Advances in Computing and Intelligent Systems

Harish Sharma · Kannan Govindan ·Ramesh C. Poonia · Sandeep Kumar ·Wael M. El-Medany Editors

Proceedings of ICACM 2019

Algorithms for Intelligent SystemsSeries Editors: Jagdish Chand Bansal · Kusum Deep · Atulya K. Nagar

Algorithms for Intelligent Systems

Series Editors

Jagdish Chand Bansal, Department of Mathematics, South Asian University,New Delhi, Delhi, IndiaKusum Deep, Department of Mathematics, Indian Institute of Technology Roorkee,Roorkee, Uttarakhand, IndiaAtulya K. Nagar, Department of Mathematics and Computer Science,Liverpool Hope University, Liverpool, UK

This book series publishes research on the analysis and development of algorithmsfor intelligent systems with their applications to various real world problems. Itcovers research related to autonomous agents, multi-agent systems, behavioralmodeling, reinforcement learning, game theory, mechanism design, machinelearning, meta-heuristic search, optimization, planning and scheduling, artificialneural networks, evolutionary computation, swarm intelligence and other algo-rithms for intelligent systems.

The book series includes recent advancements, modification and applicationsof the artificial neural networks, evolutionary computation, swarm intelligence,artificial immune systems, fuzzy system, autonomous and multi agent systems,machine learning and other intelligent systems related areas. The material will bebeneficial for the graduate students, post-graduate students as well as theresearchers who want a broader view of advances in algorithms for intelligentsystems. The contents will also be useful to the researchers from other fields whohave no knowledge of the power of intelligent systems, e.g. the researchers in thefield of bioinformatics, biochemists, mechanical and chemical engineers,economists, musicians and medical practitioners.

The series publishes monographs, edited volumes, advanced textbooks andselected proceedings.

More information about this series at http://www.springer.com/series/16171

Harish Sharma • Kannan Govindan •

Ramesh C. Poonia • Sandeep Kumar •

Wael M. El-MedanyEditors

Advances in Computingand Intelligent SystemsProceedings of ICACM 2019

123

EditorsHarish SharmaRajasthan Technical UniversityKota, Rajasthan, India

Kannan GovindanUniversity of Southern DenmarkOdense, Denmark

Ramesh C. PooniaAmity UniversityJaipur, Rajasthan, India

Sandeep KumarAmity UniversityJaipur, Rajasthan, India

Wael M. El-MedanyUniversity of BahrainZallaq, Bahrain

ISSN 2524-7565 ISSN 2524-7573 (electronic)Algorithms for Intelligent SystemsISBN 978-981-15-0221-7 ISBN 978-981-15-0222-4 (eBook)https://doi.org/10.1007/978-981-15-0222-4

© Springer Nature Singapore Pte Ltd. 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, 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.

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Preface

The 1st International Conference on Advancements in Computing and Management(ICACM-2019) was held at Jagannath University, Jaipur, Rajasthan, India, on 13–14April 2019. The keynote addresses were given by Prof. Kannan Govindan, Headof Centre, Institute of Technology and Innovation, SDU Centre for SustainableSupply Chain Engineering, University of Southern Denmark, Denmark, Prof. J. C.Bansal, South Asian University, India, Prof. Mohammad Shorif Uddin,Ex. Chairman, Department of Computer Science and Engineering, JahangirnagarUniversity, Savar, Dhaka, Bangladesh, and Elike C. C. Esq, Superintendent ofPolice, Nigeria Police Force, Nigeria. Invited talks are delivered by Dr. HarishSharma, Associate Professor, Rajasthan Technical University, Kota, India, andDr. Ramesh C. Poonia, Associate Professor, Amity University, Rajasthan, India. Wereceived 450 submissions from all over the world. The technical programmecommittee members carefully selected the papers after peer review process by atleast three reviewers. Out of 450 submissions, 60 papers were selected for pre-sentation in the conference and publication in Springer AIS series. We wish tothank the management of Jagannath University, Jaipur, Rajasthan, India, for pro-viding the best infrastructure to organize the conference. We are also thankful toSoft Computing Research Society (SCRS) for providing technical sponsorship forthis event. We are also very thankful to Springer for supporting ICACM-2019.

We hope that this conference proceedings will prove to be useful.

Jaipur, India Dr. Harish SharmaDr. Sandeep Kumar

Dr. Ramesh C. Poonia

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Contents

Intuitionistic Fuzzy Shannon Entropy Weight Based Multi-criteriaDecision Model with TOPSIS to Analyze Security Risks and SelectOnline Transaction Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Talat Parveen, H. D. Arora and Mansaf Alam

Fermat Spiral-Based Moth-Flame Optimization Algorithmfor Object-Oriented Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Rashmi Sharma and Anju Saha

A Comparative Study of Information Retrieval Using MachineLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Surabhi Solanki, Seema Verma and Kishore Chahar

Adaptive Background Subtraction Using Manual Approachfor Static Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Manoj K. Sabnis and Kavita

TweetsDaily: Categorised News from Twitter . . . . . . . . . . . . . . . . . . . . 55Divya Gupta, Aditi Sharma and Mukesh Kumar

Compressing Metaclass Files Through String Optimization . . . . . . . . . . 69S. Nadera Beevi

An Algorithm to Generate Largest Prime Number . . . . . . . . . . . . . . . . 79Rohan Tyagi, Abhishek Bajpai, BDK Patro and Neelam Srivastava

Development of a Discretization Methodology for 2.5D MillingToolpath Optimization Using Genetic Algorithm . . . . . . . . . . . . . . . . . . 93Munish Kumar and Pankaj Khatak

Machine Learning Based Prediction of PM 2.5 Pollution Levelin Delhi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Apurv Mehrotra, R. Jaya Krishna and Devi Prasad Sharma

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A Comparative Study of Load Balancing Algorithmsin a Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Ashmeet Kaur Duggal and Meenu Dave

Information Retrieval from Search Engine Using Particle SwarmOptimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Nainika Kaushik and Manjot Kaur Bhatia

Genetic Algorithm Based Multi-objective Optimization Frameworkto Solve Traveling Salesman Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 141Tintu George and T. Amudha

Design of Optimal PID Controller for Varied SystemUsing Teaching–Learning-Based Optimization . . . . . . . . . . . . . . . . . . . . 153Ashish Mishra, Navdeep Singh and Shekhar Yadav

Innovative Review on Artificial Bee Colony Algorithmand Its Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165Pooja and Gajendra Shirmal

Multilinear Regression Model to Predict Correlation Between ITGraduate Attributes for Employability Using R . . . . . . . . . . . . . . . . . . . 177Ankita Chopra and Madan Lal Saini

An Intelligent Journey to Machine Learning Applicationsin Component-Based Software Engineering . . . . . . . . . . . . . . . . . . . . . . 185Divanshi Priyadarshni Wangoo

Effective Prediction of Type II Diabetes Mellitus Using Data MiningClassifiers and SMOTE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195Mirza Shuja, Sonu Mittal and Majid Zaman

A Comprehensive Analysis of Classification Methodsfor Big Data Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213Amrinder Kaur and Rakesh Kumar

A Literature Analysis on Privacy Preservation Techniques . . . . . . . . . . 223Suman Madan

An Overview of Recommendation System: Methodsand Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231Shefali Gupta and Meenu Dave

Empirical Evaluation of Shallow and Deep Classifiers for RumorDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239Akshi Kumar, Vaibhav Singh, Tuba Ali, Saurabh Paland Jeevanjot Singh

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Short-Term Hydrothermal Scheduling Using Gray WolfOptimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253Khushboo Sharma, Hari Mohan Dubey and Manjaree Pandit

Feature Selection Using SEER Data for the Survivabilityof Ovarian Cancer Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271Prakriti Sodhi and Preeti Aggarwal

Monitoring Logistics Through Blockchain . . . . . . . . . . . . . . . . . . . . . . . 281Ninad Deshmukh, Maitree Gawande, Shivani Pande, Dhvani Kothariand Nikhil Mangrulkar

An M/M/2 Heterogeneous Service Markovian Feedback QueuingModel with Reverse Balking, Reneging and Retentionof Reneged Customers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291Bhupender Kumar Som, Vivek Kumar Sharma and Sunny Seth

Investigation of Facial Expressions for Physiological ParameterMeasurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297Neeru Rathee and Sudesh Pahal

Identification of Severity of Coronary Artery Disease:A Multiclass Deep Learning Framework . . . . . . . . . . . . . . . . . . . . . . . . 303Varun Sapra and Madan Lal Saini

An Intelligent System to Generate Possible Job Listfor Freelancers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311Md. Sabir Hossain and Mohammad Shamsul Arefin

A Novel Image Based Method for Detection and Measurementof Gall Stones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327K. Sujatha, R. Shobarani, A. Ganesan, P. SaiKrishna and Shaik Shafiya

Big Data and Query Optimization Techniques . . . . . . . . . . . . . . . . . . . . 337Aarti Chugh, Vivek Kumar Sharma and Charu Jain

Categorization and Classification of Uber Reviews . . . . . . . . . . . . . . . . . 347Mugdha Sharma, Daksh Aggarwal and Divyasha Pahuja

Review Paper on Novel Approach Improvising Techniquesfor Image Detection Using Deep Learning . . . . . . . . . . . . . . . . . . . . . . . 357N. Satyanarayana Murthy

Comparative Analysis of Selected Variant of Spider MonkeyOptimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365Basudev Sharma, Vivek Kumar Sharma and Sandeep Kumar

A Novel Method to Access Scientific Data from IRNSS/NaVICStation Using Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373Gawas Santoshi Shyamsundar and Narayana Swamy Ramaiah

Contents ix

Live Class Monitoring Using Machine Learning . . . . . . . . . . . . . . . . . . 385Anushka Krishna and Harshita Tuli

Deep Learning for Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . 391Gourav Bathla, Himanshu Aggarwal and Rinkle Rani

Text-Based Spam Tweets Detection Using Neural Networks . . . . . . . . . 401Vanyashree Mardi, Anvaya Kini, V. M. Sukanya and S. Rachana

Preserving IPR Using Reversible Digital Watermarking . . . . . . . . . . . . 409Geeta Sharma and Vinay Kumar

Analysis of Part of Speech Tags in Language Identificationof Code-Mixed Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417Mohd Zeeshan Ansari, Shazia Khan, Tamsil Amani, Aman Hamidand Syed Rizvi

Sentiment Analysis of Smartphone Product ReviewsUsing Weightage Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427Jayantkumar A. Rathod, Shetty Vignesh, Aishwary J. Shetty,Pooja and Nikshitha

Personal Identity on Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Deepti Patole, Yogita Borse, Jayesh Jain and Shreya Maher

Investigation of Robust Control of Position Control System . . . . . . . . . . 447Divya Chauhan and S. K. Jha

Real-Time Hand Hygiene Dispenser System Using Internetof Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461Sayali Salunkhe and Mahadev Patil

Modeling the Factors Affecting Crime Against Women:Using ISM Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469Bhajneet Kaur, Laxmi Ahuja and Vinay Kumar

Impact of Business Analytics for Smart Education Systemand Management Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479Suplab Kanti Podder, Debabrata Samanta and R. Gurunath

A Review on Security and Privacy Issues in Internet of Things . . . . . . . 489Amit Kumar Tyagi, Kavita Agarwal, Deepti Goyal and N. Sreenath

Study of Information Retrieval and Machine Learning-BasedSoftware Bug Localization Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503Tamanna and Om Prakash Sangwan

A Review on Diverse Applications of Case-Based Reasoning . . . . . . . . . 511Madhu Sharma and Cmaune Sharma

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Petri Net Modeling and Analysis of SMART ATM . . . . . . . . . . . . . . . . 519Madhu Sharma and Cmaune Sharma

Robot Path Planning Using Differential Evolution . . . . . . . . . . . . . . . . . 531Sanjay Jain, Vivek Kumar Sharma and Sandeep Kumar

Modified Dragon-Aodv for Efficient Secure Routing . . . . . . . . . . . . . . . 539Monika Goyal, Sandeep Kumar, Vivek Kumar Sharma and Deepak Goyal

Brain Tumor Detection and Classification . . . . . . . . . . . . . . . . . . . . . . . 547Fatema Kathawala, Ami Shah, Jugal Shah, Shranik Vora and Sonali Patil

A Multiple Criteria-Based Context-Aware Recommendation Systemfor Agro-Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557Lata Ramchandani, Shital Patel and Krunal Suthar

Latest Trends in Sheet Metal Componentsand Its Processes—A Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . 565Jibin J. Mathew, Chandrashekhar N. Sakhale and Sagar D. Shelare

An Enhance Mechanism for Secure Data Sharing with IdentityManagement in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575Hardika Modi, Shital Patel and Krunal Suthar

An Enhance Mechanism to Recognize Shill Bidders in Real-TimeAuctioning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583Bharati Bhatol, Shital Patel and Krunal Suthar

An Target-Based Privacy-Preserving Approach Using CollaborativeFiltering and Anonymization Technique . . . . . . . . . . . . . . . . . . . . . . . . . 591Priyanka Chaudhary, Krunal Suthar and Kalpesh Patel

An Effective Priority-Based Resource Allocation Approachin Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597Khyati Prajapati, Krunal Suthar and Jayesh Mevada

A Comparative Study on CBIR Using Color Features and DifferentDistance Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605Shailesh Pandey, Madan Lal Saini and Sandeep Kumar

Performance Evaluation of Wrapper-Based Feature SelectionTechniques for Medical Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619Anil Kewat, P. N. Srivastava and Dharamdas Kumhar

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635

Contents xi

About the Editors

Dr. Harish Sharma is an Associate Professor at the Department of ComputerScience & Engineering, Rajasthan Technical University, Kota. He is the secretaryand a founder member of the Soft Computing Research Society of India. He is anassociate editor of the International Journal of Swarm Intelligence (IJSI), publishedby Inderscience, and has also edited special issues of the journals MemeticComputing and Journal of Experimental and Theoretical Artificial Intelligence. Hisprimary area of interest is nature-inspired optimization techniques. He has pub-lished more than 45 papers in various international journals and conferences.

Dr. Kannan Govindan is a Professor of Operations & Supply Chain Managementand Head of the Centre for Sustainable Supply Chain Engineering, Department ofTechnology and Innovation, University of Southern Denmark, Odense. He haspublished 250 peer-reviewed research articles in journals and books and at con-ferences. With over 17000 citations and an H-index of 70, he is one of the mostinfluential supply chain engineering researchers in the world. He is editor-in-chiefof the International Journal of Business Performance and Supply Chain Modellingand International Journal of Advanced Operations Management.

Dr. Ramesh C. Poonia is a Postdoctoral Fellow at the Cyber-Physical SystemsLaboratory (CPS Lab), Department of ICT and Natural Sciences, NorwegianUniversity of Science and Technology (NTNU), Alesund. He is chief editor ofTARU Journal of Sustainable Technologies and Computing (TJSTC) and associateeditor of the Journal of Sustainable Computing: Informatics and Systems, Elsevier.He has authored/co-authored over 65 research publications in respectedpeer-reviewed journals, book chapters, and conference proceedings.

Dr. Sandeep Kumar is an Assistant Professor at Amity University Rajasthan,India. Dr. Kumar holds a Ph.D. degree in Computer Science & Engineering, 2015;M. Tech. degree from RTU, Kota, 2008; B.E. degree from Engineering College,

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Kota, 2005. He worked as guest editor for special issues in IJIIDS, IJARGE, IJESD,RPCS, IJGUC, and WREMSD. He has published more than 50 research papers invarious peer-reviewed journals and conferences.

Dr. Wael M. El-Medany is currently an Associate Professor at the University ofBahrain, Kingdom of Bahrain, and senior member of the IEEE Society. He holds aPh.D. degree in Electrical Engineering, from Manchester University, UK, (1999);and an M.Sc. degree in Computer Communications, Menoufia University, Egypt,(1991). He is the founder and managing editor of the International Journal ofComputing and Digital Systems (IJCDS), and founder and organizer of MobiApps,DPNoC, and WoTBD workshops and symposium series. He has written over fortyresearch publications and attended several national and international conferencesand workshops.

xiv About the Editors

Intuitionistic Fuzzy Shannon EntropyWeight Based Multi-criteria DecisionModel with TOPSIS to Analyze SecurityRisks and Select Online TransactionMethod

Talat Parveen, H. D. Arora and Mansaf Alam

Abstract There are various payment setups that are available which enables usto effortlessly carry out transactions uninterruptedly from any place utilizing gad-gets with network connections. Internet is a place with numerous risks, threats, andvulnerabilities along with accessibility of security abuses, and to address all thesesecurity issues related threats have become extremely challenging for each organiza-tion as well as for individuals and to select the most appropriate payment method hasbecome challenging. In this paper, intuitionistic fuzzy technique for order preferenceby similarity to ideal solution (TOPSIS) method for multi-criteria decision-making(MCDM) is proposed to rank the alternatives while Shannon’s entropy is utilizedfor weighting criteria. The proposed model is applied to select the online paymentmethod based on several criteria, the existing online payment methods are comparedwith cryptocurrency Bitcoin.

Keywords Multi-criteria decision-making · Cryptocurrency · Transaction system ·Risk · Intuitionistic fuzzy set · TOPSIS · MCDM · Entropy

1 Introduction

Regular security breach and online banking fraud are common these days, these risksdrive us to look out for alternative solutions for Internet transactions, and to makea comparison of security risk and characteristics of existing methods with the alter-nate online payment transaction systems. In this study, we will discuss certain datasafety concerns with respect to existing Internet payment systems and a forthcomingalternative, Bitcoin payment system, and to compare the existing systems with eachother based on certain criteria. The issue of prioritization of data security threat isneeded to be addressed. The TARA system is created by Intel [1] to organize the

T. Parveen (B) · H. D. AroraDepartment of Mathematics, Amity University, Noida, Indiae-mail: talat.tyagi@gmail.com

M. AlamDepartment of Computer Science, Jamia Millia Islamia, New Delhi, India

© Springer Nature Singapore Pte Ltd. 2020H. Sharma et al. (eds.), Advances in Computing and Intelligent Systems,Algorithms for Intelligent Systems,https://doi.org/10.1007/978-981-15-0222-4_1

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2 T. Parveen et al.

data safety risks which can be exceptionally beneficial to alleviate critical threats,but these frameworks lack strong mathematical establishments like TOPSIS or othermulti-criteria decision technique.

Concerning PC security it verywellmay be said that it speaks to a blend of classifi-cation, trustworthiness, and accessibility of information, data, and data frameworks.It, thus, unavoidably, includes security dangers. As indicated by ISACA BMIS [2],risk administration is perceived as a key part of managing IT security dangers. Inpresent global scenario, all inclusive of organized and complex business conditions,there is a development of security dangers, vulnerabilities, and other related dangers.The majority of the cyber threats are based on monetary issues so are the instances ofhacktivism [3]. The measure of cybercrime, security breaks, and hacking is expand-ing fundamentally with wide use of Internet services leading to direct monetarylosses [4]. Recently, Internet extortion cases have expanded with the assistance ofdifferent strategies and procedures, for example, phishing as well as data theft; thisraises extra safety efforts to be implemented to ensure clients safety while usingInternet transaction methods and also to safeguard the online banking platform andE-commerce platform, but due to these additional defensive measures, transactioncost is raised. In recent couple of years, an alternative has come up utilizing the cen-tralized payment frameworks like Internet banking, mobile banking, E-commerce,and lately a framework utilizing decentralized payment methods such as Bitcoin [5]and several other cryptocurrencies such as Ethereum are introduced.

Bitcoin is an electronic transaction/payment method and its main feature is itsdecentralization, which implies that no organization or authority controls the Bitcoinnetwork, and it is decentralized. It enables the anonymity to the clients which thusmake it hard to track the transaction [5]. In a process occurs security issues, especiallywith regards to integrity, credibility of such complex blockchain technology, and alsowith it arrives unavoidable issues and risks which affect the society. Often peopleget confronted with different security situations associated with different transactionsetups like E-banking, E-commerce, and M-banking along with inadequacies, risks,and advantages that such frameworks include. We carried out a research to study theexpanded utilization of Bitcoin blockchain technology and its methodology alongwith security parts of the Bitcoin framework itself, and furthermore to carry outcomparison with commonly used Internet transaction systems. It is extremely trou-blesome or sometimes even difficult to address all security on time, especially inhuge professional workplaces. However, it is necessary to detect the critical securityrisks and for this essential steps are required to manage the risk.

In year 1986, Atanassov [6] introduced the generalization of Zadeh’s [7] fuzzyset, called intuitionistic fuzzy set (IFS), which proved to be a useful tool to deal withnon-determinacy of system due to its characteristic of assigning a membership andnonmembership degree to each element, and further several operators were intro-duced by De et al. [8], and Grzegorzewski and Mrowka [9]. In TOPSIS method,preferences are basically human judgment decided on the basis of human percep-tions, and thus intuitionistic fuzzy methodology allowed more concrete descriptionof multi-criteria decision method problems. Hybrid approach using TOPSIS withIFS for MCDM problems is introduced by researchers with application in medical

Intuitionistic Fuzzy Shannon Entropy Weight… 3

field [10–12] and supplier selection problems [13], also, MCDM methods. Xu [14]proposed intuitionistic fuzzy aggregation operators for MCDM problems to aggre-gate intuitionistic fuzzy information. TOPSIS method is extended by researchersfor interval-valued hesitant and intuitionistic fuzzy information for multi-criteriadecision-making and multi-attribute decision-making problems [15, 16, 17, 18].

In this paper, a hybrid intuitionistic fuzzy [6] multi-criteria decision-making withTOPSIS [19] technique has been proposed for estimating the high security risk withdifferent online payment methods and to rank themost critical payment system out offour online transaction systems, namely, E-Banking, M-Banking, E-Commerce, andBitcoin cryptocurrency, based on six important criteria. It is difficult to express theopinion by decision-makers on the chosen criteria using the crisp data and to get theimpact of different alternatives on those given criteria. IFSpresented byAtanassov [6]has been effectively applied under similar conditions to numerous decision-makingproblems. In a multi-criteria decision-making process, accumulation of the point ofview of all individual decision-makers to assess the significance of criteria and thechoices is extremely important in order to carry out a compelling evaluation process.The operator IFWA is implemented to aggregate the viewpoint of individual decision-maker for grading the alternative’s and criteria’s significance. The TOPSIS processknown to evaluate the positive-ideal and negative-ideal solutions, widely popular inmulti-attribute decision-making problem, is aggregated with IFS theory for rankingthe various alternatives based on chosen criteria. Intuitionistic fuzzy [20] entropymeasure is applied to obtain the weights of each criterion and distance measure isgiven by Szmidt and Kacprzyk [21, 22]. The chosen payment systems are rankedusing intuitionistic fuzzy TOPSIS method.

2 Online Payment Systems

There are various payment setups accessible on the web network which enables usto effortlessly carry out transactions uninterruptedly from any place on the planet byutilizing gadgets with network connection. Internet is a place with numerous risks,threats, and vulnerabilities along with accessibility of security abuses. In this section,we will describe the alternative online transaction methods available for individuals:

E-Banking: E-banking money is one of the most established Internet payment sys-temswhich utilizesWorldWideWeb online service. There are different sorts of clientconfirmation and approval of transactions relying upon application security mecha-nism of each bank. There is retail e-banking and business e-banking, though retaile-banking has more users than the business e-banking, authentication and authoriza-tion system vary significantly among the retail and business clients.M-Banking: M-banking is unique payment method different from regular e-bankingwith regard to process of accessing the service, program, customizable user interfacefor different mobile screens, and also security. It utilizes the wireless application pro-tocol (WAP) service while using the mobile network or when Wi-Fi is not available.

4 T. Parveen et al.

E-Commerce: Today, there are numerous payment service providers (payment gate-ways, acquirers, and/or processors) on the online platform. Direct payment serviceshave been provided by these online service providers to their customers withoutintervention of banks as a mediator. But, these platforms utilize the credit and debitcardswhich are issued by banks to their respective customers for the payments. Thereare lots of different payment gateways available on the Internet these days, and thereare organizations providing the direct payment services to the clients with no bankinvolved in between the transaction. These payment gateways utilize the credit anddebit cards issued by bank to their clients, and the most popular e-commerce onlinepayment methods can easily be found with search engine. All these e-commercepayment methods have different security policies and follow a different mechanismto carry out transaction. PayPal is one of the most widely used payment systems,other than the credit cards.

POS (point of sale) system is one of the traditional transaction methods whichrequires dial-up communication and thus is not categorized as online transactionsystem, and this system also requires the hardware POS machine for accepting cardpayment and thus it cannot be comparedwith other online transactionmethods wherehardware is not required.

Bitcoin: Bitcoin is a peer-to-peer decentralized online payment system; it is a typeof digital currency which was developed by Nakamoto [5]. Bitcoin transaction sys-tem is decentralized and thus has no central repository, all the transactions carriedout on bitcoin network can be traced if transaction hash is known to person, or ifa bitcoin address is known, all the details are recorded without involvement of anycentral authority, and all transactions are just recorded on blockchain and they donot exist anywhere else. Bitcoin, a digital currency, is mined by solving complexmathematical algorithms all around the world with computers and supercomputers,and it is first of cryptocurrency. Any individual or an organization can mine bitcoin,and they can further be traded on various cryptocurrency exchanges, and few majorcrypto exchanges are Binance, Bittrex, Bithump, Kucoin, etc. But new bitcoin can becreated only through mining, and bitcoin has limited supply of 21 million of which17 million are mined and in circulation; it can be stored and traded through bitcoinwallet on blockchain. Bitcoin wallet allows the users to store them electronically andmake transfers from one wallet to another with small fee charged in bitcoin. Bitcoinwas the first cryptocurrency, now there are hundred other cryptocurrencies availablefor different purposes, but the dominance of Bitcoin is 53.9% among all cryptocur-rencies. Digital currency always carries security risks, like in case of Bitcoin, it isthe ownership of wallet which is decided by the private key of wallet, wallet carriespublic and private keys, and the ownership can pass on from one person to anotherwith the private key of the wallet only and one who owns a private with passcode isthe legit owner of Bitcoin wallet on blockchain. It is essential to keep private key safe,as with the loss of private keys owner will no longer be able to claim the ownershipand thus stored Bitcoin will be lost forever. Thus, a person having access to privatekeys can claim ownership of associated wallet; further, bitcoin can be stolen from the

Intuitionistic Fuzzy Shannon Entropy Weight… 5

wallet with the discovery of stolen private keys or if the keys are lost/hacked/copiedby someone, and thus possess high risk.

3 Fuzzy Set, Intuitionistic Fuzzy Set (IFS)

In 1965, fuzzy set was primarily introduced by Zadeh [7] formodeling the vagueness,in year 1986, Atanassov [6] introduced the concept of intuitionistic fuzzy sets (IFS)which is an extension to the classical fuzzy set theory, to deal with the vagueness dueto the amount of hesitation in the system. Basic definitions of fuzzy and intuitionisticfuzzy sets are given as follows.

Definition 1 A fuzzy set F in the universe Z is described by the characteristicfunction μF : Z → [0, 1]. A fuzzy set F is represented as

F = {(z, μF ) : ∀ z ∈ Z} (1)

where μF is the membership degree of element z in the set F. The fact that larger thevalue of μF more are the chances of z belonging to set F.

Definition 2 Intuitionistic fuzzy set (IFS) I on a universe of discourseZ is as follows:

I = {〈z, μI (z), νI (z)〉 : ∀z ∈ Z} (2)

where functions μI (z) and νI (z) : Z → [0, 1] depict the grade of membership andnonmembership of an element z ∈ I ⊂ Z , respectively.

Definition 3 For ∀ z ∈ Z , πI (z) = 1−μI (z)− νI (z) is called degree of uncertaintyor hesitation of IFS set I in Z, with the condition 0 ≤ μI (z) + νI (z) ≤ 1.

The parameter πI (z) is hesitation of degree whether z belongs to I or not. Ifparameter πI (z) has small value, it implies the information about z is more certain.If πI (z) is high, it implies that information is more uncertain about z, when μI (z) =1 − νI (z) ∀ z, it turns into fuzzy set [23]. For IFSs I and J of the set Z, Atanassov[6] defined the multiplication operator which is given as follows:

I ⊗ J = {μI (z) • μJ (z), νI (z) + νJ (z) − νI (z) • νJ (z)|z ∈ Z} (3)

3.1 Entropy, Fuzzy Entropy, and Intuitionistic Fuzzy Entropy

Entropy is a probabilistic measure of uncertainty, and the differences in criteria’sare evaluated using entropy in multi-criteria decision-making method. Shannon [20]

6 T. Parveen et al.

gave the concept of probability as a measure of uncertainty in a discrete distribution,where probabilities are evaluated as random variable probabilities considering itas probability mass function. Generalized Shannon’s entropy function proposed byDe Luca and Termini [24], who approximated the non-probabilistic-based entropyfunction, has been utilized for the study to select the online payment method. Here,entropy process is cooperated to evaluate the uncertainty in decision-making processin a multi-criteria decision-making setup. Several researchers proposed differentmethods to measure the degree of fuzziness [25, 26]. Schmidt and Kaprzyk [10, 27]extended De Luca and Termini [24] for evaluating the distance between intuitionisticfuzzy sets and the non-probabilistic entropy measure. Definitions of entropies are asfollows:

Definition 4 Let δn = {P = {p1, . . . pn}}, pi ≥ 0,∑n

i=1 pi = 1 be a set of randomvariable probabilities, and for any probability distribution P = (p1, . . . pn) ∈ δn ,the Shannon’s entropy [20] is defined as

H(P) = −n∑

i=1

p(zi ) log p(zi ) (4)

Definition 5: Let I be a fuzzy set defined on finite universal set Z = {zi , . . .zn},then fuzzy entropy on I is defined as

H(I ) = −1

n

n∑

i=1

[μI (zi ) logμI (zi ) + (1 − μI (zi )) log(1 − μI (zi ))

](5)

Definition 6 Let I be a fuzzy set defined on finite universal set Z = {zi , . . .zn},then Intuitionistic fuzzy entropy [10] for distance measure between IFS is given asfollows:

E(I ) = 1

n

n∑

i=1

min(μI (zi ), νI (zi )) + πI (zi )

max(μI (zi ), νI (zi )) + πI (zi )(6)

Definition 7 Let I be a fuzzy set defined on finite universal set Z = {zi , . . . zn}, thenVlachos and Sergiadis [28] proposed the following to measure intuitionistic fuzzyentropy:

E(I ) = − 1

n ln 2

n∑

i=1

[μI (zi ) lnμI (zi ) + νI (zi ) ln νI (zi )

− (1 − πI (zi )) ln(1 − πI (zi )) − πI (zi ) ln 2] (7)

Intuitionistic Fuzzy Shannon Entropy Weight… 7

4 An Algorithm for Intuitionistic Fuzzy TOPSIS

Let I = {I1, I2, . . . Im} be a set of “m” alternatives, where decision-makerswill selectfrom I based on criteria set Z = {Z1, Z2, . . . Zn} which has “n” criteria. Followingare steps involved in intuitionistic fuzzy TOPSIS method as depicted in Fig. 1.

Step 1: Decision-maker’s weight evaluation

Consider that there are “l” decision-makers in a group, the importance of decision-maker’s linguistic expression is represented by assigning intuitionistic fuzzy num-bers. Let Dk=[μk, νk, πk] be an intuitionistic fuzzy (IF) number to rate the kthdecision-maker. Thus, the kth decision-maker’s weight is evaluated as

ψk =(μk + πk

(μk

μk+νk

))

∑lk=1

(μk + πk

(μk

μk+νk

)) (8)

and∑l

k=1 ψk = 1.

Step 2: Construct aggregated intuitionistic fuzzy decision matrix.

Let P (k) =(p(k)i j

)

m×nbe an intuitionistic fuzzy decision matrix of intuitionis-

tic fuzzy value Dk=[μk, νk] for each decision-maker is constructed. Here, ψ ={ψ1, ψ2, . . . ψl} denotes each decision-maker’s weight and

∑lk=1 ψk = 1, ψk ∈

[0, 1]. In a multi-criteria decision-making procedure, an aggregated IF decisionmatrix is constituted by infusion of individual opinions of decision-makers into agroup opinion. The IFWA operator proposed by Xu (2007) is used for aggregation,P = (

pi j)m×n

, where

Determine decision maker's weights

Construct aggregated IF decision matrix

with decision makers opinion

Calculate criteria weights

Weighted aggregated IF

decision matrix

Evaluate IFPIS and IFNIS

Calculate separation measure

Calculate relative Closeness coefficient

Rank the Alternatives

Fig. 1 Algorithm for intuitionistic fuzzy TOPSIS method

8 T. Parveen et al.

P =

⎡

⎢⎢⎢⎢⎣

(μA1(z1), νA1(z1), πA1(z1)

) (μA1(z2), νA1(z2), πA1(z1)

) · · · (μA1(zn ), νA1(zn ), πA1(zn )

)

(μA2(z1), νA2(z1), πA2(z1)

) (μA2(z2), νA2(z2), πA2(z2)

) · · · (μA2(zn ), νA2(zn ), πA2(zn )

)

.

.

....

. . ....

(μAm (z1), νAm (z1), πAm (z1)

) (μAm (z2), νAm (z2), πAm (z2)

) · · · (μAm (zn ), νAm (zn ), πAm (zn ))

⎤

⎥⎥⎥⎥⎦

(9)

where

pi j = I FW Aψ

(p(1)i j , p(2)

i j , . . . , p(l)i j

)

= ψ1 p(1)i j ⊕ ψ2 p

(2)i j ⊕ ψ3 p

(3)i j ⊕ . . . ψl p

(l)i j

=[

1 −l∏

k=1

(1 − μ

(k)i j

)ψk

,

l∏

k=1

(ν

(k)i j

)ψk

,

l∏

k=1

(1 − μ

(k)i j

)ψk −l∏

k=1

(ν

(k)i j

)ψk

]

(10)

Here pi j = (μIi

(z j) · νIi

(z j), πIi

(z j))

(i = 1, 2, . . . ,m; j = 1, 2, . . . , n).

The aggregated intuitionistic fuzzy decision matrix P can be defined as follows:

P =

⎡

⎢⎢⎢⎢⎢⎢⎣

p11 p12 p13p21 p22 p23p31 p32 p33. . . . . . . . .

· · · p1m. . . p2m. . . p3m

. . ....

pn1 pn2 pn3 . . . pnm

⎤

⎥⎥⎥⎥⎥⎥⎦

(11)

Step 3: Determine the weights of criteria using entropy

In this step, the intuitionistic fuzzy entropy measure [5, 10, 19, 27, 28] can fetch

objective weights called entropy weights, considering w(k)j =

(μ

(k)j , ν

(k)j , π

(k)j

)as

weight criteria vector obtained over criteria C j by kth decision-maker, thus underintuitionistic fuzzy situation, information from each criterion C j can be evaluatedutilizing entropy equation as follows and thus criteria weights can be obtained:

E(C j

) = − 1

m ln 2

m∑

i=1

[μi j

(C j

)lnμi j

(C j

) + νi j (xi ) ln νi j(C j

)

− (1 − πi j

(C j

))ln(1 − πi j

(C j

)) − πi j(C j

)ln 2

](12)

where j = 1, . . ., n and 1/m ln 2 have a constant value which ensures 0 ≤ E(C j

) ≤1. Thus, the degree of divergence d j which is derived from intrinsic informationfetched by criteria C j is represented as

d j = 1 − E(C j

), where j = 1, . . . , n, (13)

Intuitionistic Fuzzy Shannon Entropy Weight… 9

Therefore, the entropy weight of the jth criteria is given by

wj = d j∑n

j=1 d j(14)

Step 4: Construction of weighted aggregated IFS decision matrix

Now, after determining the weight vector W and aggregating it with intuitionisticfuzzy decision matrix P, the weighted intuitionistic fuzzy decision matrix V can beobtained as [29]

V = W ′ ⊗ P = WT ⊗ [zi j]m×n

= [zi j]

where W = (w1, . . . ,wn) and zi j = ⟨μi j , νi j

⟩ =⟨1 − (

1 − μi j)wj

, νwj

i j

⟩, wj > 0

Step 5. Obtain IFPIS and IFNIS

LetM1 be a benefit criterion andM2 be a cost criterion, respectively. I+ is intuitionis-tic fuzzy positive-ideal solution (IFPIS) and I− is intuitionistic fuzzy negative-idealsolution (IFNIS). Then I+ and I− are obtained as

I+ ={⟨

C j ,((maxi μi j

(C j

)∣∣ j ∈ J1

),(mini μi j

(C j

)∣∣ j ∈ J2

)),

((mini νi j

(C j

)∣∣ j ∈ J1

),(maxi νi j

(C j

)∣∣ j ∈ J2

))

⟩∣∣∣∣i ∈ m

}

(15)

I− ={⟨

C j ,((mini μi j

(C j

)∣∣ j ∈ J1

),(maxi μi j

(C j

)∣∣ j ∈ J2

)),

((maxi νi j

(C j

)∣∣ j ∈ J1

),(mini νi j

(C j

)∣∣ j ∈ J2

))

⟩∣∣∣∣i ∈ m

}

(16)

Step 6. Calculation of the separation measures

The separation measure proposed by Szmidt and Kacprzyk [21, 22], Atanasso [29]is utilized to evaluate the alternative’s distance from IFPIS and IFNIS, which isrepresented as

δi+ =√√√√

1

2n

n∑

j=1

[(μIi W

(z j) − μI∗W

(z j))2 +

(νIi W

(z j) − νI∗W

(z j))2 +

(πIi W

(z j) − πI∗W

(z j))2

]

δi− =√√√√

1

2n

n∑

j=1

[(μIi W

(z j) − μI−W

(z j))2 +

(νIi W

(z j) − νI−W

(z j))2 +

(πIi W

(z j) − πI−W

(z j))2

]

(17)

Step 7. Calculate closeness coefficient

The relative closeness coefficient with respect to the IFPIS I+ of each alternative iscalculated as follows:

C j∗ = δi−

δi+ + δi−(18)

where 0 ≤ C j∗ ≤ 1 and j = 1, . . . ,m

10 T. Parveen et al.

Step 8. Alternative’s Rank

The alternatives are assigned ranks based on their C j∗ value in a descending order.

5 Application of IFS-TOPSIS Method in Selecting OnlinePayment System

The objective of this study is to inspect from the available transaction methodsthe most appropriate online payment method that could effectively carry out thetransactions in most efficient way. The decision-makers can effectively utilize theintuitionistic fuzzy decision-making method along with TOPSIS method to rank andanalyze the available transaction methods. However, it is challenging to categorizethe available systems, considering the criticality of certain trades and security require-ments during payment, to understand how to choose among the available paymentmethods based on these six important characteristics, namely, Vulnerability, Easeof Execution, Consequence, Threat, Operational Importance, and Resiliency. Thecriteria taken under consideration while doing this study are consequence, possibleconsequence in case of exploitation such as regulatory risk, financial losses, easeof execution, the authorization and authentication ways, and their implementation,such as 2FA (two factor authentication), Operational importance, possible conse-quences in case of unavailability of service or exploitation, resiliency, the chancesof system recovery in case of DDoS attacks, or system failure, Threat, attack fromonline ransom malware, phishing, distributed denial-of-service attacks, and so onand vulnerabilities. Main approach used here is to apply the IFS-TOPSIS with intu-itionistic fuzzy entropy method which is employed for evaluating and ranking thealternatives available for online transaction (payment) method based on six differentcriteria, also, to discuss theBitcoin cryptocurrencymethodwith other available trans-action systems. A group of decision-makers with knowledge in information securityand experience with online transaction methods are taken on board, to ensure therelevancy of judgements. These criteria form the basis of proposed methodologyconsidering the security and facilitate the decision-makers to evaluate the availableonline payment systems.

Step 1. Determine the weights of the decision-makers

Linguistic terms used for the ratings by the decision-makers are shown in Table 1,and using Eq. (8) the weights of decision-makers are obtained, which are representedin Table 2. Calculation of decision-maker’s weight are as follows:

Table 1 Linguistic terms

Linguistics term Very important Important Medium Unimportant Veryunimportant

μ 0.9 0.8 0.5 0.3 0.1

ν 0.1 0.15 0.45 0.65 0.9

Intuitionistic Fuzzy Shannon Entropy Weight… 11

Table 2 Decision-maker’sweight

Decision-makers Linguistics term Weight (ψ)

I Very important 0.397

II Medium 0.232

III Important 0.371

ψD1 = 0.9

0.9 + (0.8 + 0.05

(0.80.95

)) + (0.5 + 0.05

(0.50.95

)) = 0.397

ψD2 = 0.8 + 0.05(

0.80.95

)

0.9 + (0.8 + 0.05

(0.80.95

)) + (0.5 + 0.05

(0.50.95

)) = 0.232

ψD3 = 0.5 + 0.05(

0.50.95

)

0.9 + (0.8 + 0.05

(0.80.95

)) + (0.5 + 0.05

(0.50.95

)) = 0.371

Step 2: Construction of aggregated intuitionistic fuzzy decision matrix

Linguistic terms represented in Table 3 are used to construct the aggregated IF deci-sion matrix where decision-maker evaluates all the criteria for four online paymentmethods taken under study and results are represented in Table 4. The aggregated IFdecision matrix depending on the average of decision-maker’s viewpoint, calculatedusing Eq. (9), is represented in Table 5.

Step 3: Estimate the weights of the criteria

Decision-makers evaluated the six criteria independent of alternatives, and their opin-ions were aggregated to estimate the weights of each criterion using equation [1, 2,5], and the resultant weights are represented in Table 6.

Table 3 Linguistic terms Linguistic terms IFNs

μ ν

Extremely good 1 0

Very very good 0.9 0.1

Very good 0.8 0.1

Good 0.7 0.2

Medium good 0.6 0.3

Fair 0.5 0.4

Medium bad 0.4 0.5

Bad 0.25 0.6

Very bad 0.1 0.75

Very very bad 0.1 0.9

12 T. Parveen et al.

Table 4 Alternative weights based on criteria

Criteria Payment method Decision-makers

I II III

Ease of execution M-banking G VG G

E-banking VVG VG VG

E-commerce MG G G

Bitcoin F MG MB

Consequence M-banking MG G MG

E-banking VG G VG

E-commerce VG VG MG

Bitcoin G MG G

Threat M-banking VG G VG

E-banking VG VG G

E-commerce VG VG G

Bitcoin G G MG

Vulnerability M-banking F MG F

E-banking VG VG VVG

E-commerce MG VG VG

Bitcoin VG G MG

Resiliency M-banking G VG G

E-banking VVG G VG

E-commerce G F F

Bitcoin VG G G

Operational importance M-banking MG G VG

E-banking VVG VG VG

E-commerce G MG G

Bitcoin F MB MG

Step 4: Construct aggregated weighted IF decision matrix

An aggregated weighted IF decision matrix is constructed using Eq. (1) and Table 7is thus obtained.

Step 5: Calculate IFPIS and IFNIS

Now, intuitionistic fuzzy positive-ideal solution (IFPIS) and intuitionistic fuzzynegative-ideal solution (IFNIS) are evaluated using Eq. (16) and results are shownin Table 8.

Steps 6 and 7: Separation measures of closeness coefficient

Using Eqs. (17) and (18), the negative and positive separation measure and relativecloseness coefficient are evaluated and results are thus represented in Table 9.

Intuitionistic Fuzzy Shannon Entropy Weight… 13

Table 5 Aggregated IF decision matrix

M-banking E-banking E-commerce Bitcoin

Ease of execution μ 0.727 0.848 0.664 0.492

ν 0.17 0.1 0.235 0.406

π 0.103 0.052 0.101 0.102

Consequence μ 0.626 0.78 0.741 0.679

ν 0.273 0.117 0.15 0.22

π 0.101 0.103 0.109 0.101

Threat μ 0.78 0.768 0.768 0.666

ν 0.117 0.129 0.129 0.232

π 0.103 0.103 0.103 0.102

Vulnerability μ 0.525 0.845 0.737 0.716

ν 0.374 0.1 0.155 0.177

π 0.101 0.055 0.108 0.107

Resiliency μ 0.727 0.833 0.592 0.745

ν 0.17 0.117 0.304 0.152

π 0.103 0.05 0.104 0.103

Operational importance μ 0.711 0.848 0.679 0.52

ν 0.182 0.1 0.22 0.379

π 0.107 0.052 0.101 0.101

Table 6 Criteria weights Criteria Entropy(E(C j

))Distancemeasure (d j )

Weights (wj )

Ease ofexecution

0.83 0.136 0.034

Consequence 0.849 0.127 0.024

Threat 0.723 0.222 0.055

Vulnerability 0.765 0.201 0.034

Resiliency 0.812 0.165 0.023

Operationalimportance

0.882 0.11 0.008

Step 8. Alternatives Rank

The four alternatives were ranked based on C j which is as follows: E-Banking > E-Commerce > M-Banking > Bitcoin.

14 T. Parveen et al.

Table 7 Aggregated weighted IF decision matrix

M-banking E-banking E-commerce Bitcoin

Ease of execution 0.183884 0.255358 0.156926 0.100577

0.757801 0.697408 0.797194 0.868419

0.058315 0.047235 0.045881 0.031004

Consequence 0.147386 0.217667 0.196691 0.168249

0.810194 0.706206 0.735234 0.782333

0.04242 0.076127 0.068075 0.049418

Threat 0.245943 0.238439 0.238439 0.184903

0.670312 0.682626 0.682626 0.761561

0.083745 0.078935 0.078935 0.053537

Vulnerability 0.117348 0.268458 0.200642 0.190279

0.847972 0.679712 0.731542 0.748004

0.034681 0.051829 0.067816 0.061717

Resiliency 0.198277 0.262619 0.141526 0.207531

0.739619 0.694046 0.816537 0.725663

0.062104 0.043335 0.041938 0.066806

Operational importance 0.17711 0.25609 0.163427 0.108865

0.765253 0.69657 0.788383 0.85868

0.057637 0.04734 0.04819 0.032455

6 Conclusion

In recent times, online transaction method is most prevailing and need of the time,and to evaluate the major online transaction method, intuitionistic fuzzy entropybased decision-making along with TOPSIS methodology approach is applied on E-commerce, E-banking, M-banking, and Bitcoin payment system based on importantcriteria such as Ease of Execution, Consequence Threat, Vulnerability, Resiliency,and Operational Importance. The objective of this study was to utilize the MCDMbased on intuitionistic fuzzy entropy based decision-making along with TOPSISmethodology to rank the most efficient online payment system based on the crite-ria which are essential while using the online transaction method. The IFS methodincludes the rating of available alternatives based on the criteria selected, and weightto each criterion is given in linguistic terms and has been characterized by IF numbers.In this further, opinions of decision-makers were aggregated using the IF operator.Based on Euclidean distance, IFPIS and IFNIS are obtained and thus closeness coef-ficients of alternatives are calculated with which alternatives were ranked. Basedon the analysis, E-banking is termed as most effective online payment method, fol-lowing E-commerce and M-banking, whereas Bitcoin payment method is not muchpopular amongmasses, though it is reliable and safe method but with certain securityrisks which are discussed in Sect. 2. The proposed methodology can be extended to

Intuitionistic Fuzzy Shannon Entropy Weight… 15

Table 8 IFPIS and IFNIS

I+ I−

Ease of execution 0.255358 0.100577

0.697408 0.868419

0.047235 0.031004

Consequence 0.217667 0.147386

0.706206 0.810194

0.076127 0.04242

Threat 0.245943 0.184903

0.670312 0.761561

0.083745 0.053537

Vulnerability 0.268458 0.117348

0.679712 0.847972

0.051829 0.034681

Resiliency 0.262619 0.141526

0.694046 0.816537

0.043335 0.041938

Operational importance 0.25609 0.108865

0.69657 0.85868

0.04734 0.032455

Table 9 Separation measures of closeness coefficient

δi+ δi− C j Rank

E-banking 0.004 0.132 0.971 1

E-commerce 0.079 0.069 0.466 2

M-banking 0.089 0.069 0.437 3

Bitcoin 0.108 0.05 0.316 4

be applied in varied fields of decision-making and management decision problems.Though it turned out, based on decision-maker’s opinion bitcoin is least favorable incurrent scenario but it is a future technology and blockchain is secure enough thatit can contend with existing online payment methods. We will extend our study tocompare Bitcoin cryptocurrency with various payment methods with application indifferent fields.

16 T. Parveen et al.

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