Fuzzy Computing in Data Science

Sachi Nandan Mohanty, PhD, received his doctorate from IIT Kharagpur in 2015 and his PostDoc from IIT Kanpur in 2019. He has recently joined as an associate professor at VIT-AP University, Andhra Pradesh. He has edited 24 books and published more than 100 research papers in international journals and has been elected as Fellow of the Institute of Engineers and Senior member of IEEE Computer Society Hyderabad chapter. His research areas include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, and computational intelligence. Prasenjit Chatterjee, PhD, is an associate professor in the Mechanical Engineering Department at MCKV Institute of Engineering, India. He has more than 80 research papers in various international SCI journals. Dr. Chatterjee is one of the developers of a new multiple-criteria decision-making method called Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). Bui Thanh Hung, PhD, is the Director of Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Vietnam, and received his doctorate from Japan Advanced Institute of Science and Technology (JAIST) in 2013. He has published numerous research articles in international journals and conferences as well as 14 book chapters. His main research interests are natural language processing, machine learning, machine translation, text processing, data analytics, computer vision, and artificial intelligence.

• Preface xviiAcknowledgement xxi1 Band Reduction of HSI Segmentation Using FCM 1V. Saravana Kumar, S. Anantha Sivaprakasam, E.R. Naganathan, Sunil Bhutada and M. Kavitha1.1 Introduction 21.2 Existing Method 31.2.1 K-Means Clustering Method 31.2.2 Fuzzy C-Means 31.2.3 Davies Bouldin Index 41.2.4 Data Set Description of HSI 41.3 Proposed Method 51.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid 51.3.2 Band Reduction Using K-Means Algorithm 61.3.3 Band Reduction Using Fuzzy C-Means 71.4 Experimental Results 81.4.1 DB Index Graph 81.4.2 K-Means–Based PSC (EEOC) 91.4.3 Fuzzy C-Means–Based PSC (EEOC) 101.5 Analysis of Results 121.6 Conclusions 16References 172 A Fuzzy Approach to Face Mask Detection 21Vatsal Mishra, Tavish Awasthi, Subham Kashyap, Minerva Brahma, Monideepa Roy and Sujoy Datta2.1 Introduction 222.2 Existing Work 232.3 The Proposed Framework 262.4 Set-Up and Libraries Used 262.5 Implementation 272.6 Results and Analysis 292.7 Conclusion and Future Work 33References 343 Application of Fuzzy Logic to the Healthcare Industry 37Biswajeet Sahu, Lokanath Sarangi, Abhinadita Ghosh and Hemanta Kumar Palo3.1 Introduction 383.2 Background 413.3 Fuzzy Logic 423.4 Fuzzy Logic in Healthcare 453.5 Conclusions 49References 504 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database 55Sugyanta Priyadarshini and Nisrutha Dulla4.1 Introduction 564.2 Data Extraction and Interpretation 584.3 Results and Discussion 594.3.1 Per Year Publication and Citation Count 594.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic 604.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas 614.3.4 Major Contributing Countries Toward Fuzzy Research Articles 634.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis 664.3.6 Coauthorship of Authors 674.3.7 Cocitation Analysis of Cited Authors 684.3.8 Cooccurrence of Author Keywords 684.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries 704.4.1 Bibliographic Coupling of Documents 704.4.2 Bibliographic Coupling of Sources 714.4.3 Bibliographic Coupling of Authors 724.4.4 Bibliographic Coupling of Countries 734.5 Conclusion 74References 765 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling 79Rekha A. Kulkarni, Suhas H. Patil and Bithika Bishesh5.1 Introduction 805.2 History of Fuzzy Logic and Its Applications 815.3 Approximate Reasoning 825.4 Fuzzy Sets vs Classical Sets 835.5 Fuzzy Inference System 845.5.1 Characteristics of FIS 855.5.2 Working of FIS 855.5.3 Methods of FIS 865.6 Fuzzy Decision Trees 865.6.1 Characteristics of Decision Trees 875.6.2 Construction of Fuzzy Decision Trees 875.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment 885.8 Conclusion 90References 916 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model 93S. Mala and V. Umadevi6.1 Introduction 946.1.1 Aim and Scope 946.1.2 R-Tool 946.1.3 Application of Fuzzy Logic 946.1.4 Dataset 956.2 Model Study 966.2.1 Introduction to Machine Learning Method 966.2.2 Time Series Analysis 966.2.3 Components of a Time Series 976.2.4 Concepts of Stationary 996.2.5 Model Parsimony 1006.3 Methodology 1006.3.1 Exploratory Data Analysis 1006.3.1.1 Seed Types—Analysis 1016.3.1.2 Comparison of Location and Seeds 1016.3.1.3 Comparison of Season (Month) and Seeds 1036.3.2 Forecasting 1036.3.2.1 Auto Regressive Integrated Moving Average (ARIMA) 1036.3.2.2 Data Visualization 1066.3.2.3 Implementation Model 1086.4 Result Analysis 1086.5 Conclusion 110References 1107 Modified m-Polar Fuzzy Set ELECTRE-I Approach 113Madan Jagtap, Prasad Karande and Pravin Patil7.1 Introduction 1147.1.1 Objectives 1147.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculations 1157.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculation Method 1157.3 Application to Industrial Problems 1187.3.1 Cutting Fluid Selection Problem 1187.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem 1227.3.3 FMS Selection Problem 1257.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection 1307.4 Conclusions 143References 1438 Fuzzy Decision Making: Concept and Models 147Bithika Bishesh8.1 Introduction 1488.2 Classical Set 1498.3 Fuzzy Set 1508.4 Properties of Fuzzy Set 1518.5 Types of Decision Making 1538.5.1 Individual Decision Making 1538.5.2 Multiperson Decision Making 1578.5.3 Multistage Decision Making 1588.5.4 Multicriteria Decision Making 1608.6 Methods of Multiattribute Decision Making (MADM) 1628.6.1 Weighted Sum Method (WSM) 1628.6.2 Weighted Product Method (WPM) 1628.6.3 Weighted Aggregates Sum Product Assessment (WASPAS) 1638.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) 1668.7 Applications of Fuzzy Logic 1678.8 Conclusion 169References 1699 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India) 173Sanjaya Kumar Sahoo and Sukanta Chandra Swain9.1 Introduction 1749.2 Objectives and Methodology 1759.2.1 Objectives 1759.2.2 Methodology 1769.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants 1769.3.1 Psychological Variables Identified 1769.3.2 Fuzzy Logic for Solace to Migrants 1769.4 Findings 1789.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid 1789.6 Conclusion 179References 18010 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow 181B. RaviKrishna, Sirisha Potluri, J. Rethna Virgil Jeny, Guna Sekhar Sajja and Katta Subba Rao10.1 Significance of Machine Learning in Healthcare 18210.2 Cloud-Based Artificial Intelligent Secure Models 18310.3 Applications and Usage of Machine Learning in Healthcare 18310.3.1 Detecting Diseases and Diagnosis 18310.3.2 Drug Detection and Manufacturing 18310.3.3 Medical Imaging Analysis and Diagnosis 18410.3.4 Personalized/Adapted Medicine 18510.3.5 Behavioral Modification 18510.3.6 Maintenance of Smart Health Data 18510.3.7 Clinical Trial and Study 18510.3.8 Crowdsourced Information Discovery 18510.3.9 Enhanced Radiotherapy 18610.3.10 Outbreak/Epidemic Prediction 18610.4 Edge AI: For Smart Transformation of Healthcare 18610.4.1 Role of Edge in Reshaping Healthcare 18610.4.2 How AI Powers the Edge 18710.5 Edge AI-Modernizing Human Machine Interface 18810.5.1 Rural Medicine 18810.5.2 Autonomous Monitoring of Hospital Rooms—A Case Study 18810.6 Significance of Fuzzy in Healthcare 18910.6.1 Fuzzy Logic—Outline 18910.6.2 Fuzzy Logic-Based Smart Healthcare 19010.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems 19110.6.4 Applications of Fuzzy Logic in Healthcare 19310.7 Conclusion and Discussions 193References 19411 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS 197Rekha Gupta11.1 Introduction 19711.2 Video Conferencing Software and Its Major Features 19911.2.1 Video Conferencing/Meeting Software (VC/MS) for Higher Education Institutes 19911.3 Fuzzy TOPSIS 20311.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS 20311.4 Sample Numerical Illustration 20711.5 Conclusions 213References 21312 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming 215Kandarp Vidyasagar and Rajiv Kr. Dwivedi12.1 Introduction 21612.1.1 Basic Concepts of Fuzzy AHP and Goal Programming 21712.2 Research Model 22112.2.1 Average Growth Rate Calculation 22712.3 Result and Discussion 23312.4 Conclusion 234References 23413 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment 237Bipradas Bairagi13.1 Introduction 23813.2 Proposed Algorithm 24013.3 An Illustrative Example on Ergonomic Design Evaluation 24513.4 Conclusions 249References 24914 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic 253S. B. Goyal, Pradeep Bedi, Jugnesh Kumar and Prasenjit Chatterjee14.1 Introduction 25414.2 Control Approach in Wave Energy Systems 25514.3 Related Work 25714.4 Mathematical Modeling for Energy Conversion from Ocean Waves 25914.5 Proposed Methodology 26014.5.1 Wave Parameters 26114.5.2 Fuzzy-Optimizer 26214.6 Conclusion 264References 26415 The m-Polar Fuzzy TOPSIS Method for NTM Selection 267Madan Jagtap and Prasad Karande15.1 Introduction 26815.2 Literature Review 26815.3 Methodology 27015.3.1 Steps of the mFS TOPSIS 27015.4 Case Study 27215.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method 27315.4.2 Effect of Shannon’s Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method 27715.5 Results and Discussions 28115.5.1 Result Validation 28115.6 Conclusions and Future Scope 283References 28416 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology 287Bipradas Bairagi16.1 Introduction 28816.2 MCDM Techniques 28916.2.1 Fahp 28916.2.2 Entropy Method as Weights (Influence) Evaluation Technique 29016.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches 29116.3.1 Topsis 29116.3.2 FMOORA Method 29216.3.3 FVIKOR 29216.3.4 Fuzzy Grey Theory (FGT) 29316.3.5 COPRAS –G 29316.3.6 Super Hybrid Algorithm 29416.4 Illustrative Example 29516.5 Results and Discussions 29816.5.1 FTOPSIS 29816.5.2 FMOORA 29816.5.3 FVIKRA 29816.5.4 Fuzzy Grey Theory (FGT) 29916.5.5 COPRAS-G 29916.5.6 Super Hybrid Approach (SHA) 29916.6 Conclusions 302References 30217 Fuzzy MCDM on CCPM for Decision Making: A Case Study 305Bimal K. Jena, Biswajit Das, Amarendra Baral and Sushanta Tripathy17.1 Introduction 30617.2 Literature Review 30717.3 Objective of Research 30817.4 Cluster Analysis 30817.4.1 Hierarchical Clustering 30917.4.2 Partitional Clustering 30917.5 Clustering 31017.6 Methodology 31417.7 TOPSIS Method 31617.8 Fuzzy TOPSIS Method 31817.9 Conclusion 32517.10 Scope of Future Study 326References 326Index 329