Details

<p>Preface xvii</p> <p>Acknowledgement xxi</p> <p><b>1 Band Reduction of HSI Segmentation Using FCM 1<br /> </b><i>V. Saravana Kumar, S. Anantha Sivaprakasam, E.R. Naganathan, Sunil Bhutada and M. Kavitha</i></p> <p>1.1 Introduction 2</p> <p>1.2 Existing Method 3</p> <p>1.2.1 K-Means Clustering Method 3</p> <p>1.2.2 Fuzzy C-Means 3</p> <p>1.2.3 Davies Bouldin Index 4</p> <p>1.2.4 Data Set Description of HSI 4</p> <p>1.3 Proposed Method 5</p> <p>1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid 5</p> <p>1.3.2 Band Reduction Using K-Means Algorithm 6</p> <p>1.3.3 Band Reduction Using Fuzzy C-Means 7</p> <p>1.4 Experimental Results 8</p> <p>1.4.1 DB Index Graph 8</p> <p>1.4.2 K-Means–Based PSC (EEOC) 9</p> <p>1.4.3 Fuzzy C-Means–Based PSC (EEOC) 10</p> <p>1.5 Analysis of Results 12</p> <p>1.6 Conclusions 16</p> <p>References 17</p> <p><b>2 A Fuzzy Approach to Face Mask Detection 21<br /> </b><i>Vatsal Mishra, Tavish Awasthi, Subham Kashyap, Minerva Brahma, Monideepa Roy and Sujoy Datta</i></p> <p>2.1 Introduction 22</p> <p>2.2 Existing Work 23</p> <p>2.3 The Proposed Framework 26</p> <p>2.4 Set-Up and Libraries Used 26</p> <p>2.5 Implementation 27</p> <p>2.6 Results and Analysis 29</p> <p>2.7 Conclusion and Future Work 33</p> <p>References 34</p> <p><b>3 Application of Fuzzy Logic to the Healthcare Industry 37<br /> </b><i>Biswajeet Sahu, Lokanath Sarangi, Abhinadita Ghosh and Hemanta Kumar Palo</i></p> <p>3.1 Introduction 38</p> <p>3.2 Background 41</p> <p>3.3 Fuzzy Logic 42</p> <p>3.4 Fuzzy Logic in Healthcare 45</p> <p>3.5 Conclusions 49</p> <p>References 50</p> <p><b>4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database 55<br /> </b><i>Sugyanta Priyadarshini and Nisrutha Dulla</i></p> <p>4.1 Introduction 56</p> <p>4.2 Data Extraction and Interpretation 58</p> <p>4.3 Results and Discussion 59</p> <p>4.3.1 Per Year Publication and Citation Count 59</p> <p>4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic 60</p> <p>4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas 61</p> <p>4.3.4 Major Contributing Countries Toward Fuzzy Research Articles 63</p> <p>4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis 66</p> <p>4.3.6 Coauthorship of Authors 67</p> <p>4.3.7 Cocitation Analysis of Cited Authors 68</p> <p>4.3.8 Cooccurrence of Author Keywords 68</p> <p>4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries 70</p> <p>4.4.1 Bibliographic Coupling of Documents 70</p> <p>4.4.2 Bibliographic Coupling of Sources 71</p> <p>4.4.3 Bibliographic Coupling of Authors 72</p> <p>4.4.4 Bibliographic Coupling of Countries 73</p> <p>4.5 Conclusion 74</p> <p>References 76</p> <p><b>5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling 79<br /> </b><i>Rekha A. Kulkarni, Suhas H. Patil and Bithika Bishesh</i></p> <p>5.1 Introduction 80</p> <p>5.2 History of Fuzzy Logic and Its Applications 81</p> <p>5.3 Approximate Reasoning 82</p> <p>5.4 Fuzzy Sets vs Classical Sets 83</p> <p>5.5 Fuzzy Inference System 84</p> <p>5.5.1 Characteristics of FIS 85</p> <p>5.5.2 Working of FIS 85</p> <p>5.5.3 Methods of FIS 86</p> <p>5.6 Fuzzy Decision Trees 86</p> <p>5.6.1 Characteristics of Decision Trees 87</p> <p>5.6.2 Construction of Fuzzy Decision Trees 87</p> <p>5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment 88</p> <p>5.8 Conclusion 90</p> <p>References 91</p> <p><b>6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model 93<br /> </b><i>S. Mala and V. Umadevi</i></p> <p>6.1 Introduction 94</p> <p>6.1.1 Aim and Scope 94</p> <p>6.1.2 R-Tool 94</p> <p>6.1.3 Application of Fuzzy Logic 94</p> <p>6.1.4 Dataset 95</p> <p>6.2 Model Study 96</p> <p>6.2.1 Introduction to Machine Learning Method 96</p> <p>6.2.2 Time Series Analysis 96</p> <p>6.2.3 Components of a Time Series 97</p> <p>6.2.4 Concepts of Stationary 99</p> <p>6.2.5 Model Parsimony 100</p> <p>6.3 Methodology 100</p> <p>6.3.1 Exploratory Data Analysis 100</p> <p>6.3.1.1 Seed Types—Analysis 101</p> <p>6.3.1.2 Comparison of Location and Seeds 101</p> <p>6.3.1.3 Comparison of Season (Month) and Seeds 103</p> <p>6.3.2 Forecasting 103</p> <p>6.3.2.1 Auto Regressive Integrated Moving Average (ARIMA) 103</p> <p>6.3.2.2 Data Visualization 106</p> <p>6.3.2.3 Implementation Model 108</p> <p>6.4 Result Analysis 108</p> <p>6.5 Conclusion 110</p> <p>References 110</p> <p><b>7 Modified m-Polar Fuzzy Set ELECTRE-I Approach 113<br /> </b><i>Madan Jagtap, Prasad Karande and Pravin Patil</i></p> <p>7.1 Introduction 114</p> <p>7.1.1 Objectives 114</p> <p>7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculations 115</p> <p>7.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculation Method 115</p> <p>7.3 Application to Industrial Problems 118</p> <p>7.3.1 Cutting Fluid Selection Problem 118</p> <p>7.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem 122</p> <p>7.3.3 FMS Selection Problem 125</p> <p>7.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection 130</p> <p>7.4 Conclusions 143</p> <p>References 143</p> <p><b>8 Fuzzy Decision Making: Concept and Models 147<br /> </b><i>Bithika Bishesh</i></p> <p>8.1 Introduction 148</p> <p>8.2 Classical Set 149</p> <p>8.3 Fuzzy Set 150</p> <p>8.4 Properties of Fuzzy Set 151</p> <p>8.5 Types of Decision Making 153</p> <p>8.5.1 Individual Decision Making 153</p> <p>8.5.2 Multiperson Decision Making 157</p> <p>8.5.3 Multistage Decision Making 158</p> <p>8.5.4 Multicriteria Decision Making 160</p> <p>8.6 Methods of Multiattribute Decision Making (MADM) 162</p> <p>8.6.1 Weighted Sum Method (WSM) 162</p> <p>8.6.2 Weighted Product Method (WPM) 162</p> <p>8.6.3 Weighted Aggregates Sum Product Assessment (WASPAS) 163</p> <p>8.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) 166</p> <p>8.7 Applications of Fuzzy Logic 167</p> <p>8.8 Conclusion 169</p> <p>References 169</p> <p><b>9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India) 173<br /> </b><i>Sanjaya Kumar Sahoo and Sukanta Chandra Swain</i></p> <p>9.1 Introduction 174</p> <p>9.2 Objectives and Methodology 175</p> <p>9.2.1 Objectives 175</p> <p>9.2.2 Methodology 176</p> <p>9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants 176</p> <p>9.3.1 Psychological Variables Identified 176</p> <p>9.3.2 Fuzzy Logic for Solace to Migrants 176</p> <p>9.4 Findings 178</p> <p>9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid 178</p> <p>9.6 Conclusion 179</p> <p>References 180</p> <p><b>10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow 181<br /> </b><i>B. RaviKrishna, Sirisha Potluri, J. Rethna Virgil Jeny, Guna Sekhar Sajja and Katta Subba Rao</i></p> <p>10.1 Significance of Machine Learning in Healthcare 182</p> <p>10.2 Cloud-Based Artificial Intelligent Secure Models 183</p> <p>10.3 Applications and Usage of Machine Learning in Healthcare 183</p> <p>10.3.1 Detecting Diseases and Diagnosis 183</p> <p>10.3.2 Drug Detection and Manufacturing 183</p> <p>10.3.3 Medical Imaging Analysis and Diagnosis 184</p> <p>10.3.4 Personalized/Adapted Medicine 185</p> <p>10.3.5 Behavioral Modification 185</p> <p>10.3.6 Maintenance of Smart Health Data 185</p> <p>10.3.7 Clinical Trial and Study 185</p> <p>10.3.8 Crowdsourced Information Discovery 185</p> <p>10.3.9 Enhanced Radiotherapy 186</p> <p>10.3.10 Outbreak/Epidemic Prediction 186</p> <p>10.4 Edge AI: For Smart Transformation of Healthcare 186</p> <p>10.4.1 Role of Edge in Reshaping Healthcare 186</p> <p>10.4.2 How AI Powers the Edge 187</p> <p>10.5 Edge AI-Modernizing Human Machine Interface 188</p> <p>10.5.1 Rural Medicine 188</p> <p>10.5.2 Autonomous Monitoring of Hospital Rooms—A Case Study 188</p> <p>10.6 Significance of Fuzzy in Healthcare 189</p> <p>10.6.1 Fuzzy Logic—Outline 189</p> <p>10.6.2 Fuzzy Logic-Based Smart Healthcare 190</p> <p>10.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems 191</p> <p>10.6.4 Applications of Fuzzy Logic in Healthcare 193</p> <p>10.7 Conclusion and Discussions 193</p> <p>References 194</p> <p><b>11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS 197<br /> </b><i>Rekha Gupta</i></p> <p>11.1 Introduction 197</p> <p>11.2 Video Conferencing Software and Its Major Features 199</p> <p>11.2.1 Video Conferencing/Meeting Software (VC/MS) for Higher Education Institutes 199</p> <p>11.3 Fuzzy TOPSIS 203</p> <p>11.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS 203</p> <p>11.4 Sample Numerical Illustration 207</p> <p>11.5 Conclusions 213</p> <p>References 213</p> <p><b>12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming 215<br /> </b><i>Kandarp Vidyasagar and Rajiv Kr. Dwivedi</i></p> <p>12.1 Introduction 216</p> <p>12.1.1 Basic Concepts of Fuzzy AHP and Goal Programming 217</p> <p>12.2 Research Model 221</p> <p>12.2.1 Average Growth Rate Calculation 227</p> <p>12.3 Result and Discussion 233</p> <p>12.4 Conclusion 234</p> <p>References 234</p> <p><b>13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment 237<br /> </b><i>Bipradas Bairagi</i></p> <p>13.1 Introduction 238</p> <p>13.2 Proposed Algorithm 240</p> <p>13.3 An Illustrative Example on Ergonomic Design Evaluation 245</p> <p>13.4 Conclusions 249</p> <p>References 249</p> <p><b>14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic 253<br /> </b><i>S. B. Goyal, Pradeep Bedi, Jugnesh Kumar and Prasenjit Chatterjee</i></p> <p>14.1 Introduction 254</p> <p>14.2 Control Approach in Wave Energy Systems 255</p> <p>14.3 Related Work 257</p> <p>14.4 Mathematical Modeling for Energy Conversion from Ocean Waves 259</p> <p>14.5 Proposed Methodology 260</p> <p>14.5.1 Wave Parameters 261</p> <p>14.5.2 Fuzzy-Optimizer 262</p> <p>14.6 Conclusion 264</p> <p>References 264</p> <p><b>15 The m-Polar Fuzzy TOPSIS Method for NTM Selection 267<br /> </b><i>Madan Jagtap and Prasad Karande</i></p> <p>15.1 Introduction 268</p> <p>15.2 Literature Review 268</p> <p>15.3 Methodology 270</p> <p>15.3.1 Steps of the mFS TOPSIS 270</p> <p>15.4 Case Study 272</p> <p>15.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method 273</p> <p>15.4.2 Effect of Shannon’s Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method 277</p> <p>15.5 Results and Discussions 281</p> <p>15.5.1 Result Validation 281</p> <p>15.6 Conclusions and Future Scope 283</p> <p>References 284</p> <p><b>16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology 287<br /> </b><i>Bipradas Bairagi</i></p> <p>16.1 Introduction 288</p> <p>16.2 MCDM Techniques 289</p> <p>16.2.1 Fahp 289</p> <p>16.2.2 Entropy Method as Weights (Influence) Evaluation Technique 290</p> <p>16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches 291</p> <p>16.3.1 Topsis 291</p> <p>16.3.2 FMOORA Method 292</p> <p>16.3.3 FVIKOR 292</p> <p>16.3.4 Fuzzy Grey Theory (FGT) 293</p> <p>16.3.5 COPRAS –G 293</p> <p>16.3.6 Super Hybrid Algorithm 294</p> <p>16.4 Illustrative Example 295</p> <p>16.5 Results and Discussions 298</p> <p>16.5.1 FTOPSIS 298</p> <p>16.5.2 FMOORA 298</p> <p>16.5.3 FVIKRA 298</p> <p>16.5.4 Fuzzy Grey Theory (FGT) 299</p> <p>16.5.5 COPRAS-G 299</p> <p>16.5.6 Super Hybrid Approach (SHA) 299</p> <p>16.6 Conclusions 302</p> <p>References 302</p> <p><b>17 Fuzzy MCDM on CCPM for Decision Making: A Case Study 305<br /> </b><i>Bimal K. Jena, Biswajit Das, Amarendra Baral and Sushanta Tripathy</i></p> <p>17.1 Introduction 306</p> <p>17.2 Literature Review 307</p> <p>17.3 Objective of Research 308</p> <p>17.4 Cluster Analysis 308</p> <p>17.4.1 Hierarchical Clustering 309</p> <p>17.4.2 Partitional Clustering 309</p> <p>17.5 Clustering 310</p> <p>17.6 Methodology 314</p> <p>17.7 TOPSIS Method 316</p> <p>17.8 Fuzzy TOPSIS Method 318</p> <p>17.9 Conclusion 325</p> <p>17.10 Scope of Future Study 326</p> <p>References 326</p> <p>Index 329</p>

<p><b>Sachi Nandan Mohanty, PhD,</b> 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. <p><b>Prasenjit Chatterjee, PhD,</b> 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). <p><b>Bui Thanh Hung, PhD,</b> 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.

<p><b>This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges.</b> <p>The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. <p><b>Audience</b> <p>Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.

US