence in DataMining (ICCIDM 2017)” is organized by Veer Surendra Sai University of Tech-nology (VSSUT), Burla, Sambalpur, Odisha, India, during 11–12 November 2017.ICCIDM is an international forum for representation of research and developments

in the fields of data mining and computational intelligence More than 250prospective authors submitted their research papers to the conference After athorough double-blind peer review process, editors have selected 78 papers Theproceedings of ICCIDM is a mix of papers from some latestfindings and research

of the authors It is being a great honour for us to edit the proceedings We haveenjoyed considerably working in cooperation with the international advisory,programme and technical committee to call for papers, review papers andfinalizepapers to be included in the proceedings

This international conference on CIDM aims at encompassing new breed ofengineers, technologists making it a crest of global success All the papers arefocused on the thematic presentation areas of the conference, and they have pro-vided ample opportunity for presentation in different sessions Research in data

v

mining has its own history But, there is no doubt about the tips and furtheradvancements in the data mining areas will be the main focus of the conference.This year’s programme includes exciting collections of contributions resulting from

a successful call for papers Apart from those, two special sessions namedputational Intelligence in Data Analytics” and “Applications of ComputationalIntelligence in Power and Energy Systems” have been proposed for more discus-sions on the theme-related areas The selected papers have been divided into the-matic areas including both review and research papers and highlight the currentfocus on computational intelligence techniques in data mining

“Com-We hope the author’s own research and opinions add value to it First andforemost are the authors of papers, columns and editorials whose works have madethe conference a great success We had a great time putting together this pro-ceedings The ICCIDM conference and proceedings are a credit to a large group ofpeople, and everyone should be congratulated for the outcome We extend our deepsense of gratitude to all those for their warm encouragement, inspiration andcontinuous support for making it possible

We hope all of us will appreciate the good contributions made and justify ourefforts

Sambalpur, India Himansu Sekhar BeheraSrikakulam, India Janmenjoy NayakSambalpur, India Bighnaraj NaikAuburn, USA/Ostrava, Czech Republic Ajith Abraham

Conference Committee

Chief Patron and President

Prof E Saibaba Reddy, Vice Chancellor, VSSUT, B.Tech., M.E (Hons.) (Roorkee),Ph.D (Nottingham, UK), Postdoc (Halifax, Canada), Postdoc (Birmingham, UK)

Honorary Advisory Chair

Prof S K Pal, Sr., Member IEEE, LFIEEE, FIAPR, FIFSA, FNA, FASc, FNASc,FNAE, Distinguished Scientist and Former Director, Indian Statistical Institute,India

Prof V E Balas, Sr., Member IEEE, Aurel Vlaicu University, Romania

Honorary General Chair

Prof Rajib Mall, Indian Institute of Technology (IIT) Kharagpur, India

Prof P K Hota, Dean, CDCE, VSSUT, India

General Chair

Prof Ashish Ghosh, Indian Statistical Institute, Kolkata, India

Prof B K Panigrahi, Indian Institute of Technology (IIT) Delhi, India

Programme Chair

Dr H S Behera, Veer Surendra Sai University of Technology (VSSUT), Burla,Odisha, India

vii

Chairman, Organizing Committee

Prof Amiya Ku Rath, HOD, Department of CSE and IT, Veer Surendra SaiUniversity of Technology (VSSUT), Burla, Odisha, India

Vice-Chairman, Organizing Committee

Dr S K Padhy, HOD, Department of Computer Application, Veer Surendra SaiUniversity of Technology (VSSUT), Burla, Odisha, India

Convenor

Dr Bighnaraj Naik, Department of Computer Application, Veer Surendra SaiUniversity of Technology (VSSUT), Burla, Odisha, India

International Advisory Committee

Prof A Abraham, Machine Intelligence Research Labs, USA

Prof Dungki Min, Konkuk University, Republic of Korea

Prof Francesco Marcelloni, University of Pisa, Italy

Prof Francisco Herrera, University of Granada, Spain

Prof A Adamatzky, Unconventional Computing Centre, UWE, UK

Prof H P Proença, University of Beira Interior, Portugal

Prof P Mohapatra, University of California

Prof S Naik, University of Waterloo, Canada

Prof George A Tsihrintzis, University of Piraeus, Greece

Prof Richard Le, La Trobe University, Australia

Prof Khalid Saeed, AUST, Poland

Prof Yew-Soon Ong, Singapore

Prof Andrey V Savchenko, NRU HSE, Russia

Prof P Mitra, P.S University, USA

Prof D Sharma, University of Canberra, Australia

Prof Istvan Erlich, University of Duisburg-Essen, Germany

Prof Michele Nappi, University of Salerno, Italy

Prof Somesh Jha, University of Wisconsin, USA

Prof Sushil Jajodia, George Mason University, USA

Prof S Auephanwiriyakul, Chiang Mai University, Thailand

Prof Carlos A Coello Coello, Mexico

Prof M Crochemore, University de Marne-la-Vallée, France

Prof T Erlebach, University of Leicester, Leicester, UK

Prof T Baeck, Universiteit Leiden, Leiden, The Netherlands

Prof J Biamonte, ISI Foundation, Torino, Italy

Prof C S Calude, University of Auckland, New Zealand

Prof P Degano, Università di Pisa, Pisa, Italy

Prof Raouf Boutaba, University of Waterloo, Canada

Prof Kenji Suzuki, University of Chicago

Prof Raj Jain, WU, USA

Prof D Al-Jumeily, Liverpool J Moores University, UK

Prof M S Obaidat, Monmouth University, USA

Prof P N Suganthan, NTU, Singapore

Prof Biju Issac, Teesside University, UK

Prof Brijesh Verma, CQU, Australia

Prof Ouri E Wolfson, University of Illinois, USA

Prof Klaus David, University of Kassel, Germany

Prof M Dash, NTU, Singapore

Prof L Kari, Western University, London, Canada

Prof A S M Sajeev, Australia

Prof Tony Clark, MSU, UK

Prof Sanjib ku Panda, NUS, Singapore

Prof R C Hansdah, IISC Bangalore

Prof G Chakraborty, Iwate Prefectural University, Japan

Prof Atul Prakash, University of Michigan, USA

Prof Sara Foresti, University of degli Studi di Milano, Italy

Prof Pascal Lorenz, University of Haute Alsace, France

Prof G Ausiello, University di Roma“La Sapienza”, Italy

Prof X Deng, University of Liverpool, England, UK

Prof Z Esik, University of Szeged, Szeged, Hungary

Prof A G Barto, University of Massachusetts, USA

Prof G Brassard, University de Montréal, Montréal, Canada

Prof L Cardelli, Microsoft Research, England, UK

Prof A E Eiben, VU University, The Netherlands

Prof Patrick Siarry, Université de Paris, Paris

Prof R Herrera Lara, EEQ, Ecuador

Prof M Murugappan, University of Malaysia

National Advisory Committee

Prof P K Pradhan, Registrar, VSSUT, Burla

Prof R P Panda, VSSUT, Burla

Prof A N Nayak, Dean, SRIC, VSSUT, Burla

Prof D Mishra, Dean, Students’ Welfare, VSSUT, Burla

Prof P K Kar, Dean, Faculty & Planning, VSSUT, Burla

Prof P K Das, Dean, Academic Affairs, VSSUT, Burla

Prof S K Swain, Dean, PGS&R, VSSUT, Burla

Prof S Panda, Coordinator TEQIP, VSSUT, Burla

Prof D K Pratihar, IIT Kharagpur

Prof K Chandrasekaran, NIT Karnataka

Prof S G Sanjeevi, NIT Warangal

Prof G Saniel, NIT Durgapur

Prof B B Amberker, NIT Warangal

Prof R K Agrawal, JNU, New Delhi

Prof U Maulik, Jadavpur University

Prof Sonajharia Minz, JNU, New Delhi

Prof K K Shukla, IIT, BHU

Prof A V Reddy, JNTU, Hyderabad

Prof A Damodaram, Sri Venkateswara University

Prof C R Tripathy, SU, Odisha

Prof P Sanyal, WBUT, Kolkata

Prof G Panda, IIT, BBSR

Prof B B Choudhury, ISI Kolkata

Prof G C Nandy, IIIT Allahabad

Prof R C Hansdah, IISC Bangalore

Prof S K Basu, BHU, India

Prof J V R Murthy, JNTU, Kakinada

Prof D V L N Somayajulu, NIT Warangal

Prof G K Nayak, IIIT, BBSR

Prof P P Choudhury, ISI Kolkata

Prof D Vijaya Kumar, AITAM, Srikakulam

Prof Sipra Das Bit, IIEST, Kolkata

Prof S Bhattacharjee, NIT Surat

Technical Committee Members

Dr Adel M Alimi, REGIM-Lab, ENIS, University of Sfax, Tunisia

Dr Chaomin Luo, University of Detroit Mercy Detroit, Michigan, USA

Dr Istvan Erlich, Department of EE & IT, University of Duisburg-Essen, Germany

Dr Tzyh Jong Tarn, Washington University in St Louis, USA

Dr Simon X Yang, University of Guelph, Canada

Dr Raffaele Di Gregorio, University of Ferrara, Italy

Dr Kun Ma, Shandong Provincial Key Laboratory of Network Based IntelligentComputing, University of Jinan, China

Dr Azah Kamilah Muda, Faculty of ICT, Universiti Teknikal Malaysia Melaka,Malaysia

Dr Biju Issac, Teesside University, Middlesbrough, England, UK

Dr Bijan Shirinzadeh, Monash University, Australia

Dr Enver Tatlicioglu, Izmir Institute of Technology, Turkey

Dr Hajime Asama, The University of Tokyo, Japan

Dr N P Padhy, Department of EE, IIT Roorkee, India

Dr Ch Satyanarayana, Department of Computer Science and Engineering, JNTUKakinada, India

Dr B Majhi, Department of Computer Science and Engineering, NIT Rourkela,India

Dr M Murugappan, School of Mechatronic Engineering, University MalaysiaPerlis, Perlis, Malaysia

Dr Kashif Munir, King Fahd University of Petroleum and Minerals, Hafr Al-BatinCampus, Kingdom of Saudi Arabia

Dr L Sumalatha, Department of Computer Science and Engineering, JNTUKakinada, India

Dr K N Rao, Department of Computer Science and Engineering, AndhraUniversity, Visakhapatnam, India

Dr S Das, Indian Statistical Institute, Kolkata, India

Dr D P Mohaptra, National Institute of Technology (NIT) Rourkela, India

Dr A K Turuk, Head, Department of CSE, NIT RKL, India

Dr M P Singh, Department of CSE, NIT Patna, India

Dr R Behera, Department of EE, IIT Patna, India

Dr P Kumar, Department of CSE, NIT Patna, India

Dr A Das, Department of CSE, IIEST, WB, India

Dr J P Singh, Department of CSE, NIT Patna, India

Dr M Patra, Berhampur University, Odisha, India

Dr A Deepak, Department of CSE, NIT Patna, India

Dr D Dash, Department of CSE, NIT Patna, India

Publication Chair

Dr Janmenjoy Nayak, Sri Sivani College of Engineering, Srikakulam, AndhraPradesh, India

Special Session Chairs

Special Session on “Computational Intelligence in Data Analysis”:

Dr Asit Kumar Das, Indian Institute of Engineering Science and Technology(IIEST), IIIT Shibpur, WB, India

Dr Imon Mukherjee, International Institute of Information Technology (IIIT)Kalyani, WB, India

Special Session on “Computational Intelligence in Power & Energy Systems”

Prof (Dr.) P K Hota, Veer Surendra Sai University of Technology (VSSUT),Odisha, India

Dr Sasmita Behera, Veer Surendra Sai University of Technology (VSSUT),Odisha, India

Publicity Chair

Prof P C Swain, VSSUT, Burla

Dr Santosh Kumar Majhi, VSSUT, Burla

Mrs E Oram, VSSUT, Burla

Registration Chair

Dr Sucheta Panda, VSSUT, Burla

Mrs Sasmita Acharya, VSSUT, Burla

Mrs Sasmita Behera, VSSUT, Burla

Ms Gargi Bhattacharjee, VSSUT, Burla

Mrs Santi Behera, VSSUT, Burla

Sponsorship Chair

Dr Satyabrata Das, VSSUT, Burla

Mr Sanjaya Kumar Panda, VSSUT, Burla

Mr Sujaya Kumar Sathua, VSSUT, Burla

Mr Gyanaranjan Shial, VSSUT, Burla

Web Chair

Mr D C Rao, VSSUT, Burla

Mr Kishore Kumar Sahu, VSSUT, Burla

Mr Suresh Kumar Srichandan, VSSUT, Burla

Organizing Committee Members

Dr Manas Ranjan Kabat, VSSUT, Burla

Dr Rakesh Mohanty, VSSUT, Burla

Dr Suvasini Panigrahi, VSSUT, Burla

Dr Manas Ranjan Senapati, VSSUT, Burla

Dr P K Sahu, VSSUT, Burla

Mr Satya Prakash Sahoo, VSSUT, Burla

Mr Sanjib Nayak, VSSUT, Burla

Ms Sumitra Kisan, VSSUT, Burla

Mr Pradipta Kumar Das, VSSUT, Burla

Mr Atul Vikas Lakra, VSSUT, Burla

Ms Alina Mishra, VSSUT, Burla

Ms Alina Dash, VSSUT, Burla

Dr M K Patel, VSSUT, Burla

Mr D P Kanungo, VSSUT, Burla

Mr D Mishra, VSSUT, Burla

Mr S Mohapatra, VSSUT, Burla

International Reviewer Committee

Arun Agarwal, Siksha‘O’ Anusandhan University, Odisha, India

M Marimuthu, Coimbatore Institute of Technology, Coimbatore, India

Sripada Rama Sree, Aditya Engineering College (AEC), Surampalem, AndhraPradesh, India

Harihar Kalia, Seemanta Engineering College, Odisha, India

A S Aneeshkumar, Alpha Arts and Science College, Chennai, Tamil Nadu, IndiaKauser Ahmed P., VIT University, Vellore, Tamil Nadu, India

Ajanta Das, Birla Institute of Technology, Mesra, India

Manuj Darbari, BBD University, Lucknow, India

Manoj Kumar Patel, Veer Surendra Sai University of Technology, Odisha, IndiaNagaraj V Dharwadkar, Rajarambapu Institute of Technology, Maharashtra, IndiaChandan Jyoti Kumar, Department of Computer Science and Information Tech-nology, Cotton College State University, Assam, India

Biswapratap Singh Sahoo, Department of Electrical Engineering, National TaiwanUniversity, Taipei, Taiwan

S P Tripathy, NIT Durgapur, West Bengal, India

Suma V., Dean, Dayananda Sagar College of Engineering, Odisha, India

R Patel, BVM Engineering College, Gujarat, India

P Sivakumar, SKP Engineering College, Tamil Nadu, India

Partha Garai, Kalyani Government Engineering College, Kalyani, West Bengal,India

Anuranjan Misra, Noida International University, India

K G Srinivasagan, National Engineering College, Tamil Nadu, India

Jyotismita Chaki, Jadavpur University, West Bengal, India

Sawon Pratiher, IIT Kharagpur, India

R V S Lalitha, Aditya College of Engineering and Technology, Surampalem,India

G Rosline Nesa Kumari, Saveetha University, Chennai, Tamil Nadu, IndiaNarayan Joshi, Parul University, Vadodara, India

S Vaithyasubramanian, Sathyabama University, Chennai, Tamil Nadu, IndiaAbhishek Kumar, Lovely Professional University, Punjab, India

Prateek Agrawal, Lovely Professional University, Punjab, India

Sumanta Panda, Veer Surendra Sai University of Technology, Odisha, IndiaJayakishan Meher, Centurion University of Technology and Management, Odisha,India

Balakrushna Tripathy, VIT University, Tamil Nadu, India

Rajan Patel, Sankalchand Patel College of Engineering, NG, India

Narender Singh, Chhaju Ram Memorial Jat College, Haryana, India

P M K Prasad, GMR Institute of Technology, Andhra Pradesh, India

Chhabi Rani Panigrahi, Central University Rajasthan, Gujarat, India

Srinivas Sethi, Indira Gandhi Institute of Technology, Odisha, India

Mahmood Ali Mirza, DMS SVH College of Engineering, Andhra Pradesh, India

P Kumar, Zeal College of Engineering and Research, Pune, India

B K Sarkar, BIT Mesra, Ranchi, India

Trilochan Panigrahi, National Institute of Technology, Goa, India

Deepak D Kshirsagar, College of Engineering Pune (COEP), Pune, IndiaRahul Paul, University of South Florida, USA

Maya V Karki, Professor, MSRIT, Bangalore, India

A Anny Leema, B S Abdur Rahman University, Chennai, India

S Logeswari, Bannari Amman Institute of Technology, Sathyamangalam, TamilNadu, India

R Gomathi, Bannari Amman Institute of Technology, Sathyamangalam, TamilNadu, India

R Kavitha, Sastra University, Thanjavur, Tamil Nadu, India

S Das, Veer Surendra Sai University of Technology, Odisha, India

S K Majhi, Veer Surendra Sai University of Technology, Odisha, India

Sucheta Panda, Veer Surendra Sai University of Technology, Odisha, India

P K Hota, Veer Surendra Sai University of Technology, Odisha, India

G T Chandrasekhar, Sri Sivani College of Engineering, Srikakulam, AndhraPradesh, India

Janmenjoy Nayak, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh,India

Bighnaraj Naik, Veer Surendra Sai University of Technology, Odisha, India

The theme and relevance of ICCIDM attracted more than 250 researchers/academicians around the globe which enabled us to select good quality papers andserve to demonstrate the popularity of the ICCIDM conference for sharing ideasand researchfindings with truly national and international communities Thanks toall those who have contributed in producing such a comprehensive conferenceproceedings of ICCIDM

The organizing committee believes and trusts that we have been true to the spirit

of collegiality that members of ICCIDM value even as also maintaining an elevatedstandard as we have reviewed papers, provided feedback and presented a strongbody of published work in this collection of proceedings Thanks to all the members

of the organizing committee for their heartfelt support and cooperation

After the three successful versions of ICCIDM, it has indeed been an honour for

us to edit the proceedings of this fourth series of ICCIDM We have been fortunateenough to work in cooperation with a brilliant international as well as nationaladvisory board, reviewers, and programme and technical committee consisting ofeminent academicians to call for papers, review papers and finalize papers to beincluded in the proceedings

We would like to express our heartfelt gratitude and obligations to the benignreviewers for sparing their valuable time, putting an effort to review the papers in astipulated time and providing their valuable suggestions and appreciation inimprovising the presentation, quality and content of this proceedings The eminence

of these papers is an accolade not only to the authors but also to the reviewers whohave guided towards perfection

Last but not least, the editorial members of Springer Publishing deserve a specialmention and our sincere thanks to them not only for making our dream come true inthe shape of this proceedings, but also for its hassle-free and in-time publication inthe reputed Advances in Intelligent Systems and Computing, Springer

The ICCIDM conference and proceedings are a credit to a large group of people,and everyone should be proud of the outcome

xvii

BER Performance Analysis of Image Transmission Using OFDM

Technique in Different Channel Conditions Using Various

Modulation Techniques 1Arun Agarwal, Binayak Satish Kumar and Kabita Agarwal

On Understanding the Release Patterns of Open Source

Java Projects 9Arvinder Kaur and Vidhi Vig

A Study of High-Dimensional Data Imputation Using Additive

LASSO Regression Model 19

K Lavanya, L S S Reddy and B Eswara Reddy

An Efficient Multi-keyword Text Search Over Outsourced

Encrypted Cloud Data with Ranked Results 31Prabhat Keshari Samantaray, Navjeet Kaur Randhawa

and Swarna Lata Pati

Robust Estimation of IIR System ’s Parameter Using Adaptive

Particle Swarm Optimization Algorithm 41Meera Dash, Trilochan Panigrahi and Renu Sharma

A Shallow Parser-based Hindi to Odia Machine

Translation System 51Jyotirmayee Rautaray, Asutosh Hota and Sai Sankar Gochhayat

Coherent Method for Determining the Initial Cluster Center 63Bikram Keshari Mishra and Amiya Kumar Rath

Digital Image Watermarking Using (2, 2) Visual Cryptography

with DWT-SVD Based Watermarking 77Kamal Nayan Kaur, Divya, Ishu Gupta and Ashutosh Kumar Singh

xix

Modeling of Nexa-1.2kW Proton Exchange Membrane Fuel Cell

Power Supply Using Swarm Intelligence 87Tata Venkat Dixit, Anamika Yadav and Shubhrata Gupta

Survey of Different Load Balancing Approach-Based Algorithms

in Cloud Computing: A Comprehensive Review 99Arunima Hota, Subasish Mohapatra and Subhadarshini Mohanty

Analysis of Credit Card Fraud Detection Using Fusion Classi fiers 111Priyanka Kumari and Smita Prava Mishra

An Ef ficient Swarm-Based Multicast Routing Technique—Review 123Priyanka Kumari and Sudip Kumar Sahana

A New Howard –Crandall–Douglas Algorithm for the American

Option Problem in Computational Finance 135Nawdha Thakoor, Dhiren Kumar Behera, Désiré Yannick Tangman

and Muddun Bhuruth

Graph Anonymization Using Hierarchical Clustering 145Debasis Mohapatra and Manas Ranjan Patra

Reevaluation of Ball-Race Conformity Effect on Rolling Element

Bearing Life Using PSO 155

S N Panda, S Panda, D S Khamari, P Mishra and A K Pattanaik

Static Cost-Effective Analysis of a Shifted Completely Connected

Network 165Mohammed N M Ali, M M Hafizur Rahman, Dhiren K Behera

and Yasushi Inoguchi

Dynamic Noti fications in Smart Cities for Disaster Management 177Sampada Chaudhari, Amol Bhagat, Nitesh Tarbani and Mahendra Pund

Application of Classi fication Techniques for Prediction

and Analysis of Crime in India 191Priyanka Das and Asit Kumar Das

Improving Accuracy of Classi fication Based on C4.5 Decision Tree

Algorithm Using Big Data Analytics 203Bhavna Rawal and Ruchi Agarwal

Comparative Study of MPPT Control of Grid-Tied PV Generation

by Intelligent Techniques 213

S Behera, D Meher and S Poddar

Stability Analysis in RECS-Integrated Multi-area AGC System

with SOS Algorithm Based Fuzzy Controller 225Prakash Chandra Sahu, Ramesh Chandra Prusty and Sidhartha Panda

Log-Based Reward Field Function for Deep-Q-Learning for Online

Mobile Robot Navigation 237Arun Kumar Sah, Prases K Mohanty, Vikas Kumar

and Animesh Chhotray

Integrated Design for Assembly Approach Using Ant Colony

Optimization Algorithm for Optimal Assembly Sequence Planning 249

G Bala Murali, B B V L Deepak, B B Biswal

and Bijaya Kumar Khamari

Design and Performance Evaluation of Fractional Order

PID Controller for Heat Flow System Using Particle Swarm

Optimization 261Rosy Pradhan, Susmita Pradhan and Bibhuti Bhusan Pati

Land Records Data Mining: A Developmental Tool

for Government of Odisha 273Pabitrananda Patnaik, Subhashree Pattnaik and Prashant Kumar Pramanik

Piecewise Modeling of ECG Signals Using Chebyshev Polynomials 287

Om Prakash Yadav and Shashwati Ray

Analysis of Supplementary Excitation Controller for Hydel Power

System GT Dynamic Using Metaheuristic Techniques 297Mahesh Singh, R N Patel and D D Neema

Farthest SMOTE: A Modi fied SMOTE Approach 309Anjana Gosain and Saanchi Sardana

DKFCM: Kernelized Approach to Density-Oriented Clustering 321Anjana Gosain and Tusharika Singh

Comparative Study of Optimal Overcurrent Relay Coordination

Using Metaheuristic Techniques 333Shimpy Ralhan, Richa Goswami and Shashwati Ray

Prediction of Gold Price Movement Using Discretization

Procedure 345Debanjan Banerjee, Arijit Ghosal and Imon Mukherjee

Language Discrimination from Speech Signal Using Perceptual

and Physical Features 357Ghazaala Yasmin, Ishani DasGupta and Asit K Das

Constructing Fuzzy Type-I Decision Tree Using Fuzzy Type-II

Ambiguity Measure from Fuzzy Type-II Datasets 369Mohamed A Elashiri, Ahmed T Shawky and Abdulah S Almahayreh

Aspect-Level Sentiment Analysis on Hotel Reviews 379Nibedita Panigrahi and T Asha

Node Grouping and Link Segregation in Circular Layout

with Edge Bundling 391Surbhi Dongaonkar and Vahida Attar

Fuzzy-Based Mobile Base Station Clustering Technique to Improve

the Wireless Sensor Network Lifetime 401

R Sunitha and J Chandrika

Hydropower Generation Optimization and Forecasting Using PSO 411

D Kiruthiga and T Amudha

Automatic Identi fication and Classification of Microaneurysms,

Exudates and Blood Vessel for Early Diabetic Retinopathy

Recognition 423Vaibhav V Kamble and Rajendra D Kokate

Performance Analysis of Tree-Based Approaches

for Pattern Mining 435Anindita Borah and Bhabesh Nath

Discovery of Variables Affecting Performance of Athlete Students

Using Data Mining 449Rahul Sarode, Aniket Muley, Parag Bhalchandra, Sinku Kumar Singh

and Mahesh Joshi

Implementation of Non-restoring Reversible Divider Using

a Quantum-Dot Cellular Automata 459Ritesh Singh, Neeraj Kumar Misra and Bandan Bhoi

Depth Estimation of Non-rigid Shapes Based on Fibonacci

Population Degeneration Particle Swarm Optimization 471Kothapelli Punnam Chandar and Tirumala Satya Savithri

Connecting the Gap Between Formal and Informal Attributes

Within Formal Learning with Data Mining Techniques 483Shivanshi Goel, A Sai Sabitha and Abhay Bansal

Multiple Linear Regression-Based Prediction Model to Detect

Hexavalent Chromium in Drinking Water 493

K Sri Dhivya Krishnan and P T V Bhuvaneswari

Data Engineered Content Extraction Studies

for Indian Web Pages 505Bhanu Prakash Kolla and Arun Raja Raman

Steganography Using FCS Points Clustering

and Kekre ’s Transform 513Terence Johnson, Susmita Golatkar, Imtiaz Khan, Vaishakhi Pilankar

and Nehash Bhobe

Anomaly Detection in Phonocardiogram Employing

Deep Learning 525

V G Sujadevi, K P Soman, R Vinayakumar and A U Prem Sankar

Secured Image Transmission Through Region-Based Steganography

Using Chaotic Encryption 535Shreela Dash, M N Das and Mamatarani Das

Technical Analysis of CNN-Based Face Recognition

System —A Study 545

S Sharma, Ananya Kalyanam and Sameera Shaik

Application of Search Group Algorithm for Automatic Generation

Control of Interconnected Power System 557Dillip Khamari, Rabindra Kumar Sahu and Sidhartha Panda

A Heuristic Comparison of Optimization Algorithms for the

Trajectory Planning of a 4-axis SCARA Robot Manipulator 569Pradip Kumar Sahu, Gunji Balamurali, Golak Bihari Mahanta

and Bibhuti Bhusan Biswal

A Computer-Aided Diagnosis System for Breast Cancer Using

Deep Convolutional Neural Networks 583Nacer Eddine Benzebouchi, Nabiha Azizi and Khaled Ayadi

Indian Stock Market Prediction Using Machine Learning

and Sentiment Analysis 595Ashish Pathak and Nisha P Shetty

Exploring the Average Information Parameters over Lung Cancer

for Analysis and Diagnosis 605Vaishnaw G Kale and Vandana B Malode

A Hash-Based Approach for Document Retrieval by Utilizing

Term Features 617Rajeev Kumar Gupta, Durga Patel and Ankit Bramhe

Transform Domain Mammogram Classi fication Using Optimum

Multiresolution Wavelet Decomposition and Optimized

Association Rule Mining 629Poonam Sonar and Udhav Bhosle

Noise Reduction in Electrocardiogram Signal Using Hybrid

Methods of Empirical Mode Decomposition with Wavelet

Transform and Non-local Means Algorithm 639Sarmila Garnaik, Nikhilesh Chandra Rout and Kabiraj Sethi

A Path-Oriented Test Data Generation Approach Hybridizing

Genetic Algorithm and Arti ficial Immune System 649Gargi Bhattacharjee and Ashish Singh Saluja

To Enhance Web Response Time Using Agglomerative Clustering

Technique for Web Navigation Recommendation 659Shraddha Tiwari, Rajeev Kumar Gupta and Ramgopal Kashyap

Query-Optimized PageRank: A Novel Approach 673Rajendra Kumar Roul, Jajati Keshari Sahoo and Kushagr Arora

Effects of Social Media on Social, Mental, and Physical Health

Traits of Youngsters 685Gautami Tripathi and Mohd Abdul Ahad

Document Labeling Using Source-LDA Combined

with Correlation Matrix 697Rajendra Kumar Roul and Jajati Keshari Sahoo

Diffusion Least Mean Square Algorithm for Identification of IIR

System Present in Each Node of a Wireless Sensor Networks 709

Km Dimple, Dinesh Kumar Kotary and Satyasai Jagannath Nanda

Comparative Evaluation of Various Feature Weighting Methods

on Movie Reviews 721

S Sivakumar and R Rajalakshmi

Dynamic ELD with Valve-Point Effects Using Biogeography-Based

Optimization Algorithm 731

A K Barisal, Soudamini Behera and D K Lal

A Survey on Teaching –Learning-Based Optimization Algorithm:

Short Journey from 2011 to 2017 739Janmenjoy Nayak, Bighnaraj Naik, G T Chandrasekhar and H S Behera

Predicting Users ’ Preferences for Movie Recommender System

Using Restricted Boltzmann Machine 759Dayal Kumar Behera, Madhabananda Das and Subhra Swetanisha

Comparative Analysis of DTC Induction Motor Drives with Fire fly,

Harmony Search, and Ant Colony Algorithms 771Naveen Goel, R N Patel and Saji Chacko

DNA Gene Expression Analysis on Diffuse Large B-Cell

Lymphoma (DLBCL) Based on Filter Selection Method

with Supervised Classi fication Method 783Alok Kumar Shukla, Pradeep Singh and Manu Vardhan

Categorizing Text Data Using Deep Learning: A Novel Approach 793Rajendra Kumar Roul and Sanjay Kumar Sahay

An Approach to Detect Patterns (Sub-graphs) with Edge Weight

in Graph Using Graph Mining Techniques 807Bapuji Rao and Sarojananda Mishra

Comparative Performance Analysis of Adaptive Tuned PID

Controller for Multi-machine Power System Network 819Mahesh Singh, Aparajita Agrawal, Shimpy Ralhan and Rajkumar Jhapte

A Competent Algorithm for Enhancing Low-Quality Finger Vein

Images Using Fuzzy Theory 831Rose Bindu Joseph and Devarasan Ezhilmaran

An Adaptive Fuzzy Filter-Based Hybrid ARIMA-HONN Model

for Time Series Forecasting 841Sibarama Panigrahi and H S Behera

An Evolutionary Algorithm-Based Text Categorization Technique 851Ajit Kumar Das, Asit Kumar Das and Apurba Sarkar

Short-Term Load Forecasting Using Genetic Algorithm 863Papia Ray, Saroj Kumar Panda and Debani Prasad Mishra

A Dynamic Bottle Inspection Structure 873Santosh Kumar Sahoo, M Mahesh Sharma and B B Choudhury

Feature Selection-Based Clustering on Micro-blogging Data 885Soumi Dutta, Sujata Ghatak, Asit Kumar Das, Manan Gupta

and Sayantika Dasgupta

Author Index 897

About the Editors

Prof Himansu Sekhar Behera is working as Head of the Department andAssociate Professor in the Department of Information Technology, Veer SurendraSai University of Technology (VSSUT) (a unitary technical university, established

by Government of Odisha), Burla, Odisha He has received his M.Tech degree inComputer Science and Engineering from NIT Rourkela (formerly REC, Rourkela)and Doctor of Philosophy in Engineering (Ph.D.) from Biju Patnaik University ofTechnology (BPUT), Rourkela, Government of Odisha, respectively He haspublished more than 80 research papers in various international journals and con-ferences and edited 11 books and is acting as a member of the editorial/reviewerboard of various international journals He is proficient in the field of computerscience engineering, served the capacity of programme chair and tutorial chair andacts as advisory member of committees of many national and international con-ferences His research interests include data mining and intelligent computing He isassociated with various educational and research societies like OITS, ISTE, IE,ISTD, CSI, OMS, AIAER, SMIAENG, SMCSTA He is currently guiding sevenPh.D scholars, and three scholars have been awarded under his guidance

Dr Janmenjoy Nayak is working as an Associate Professor at Sri Sivani College

of Engineering, Srikakulam, Andhra Pradesh, India He has been awarded INSPIREResearch Fellowship from the Department of Science and Technology, Government

of India (both as JRF and as SRF levels) for doing his Doctoral Research in theDepartment of Computer Science and Engineering & Information Technology,Veer Surendra Sai University of Technology, Burla, Odisha, India He completedhis M.Tech degree (gold medallist and topper of the batch) in Computer Sciencefrom Fakir Mohan University, Balasore, Odisha, India, and M.Sc degree (goldmedallist and topper of the batch) in Computer Science from Ravenshaw Univer-sity, Cuttack, Odisha, India He has published more than 50 research papers invarious reputed peer-reviewed international conferences, refereed journals andchapters He has also published one textbook on“Formal Languages and AutomataTheory” in Vikash Publishing House Pvt Ltd., which has been widely acclaimedthroughout the country and abroad by the students of all levels He is the recipient

xxvii

of“Young Faculty in Engineering” award from Centre of Advance Research andDesign, VIFA-2017, Chennai, India, for exceptional academic records and researchexcellence in the area of computer science engineering He has been serving as anactive member of reviewer committee of various reputed peer-reviewed journals

such as IET Intelligent Transport Systems, Journal of Classi fication, Springer, International Journal of Computational System Engineering, International Journal

of Swarm Intelligence, International Journal of Computational Science and neering, International Journal of Data Science He is the life member of some

Engi-of the reputed societies like CSI, India, OITS, OMS, IAENG (Hong Kong) Hisareas of interest include data mining, nature-inspired algorithms and softcomputing

Dr Bighnaraj Naik is an Assistant Professor in the Department of ComputerApplications, Veer Surendra Sai University of Technology, Burla, Odisha, India

He received his doctoral degree from the Department of Computer Science andEngineering & Information Technology, Veer Surendra Sai University of Tech-nology, Burla, Odisha, India; master’s degree from the Institute of TechnicalEducation and Research, SOA University, Bhubaneswar, Odisha, India; andbachelor’s degree from the National Institute of Science and Technology, Ber-hampur, Odisha, India He has published more than 50 research papers in variousreputed peer-reviewed international conferences, refereed journals and chapters Hehas more than 8 years of teaching experience in thefields of computer science andinformation technology He is the life member of International Association ofEngineers (Hong Kong) His areas of interest include data mining, soft computing

He is the recipient of“Young Faculty in Engineering” award for 2017 from Centre

of Advance Research and Design, VIFA-2017, Chennai, India, for exceptionalacademic records and research excellence in the area of computer science engi-neering He has been serving as an active member of reviewer committee of various

reputed peer-reviewed journals such as Swarm and Evolutionary Computation, Elsevier; Journal of King Saud University, Elsevier; International Journal of

Computational System Engineering, Inderscience; International Journal of Swarm Intelligence, Inderscience; International Journal of Computational Science and Engineering, Inderscience; International Journal of Data Science, Inderscience.

Currently, He is serving as editor of the book entitled “Information Security inBiomedical Signal Processing”, IGI-Global (publisher), USA Also, he is the Guest

Editor of International Journal of Computational Intelligence Studies, Inderscience Publication, and International Journal of Data Science and Analytics, Springer.

Prof Ajith Abraham received M.S degree from Nanyang TechnologicalUniversity, Singapore, and Ph.D degree in Computer Science from MonashUniversity, Melbourne, Australia He is currently a Research Professor at theTechnical University of Ostrava, Czech Republic He is also the Director ofMachine Intelligence Research Labs (MIR Labs), Scientific Network for Innovationand Research Excellence, which has members from more than 75 countries Heserves/has served the editorial board of over 50 international journals and has alsoguest-edited 40 special issues on various topics He is an author/co-author of more

than 700 publications, and some of the works have also won the best paper awards

at international conferences and also received several citations Some articles areavailable in the ScienceDirect Top 25 hottest articles He serves the IEEE ComputerSociety’s Technical Committee on Scalable Computing and was the General Chair

of the 8th International Conference on Pervasive Intelligence and Computing(PICOM 2009) and 2nd International Conference on Multimedia InformationNetworking and Security (MINES 2010) He is also the General Co-chair ofMINES 2011 Since 2008, he is the Chair of IEEE Systems Man and CyberneticsSociety Technical Committee on Soft Computing He is a Senior Member of IEEE,the IEEE Computer Society, the Institution of Engineering and Technology(UK) and the Institution of Engineers Australia (Australia) He is the founder ofseveral IEEE-sponsored annual conferences, which are now annual events—HybridIntelligent Systems (HIS—11 years); Intelligent Systems Design and Applications(ISDA—11 years); Information Assurance and Security (IAS—7 years); NextGeneration Web Services Practices (NWeSP—7 years), Computational Aspects ofSocial Networks (CASoN—3 years), Soft Computing and Pattern Recognition(SoCPaR—3 years), Nature and Biologically Inspired Computing (NaBIC—3years) are some examples

BER Performance Analysis of Image

Transmission Using OFDM Technique

in Different Channel Conditions Using

Various Modulation Techniques

Arun Agarwal, Binayak Satish Kumar and Kabita Agarwal

Abstract It can be clearly seen in the modern world of digital era that we depend alot on mobile/smartphones which in return depends on data/information to proof itsworthy To meet this increased demand of data rate, new technology is needed asfor old technology like GPRS and EDGE which is beyond their capacity Soorthogonal frequency division multiplexing (OFDM) takes this place as it increasesthe data rate in the same bandwidth, as it is alsofixed In this paper, we present acomparison between data handling using different modulations, which in turn can

be assumed for bit error rate (BER) and signal-to-noise ratio (SNR) curve, ofnon-OFDM and OFDM channel under different fading environments

Keywords Data rate ⋅ BER ⋅ Fading channels ⋅ SNR ⋅ Convolutional codingHigh-speed Internet ⋅ OFDM ⋅ Modulation

1 Introduction

In modern world for data need of every individual person, the technologies likeGPRS and EDGE are not sufficient enough as they have far less data throughputthan the need is In this place comes orthogonal frequency division multiplexing

A Agarwal

Department of EIE, ITER, Siksha ‘O’ Anusandhan Deemed to be University,

Khandagiri Square, Bhubaneswar 751030, Odisha, India

e-mail: arunagrawal@soa.ac.in

B S Kumar (✉)

Department of ECE, ITER, Siksha ‘O’ Anusandhan Deemed to be University,

Khandagiri Square, Bhubaneswar 751030, Odisha, India

e-mail: binayakdsip008@gmail.com

K Agarwal

Department of ETC, C V Raman College of Engineering,

Bhubaneswar 752054, Odisha, India

e-mail: akkavita22@gmail.com

© Springer Nature Singapore Pte Ltd 2019

H S Behera et al (eds.), Computational Intelligence in Data Mining,

Advances in Intelligent Systems and Computing 711,

https://doi.org/10.1007/978-981-10-8055-5_1

1

(OFDM) which is a high data throughput technology where a single large datastream transmission takes place over a number of low-rate sub-carriers [1] Thesemultiple sub-carriers are maintained orthogonal to each other in OFDM, by theaddition of cyclic prefix/guard interval So, this helps in utilizing lesser bandwidth,i.e., more data in same bandwidth.

OFDM nowadaysfinds tremendous applications in variety of wireless and wireddigital communication channels for broadband data such as Wi-fi Due to use ofcyclic prefix in OFDM, it efficiently mitigate the effect of multi-path fading channeland hence resistant to intersymbol interference (ISI) [1–8] OFDM also allows theimplementation of single-frequency networks (SFN), whereby network transmitterscan cover large area with the same transmission frequency Such advantage allowsOFDM to find application in high data rate digital services such as digital videobroadcasting (DVB) and digital audio broadcasting (DAB)

In this work, we evaluate the BER performance with various digital modulationtechniques with OFDM that support high data throughput [9,10] The performance

of QPSK, 16-QAM, and 64-QAM was compared with BPSK which is the mostcommon type of digital modulation with lowest BER-to-SNR ratio for achievinghigher data rate with minimal transmitter power in Rayleigh fading environment.The contribution of this paper has been divided into three sections Section1

explained about introduction Section2will brief about fading channels Finally, inSect.3 simulation results are presented and we compare BER for image trans-mission using OFDM with that of non-OFDM transmission technique

2 Fading Environment

If it would have been only the condition of sending and receiving the informationthrough communication channel, then there would have been no problem, but inreal world it’s not In real world, the communication channel wired or wireless isprone to introduction of noises which is due to reflection of signal, refraction ofsignal, interference from electronic appliances, etc., and this causes in fading ofsignal strength

2.1 AWGN Channel

Additive white Gaussian noise (AWGN) channel is the most common type channelmodel that adds white Gaussian noise to the signal that passes through it Here, awhite noise withfixed spectral density (watts/hertz of bandwidth) is simply addedwith amplitude that follows a Gaussian distribution This channel does not havefading component It has a very simple mathematical model which is useful fordeeper system analysis

The AWGN channel adds white Gaussian noise to a real or complex inputsignal When the input signal is real, this channel adds real Gaussian noise andproduces a real output signal When the input signal is complex, this channel addscomplex Gaussian noise and produces a complex output signal.

The AWGN channel model is widely used in LOS microwave links, satellite anddeep space communication links AWGN is frequently used model for most ter-restrial path modeling which evaluates the effect of background additive noise ofthe channel

2.2 Rician Channel

The model for Rician fading channel is same as Rayleigh fading except that inRician channels in which we have a strong line of sight component along withreflected waves A Rician fading channel can be characterized by two parameters, Kand Ω K represents a ratio between the direct path power and the power in thescattered paths.Ω is defined to be the total power from both paths and acts as ascaling factor to the distribution

3 Proposed Work

An image is transmitted using BPSK, QPSK, 16-QAM, and 64-QAM digitalmodulation scheme using OFDM technique in AWGN, Rayleigh, and Ricianchannel Then, the BER graph of the same is compared with non-OFDM technique

to justify/verify that OFDM is a better technique for transmission in fadingchannels

4 Simulation Results and Discussion

In this section, all results of the simulation are presented and analyzed lated BER plots are shown for different modulation techniques and with andwithout OFDM Performance analysis was carried out in three channels, namelyAWGN, Rayleigh, and Rician to test suitability of the communication systemmodel proposed Figure1below presents BER performance curve of non-OFDM inAWGN channel

Simu-From the Table1 of SNR versus BER of AWGN channel, it can be seen thatwhat it looks like a very small improvement around 0.2 dB in SNR value in OFDM

in comparison with non-OFDM method

From the Table2of SNR versus BER of Rician channel, it can be seen that nownotable improvement of about 3.5 dB in SNR value in OFDM in comparison withnon-OFDM method.

Fig 1 BER performance curve of non-OFDM in AWGN channel

Fig 2 BER performance curve of OFDM in AWGN channel

From the Table3 of SNR versus BER of Rayleigh channel, it can be seen animprovement of about 7 dB that can be worth looking for in SNR value in OFDM

in comparison with non-OFDM method

Fig 3 BER performance curve of non-OFDM in Rician channel

Fig 4 BER performance curve of OFDM in Rician channel

Fig 5 BER performance curve of non-OFDM in Rayleigh channel

Fig 6 BER performance curve of OFDM in Rayleigh channel

5 Conclusion

As, we are seeing a 0.2–0.5 dB improvement in SNR in AWGN channel, then a2.5–5.0 dB improvement in SNR in Rician channel and finally a 7–9 dBimprovement in SNR in Rayleigh channel Therefore it confirms that proposedmodel is suitable for Rayleigh fading channel

It can be concluded from the result of our simulation that in Rayleigh fadingenvironment, with AWGN noise, the BER curve showed an improvement in SNRwith OFDM We carried out comparisons between OFDM technology andnon-OFDM technology for transmission of image The performance of BER curve

is better with OFDM technology in all channels Finally to conclude OFDM is apromising technology for upcomiong 5G networks with MIMO

References

1 Gangadharappa, Mandlem, Rajiv Kapoor, and Hirdesh Dixit “An efficient hierarchical 16-QAM dynamic constellation to obtain high PSNR reconstructed images under varying channel conditions ” IET Communications 10.2 (2016): pp 139–147.

2 Song, Jie, and K.j.r Liu “Robust Progressive Image Transmission over OFDM Systems Using Space-time Block Code ” IEEE Transactions on Multimedia 4.3 (2002): pp 394–406.

Table 1 SNR for BER at 10−3in AWGN channel for Figs 1 and 2

Modulation method Modulation scheme

Table 2 SNR for BER at 10−3in Rician channel for Figs 3 and 4

Modulation method Modulation scheme

Table 3 SNR for BER at 10−3in Rayleigh channel for Figs 5 and 6

Modulation method Modulation scheme

3 Alok, and Davinder S Saini “Performance analysis of coded-OFDM with RS-CC and Turbo codes in various fading environment ” Information Technology and Multimedia (ICIM), 2011 International Conference on IEEE, 2011: pp 1 –6.

4 Arun Agarwal, S.K Patra “Performance prediction of OFDM based DAB system using Block coding techniques ”, presented in proceedings of the IEEE International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT-2011), Mar 23rd –24th,

2011 at Nagarcoil, Kanyakumari, India.

5 Arun Agarwal, Saurabh N Mehta, “Combined Effect of Block interleaving and FEC on BER Performance of OFDM based WiMAX (IEEE 802.16d) System ”, American Journal of Electrical and Electronic Engineering, © Science and Education Publishing, USA, Vol 3,

No 1, pp 4 –12, March 2015, https://doi.org/10.12691/ajeee-3-1-2.

6 Arun Agarwal, Kabita Agarwal, “Performance Prediction of WiMAX (IEEE 802.16d) Using Different Modulation and Coding Pro files in Different Channels”, pp 2221–2234, Volume

10, Number 2, February 2015 in International Journal of Applied Engineering Research (IJAER).

7 Arun Agarwal, Saurabh N Mehta, “Design and Performance Analysis of MIMO-OFDM System Using Different Antenna con figurations”, in conference Proceedings of the IEEE International Conference on Electrical, Electronics, and Optimization Techniques (IEEE-ICEEOT-2016), Chennai, Tamil Nadu, India, 3rd to 5th March 2016, pp 1373 –1377.

8 Arun Agarwal, Kabita Agarwal, “Design and Simulation of COFDM for high speed wireless communication and Performance analysis ”, in IJCA - International Journal of Computer Applications, pp 22 –28, Vol-2, Oct-2011., ISBN: 978-93-80865-49-3.

9 Nyirongo, Nyembezi, Wasim Q Malik, and David J Edwards “Concatenated RS-convolutional codes for ultrawideband multiband-OFDM ” 2006 IEEE International Conference on Ultra-Wideband IEEE, 2006: pp 137 –142.

10 Haque, Md Dulal, Shaikh Enayet Ullah, and Md Razu Ahmed “Performance evaluation of a wireless orthogonal frequency division multiplexing system under various concatenated FEC channel-coding schemes ” Computer and Information Technology, 2008 ICCIT 2008 11th International Conference on IEEE, 2008; pp 94 –97.

On Understanding the Release Patterns

of Open Source Java Projects

Arvinder Kaur and Vidhi Vig

Abstract Release length is of great significance to companies as well as toresearchers as it provides a deeper insight into the rules and practices followed bythe applications It has been observed that many Open Source projects follow agilepractices of parallel development and Rapid Releases (RR) but, very few studies tilldate, have analyzed release patterns of these Open Source projects This paperanalyzes ten Open Source Java projects (Apache Server Foundation) comprising

718 releases to study the evolution of release lengths The results of the study showthat: (1) eight out of ten datasets followed RR models (2) None of these datasetsfollowed RR models since their first release (3) The average release length wasfound to be four months for major versions and one month for minor versions(exceptions removed) (4) There exists a negative correlation between number ofcontributors and release length

Keywords Release cycle ⋅ Frequent versions ⋅ Evolution ⋅ Open SourceRepositories

1 Introduction

Agile methodologies like XP [1] instituted the notion of faster or Rapid Release

cycles and advocate the benefits of using them for both companies and customers.Soaring market competition has forced many software companies to exerciseshorter release cycles and release their products within a span of weeks or days [2].Mozilla Firefox migrated to Rapid Release concept after facing huge competitionfrom Google Chrome and shifted from its Traditional Release model of one year for

a major release to 6 weeks from version 5.0 [3,4]

A Kaur ⋅ V Vig (✉)

University School of Information, Communication and Technology, Guru Gobind

Singh Indraprastha University, Sec 16-C, Dwarka, New Delhi 110078, India

e-mail: vidhi.ipu@gmail.com

A Kaur

e-mail: arvinderkaurtakkar@yahoo.com

© Springer Nature Singapore Pte Ltd 2019

H S Behera et al (eds.), Computational Intelligence in Data Mining,

Advances in Intelligent Systems and Computing 711,

https://doi.org/10.1007/978-981-10-8055-5_2

9

These shorter cycles allow faster customer feedback thereby enabling companies

to schedule their succeeding releases more easily and do not pressurize thedeveloper to complete the entire feature at once The features can be published inincremental releases, and meanwhile, the developer can pay attention on qualityassurance [5] resulting in faster bug detection and correction [4] Shorter releasecycles have been adapted in many software and embedded domains A recent study[6] analyzed release patterns in mobile domain and found that frequently updatedmobile applications (i.e., shorter release cycles) on Google Play Store were highlyranked by the users, irrespective of their high update frequency [6] In fact, updatedversions were embraced more quickly by the users worldwide [5] Still, there existcertain disadvantages to this methodology in terms of lesser time tofix bugs, lack ofstabilized platforms [7], increased user cost due to continuous updates [8], andinability to test all configuration of released product [9]

From testing perspective, some recent studies [10, 11] advocate using RRmodels since it allows testing to be more concentrated and allowed extendedinvestigation of features pertaining to highest risks Alternatively, releasing versionsfaster makes testing more continuous and deadline oriented However, some studiesfound that testing is hampered in such models as there is lesser time to test [12].This study is an endeavor to analyze the characteristics of datasets and theirrelease patterns in Open Source The ease of replication is the prime USP (UniqueSelling Point) of OSS Not only it offers pliability of replication, but also enablesconfirmation or revision to a larger research group, unlike closed source andindustrial data Since Open Source is the breeding ground of many software andresearches, it must be explored for every new technique and domain

The paper presents the data collection methodology in Sect.2, followed byanalyses in Sect.3, discusses the exceptions in Sect.3.3, and concludes the result inSect.4

2 Data Collection Methodology

Apache Server Foundation (ASF) [13] hosts numerous projects in multiple guages like Java, Python, Ruby, C, and C++ Out of all, Java holds the maximumportion, thereby making it an ideal choice for study The required software artifactswere obtained from the Git [14] and Jira [15] repository While selection, thefollowing points were kept in mind:

lan-• The projects must be in “Active” state, i.e., last developmental activity not laterthan six months

• Projects labeled as “Retired” by ASF were ignored

• Projects must have a development life cycle of more than five years

Firstly, Apache Server Foundation was explored, and projects written in Java

language were kept in pool for consideration Then, the above-mentioned selection

criteria were applied, and projects fulfilling the criteria were separated from others.These projects were now subjected to Simple Random Sampling (since it enhances

generalizability), and ten datasets werefinally selected for the analysis The artifacts

of the selected datasets were obtained from Git and Jira (which are source and bugrepositories, respectively) The process of data collection is explained graphically inFig.1 given below, and the characteristics of the studied projects are presented inTable1

Note: The word Release and Version are used synonymously in the study

3 Analysis

This section of the paper presents the outcome and possible interpretation of theresults obtained Atfirst, the study investigates the release trends and patterns of thedatasets selected, then the major and minor versions of the datasets are investigated,andfinally, exceptions are identified and examined

3.1 Datasets and Their Entire Release History

Rapid Release (RR) model encourages releases to be published in days or weeks incomparison with the Traditional Release (TR) model where the releases are not sodeadline oriented The terms“Rapid Release” and “Traditional Releases” are usedantonyms by the research papers published in this domain [4, 6, 10, 12] Foranalysis, the studyfirst collected the release dates of all the released versions andthen calculated the release time between each version Release time of major ver-sions (T), calculated using Eq.1, is the time between two subsequent majorversions

T R ð Þ = Time Version i + 1ð ð Þ − Version ið ÞÞ ð1Þwhere T(R) is the time in days, weeks, and months, Version (i) = Release date ofVersion (i), Version (i + 1) = Release date of next subsequent Version (i + 1).Next, the mean time of these releases was calculated using Eq.2, in order to seethe average time taken by each dataset to release their version

Fig 1 Data collection process

AM =1

n∑n

where AM = Arithmetic Mean, n = number of datasets

This Arithmetic Mean of the releases gives the average time (days, weeks,months) taken by each dataset to air their release and further enable the study toidentify the release models followed by these datasets However, the study assumesdatasets with release time in days or weeks under RR model and release time inmonths under TR models The result of the analysis is presented in Table2

It can be observed that the average release length of eight out of ten selecteddatasets is one month while two datasets, Chukwa and Rat, take an average of ayear to publish their release On further investigation, it was found that these twodatasets had the minimum number of releases (six for Chukwa and eight for Rat) intheir entire life span of seven years This clearly indicates that these two datasets donot follow the RR model and fall under the percentage of projects that follow the

TR models It was found that more than 54% of the datasets follow the RR model inOpen Source repositories [16], and our results not only comply with their results butalso provide stronger proof of RR trend in OSS Figure2given below presents therelease patterns of Chukwa and Rat

The average of these AM, i.e., the Grand Mean (GM) was calculated using

Eq.3, to check average release time of the datasets together

where, GM = Grand Mean, AM = Arithmetic Mean, N = total number of datasets

Table 2 Arithmetic Mean of releases of all the datasets (in days, weeks and months)

Projects Avro Camel Cordova Chukwa Groovy Hive Jclouds Pig Rat Zookeeper

Fig 2 Release cycle of Chukwa and Rat