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<p>Preface xiii</p> <p><b>1 Decision Making Using Fuzzy Logic Using Multicriteria 1</b><br /><i>Panem Charanarur, Srinivasa Rao Gundu and J.Vijaylaxmi</i></p> <p>1.1 Introduction 2</p> <p>1.2 Fuzzy Logic 5</p> <p>1.3 Decision Making 6</p> <p>1.4 Literature Review 7</p> <p>1.5 Conclusion 10</p> <p><b>2 Application of Fuzzy Logic in the Context of Risk Management 13</b><br /><i>Sudipta Adhikary and Kaushik Banerjee</i></p> <p>2.1 Introduction 13</p> <p>2.2 Objectives of Risk Management 14</p> <p>2.3 Improved Risk Estimation 15</p> <p>2.4 Threat at Quantification Matrix 17</p> <p>2.5 Fundamental Definitions 18</p> <p>2.6 Fuzzy Logic 19</p> <p>2.7 Risk Related to Fuzzy Matrix 20</p> <p>2.8 Conclusion 26</p> <p><b>3 Use of Fuzzy Logic for Controlling Greenhouse Environment: Study Through the Lens of Web Monitoring 29</b><br /><i>Kaushik Banerjee and Sudipta Adhikary</i></p> <p>3.1 Introduction 29</p> <p>3.2 Design (Hardware) 30</p> <p>3.3 Programming Arduino Mega Board 31</p> <p>3.4 Implementation of a Prototype 34</p> <p>3.5 Results 35</p> <p>3.6 Conclusion 37</p> <p><b>4 Fuzzy Logics and Marketing Decisions 41</b><br /><i>Mohammed Majeed</i></p> <p>4.1 Introduction 41</p> <p>4.2 Literature 42</p> <p>4.3 Conclusion 46</p> <p>4.4 Further Studies 47</p> <p><b>5 A Method for Ranking Fuzzy Numbers Based on Their Value, Ambiguity, Fuzziness, and Vagueness 51</b><br /><i>Sunayana Saikia and Rituparna Chutia</i></p> <p>5.1 Introduction 51</p> <p>5.2 Preliminaries 54</p> <p>5.3 The Designed Method 56</p> <p>5.4 Validate the Reasonableness of the Suggested Ranking Algorithm 68</p> <p>5.5 Comparative Analysis and Numerical Examples 75</p> <p>5.6 Application 87</p> <p>5.7 Conclusions 94</p> <p><b>6 Evacuation of Attributes to Translucent TNSET in Mathematics Using Rough Topology 99</b><br /><i>Kala Raja Mohan, R. Narmada Devi, Nagadevi Bala Nagaram, Sathish Kumar Kumaravel and Regan Murugesan</i></p> <p>6.1 Introduction 99</p> <p>6.2 Basic Concepts of Rough Topology 100</p> <p>6.3 Algorithm 102</p> <p>6.4 Information System 102</p> <p>6.5 Working Procedure 104</p> <p>6.6 Conclusion 104</p> <p><b>7 Design of Type-2 Fuzzy Controller for Hybrid Multi-Area Power System 107</b><br /><i>Susmit Chakraborty, Arindam Mondal and Soumen Biswas</i></p> <p>7.1 Introduction 108</p> <p>7.2 Plant Model 108</p> <p>7.3 Controller Design 109</p> <p>7.4 Levenberg-Marquardt Algorithm 115</p> <p>7.5 Optimization of Controller Parameters Using CASO Algorithm 116</p> <p>7.6 Result and Analysis 116</p> <p>7.7 Conclusion 121</p> <p><b>8 Alzheimer's Detection and Classification Using Fine-Tuned Convolutional Neural Network 125</b><br /><i>Anooja Ali, Sarvamangala D. R., Meenakshi Sundaram A. and Rashmi C.</i></p> <p>8.1 Introduction 125</p> <p>8.2 Literature Review 129</p> <p>8.3 Methodology 133</p> <p>8.3.1 Dataset 134</p> <p>8.3.2 Pre-Processing 134</p> <p>8.4 Implementation and Results 134</p> <p>8.5 Conclusion 138</p> <p><b>9 Design of Fuzzy Logic-Based Smart Cars Using Scilab 143</b><br /><i>Josiga S., Maheswari R. and Subbulakshmi T.</i></p> <p>9.1 Introduction 143</p> <p>9.2 Literature Survey 145</p> <p>9.3 Proposed Fuzzy Inference System for Smart Cars 149</p> <p>9.4 Implementation Details and Results 155</p> <p>9.5 Conclusion and Future Work 156</p> <p><b>10 Financial Planning and Decision Making for Students Using Fuzzy Logic 159</b><br /><i>G. Surya Deepan and T. Subbulakshmi</i></p> <p>10.1 Introduction 159</p> <p>10.2 Literature Review 161</p> <p>10.3 System Architecture 163</p> <p>10.4 Conclusion and Future Scope 169</p> <p><b>11 A Novel Fuzzy Logic (FL) Algorithm for the Automatic Detection of Oral Cancer 173</b><br /><i>M. Praveena Kiruba bai and G. Arumugam</i></p> <p>11.1 Introduction 173</p> <p>11.2 Image Enhancement 174</p> <p>11.3 Gabor Transform 175</p> <p>11.4 Image Transformation 175</p> <p>11.5 Adaptive Networks: Architecture 176</p> <p>11.6 Results and Discussions 177</p> <p>11.7 Conclusion 177</p> <p><b>12 A Study on Decision Making of Difficulties Faced by Indian Workers Abroad by Using Rough Topology 179</b><br /><i>Nagadevi Bala Nagaram, R. Narmada Devi , Kala Raja Mohan, Regan Murugesan and Sathish Kumar Kumaravel</i></p> <p>12.1 Introduction 179</p> <p>12.2 Fundamental Idea of Rough Topology 182</p> <p>12.3 Algorithm 183</p> <p>12.4 Information System 183</p> <p>12.5 Working Procedure 185</p> <p>12.6 Conclusion 185</p> <p><b>13 Case Study on Fuzzy Logic: Fuzzy Logic-Based PID Controller to Tune the DC Motor Speed 187</b><br /><i>Devendra Kumar Somwanshi</i></p> <p>13.1 Introduction 188</p> <p>13.2 Literature Review 190</p> <p>13.3 Design of Fuzzy-Based PID Controller 196</p> <p>13.4 Experimental Work and Results Analysis 205</p> <p>13.5 Conclusion and Future Scope 207</p> <p><b>14 Application of Intuitionistic Fuzzy Network Using Efficient Domination 213</b><br /><i>A. Meenakshi, J. Senbagamalar and A. Kannan</i></p> <p>14.1 Introduction 213</p> <p>14.2 Efficient Domination in Intuitionistic Fuzzy Graph (IFG) 215</p> <p>14.3 Main Frame Work 217</p> <p>14.4 Secret Key 219</p> <p>14.5 Illustration 224</p> <p>14.6 Conclusion 231</p> <p><b>15 Analysis of Parameters Related to Malaria with Comparative Study on Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps 233</b><br /><i>Regan Murugesan, Sathish Kumar Kumaravel, Kala Raja Mohan, Narmada Devi Rathinam and Suresh Rasappan</i></p> <p>15.1 Introduction 233</p> <p>15.2 Parameters of Malaria 235</p> <p>15.3 Fuzzy Cognitive Map 235</p> <p>15.4 Neutrosophic Cognitive Map 240</p> <p>15.5 Comparison and Discussion 246</p> <p>15.6 Conclusion 247</p> <p><b>16 Applications of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Analysis of Dengue Fever 249</b><br /><i>Sathish Kumar Kumaravel, Regan Murugesan, Nagadevi Bala Nagaram, Suresh Rasappan and G. Yamini</i></p> <p>16.1 Introduction 249</p> <p>16.2 Parameters of Dengue 251</p> <p>16.3 Fuzzy Cognitive Maps 251</p> <p>16.4 Neutrosophic Cognitive Map 257</p> <p>16.5 Comparison and Discussion 263</p> <p>16.6 Conclusion 264</p> <p><b>17 A Comprehensive Review and Analysis of the Plethora of Branches of Medical Science and Bioinformatics Based on Fuzzy Logic 267</b><br /><i>Partha Sarker and Siddhartha Roy</i></p> <p>17.1 Introduction 267</p> <p>17.2 Previous Work 271</p> <p>17.3 Fuzzy Logic in Medical Fields and Bioinformatics 271</p> <p>17.4 Review of Published Work and In-Depth Analysis 273</p> <p>17.5 Conclusion 273</p> <p>References 277</p> <p>Index 279</p>

<p><b>Rahul Kar, PhD,</b> is working as a state-aided teacher of mathematics, Faculty of Kalyani Mahavidyalaya, Nadia, West Bengal, India. <p><b>Dac-Nhuong Le, PhD,</b> is an associate professor of computer science and deputy head of the Faculty of Information Technology, Haiphong University, Vietnam. <p><b>Gunjan Mukherjee, PhD,</b> is an assistant professor in the Department of Computational Science, Brainware University, Kolkata, India. <p><b>Biswadip Basu Mallik, PhD,</b> is a senior assistant professor of mathematics in the Department of Basic Sciences & Humanities, Institute of Engineering & Management, Kolkata, India. <p><b>Ashok Kumar Shaw, PhD,</b> is an applied mathematician and professor and Dean of R&D at the Budge Budge Institute of Technology, Kolkata, India.

<p><b>The prime objective of developing this book is to provide meticulous details about the basic and advanced concepts of fuzzy logic and its all-around applications to different fields of mathematics and engineering.</b> <p>The basic steps of fuzzy inference systems starting from the core foundation of the fuzzy concepts are presented in this book. The fuzzy theory is a mathematical concept and, at the same time, it is applied to many versatile engineering fields and research domains related to computer science. The fuzzy system offers some knowledge about uncertainty and is also related to the theory of probability. A fuzzy logic-based model acts as the classifier for many different types of data belonging to several classes. <p>Covered in this book are topics such as the fundamental concepts of mathematics, fuzzy logic concepts, probability and possibility theories, and evolutionary computing to some extent. The combined fields of neural network and fuzzy domain (known as the neuro-fuzzy system) are explained and elaborated. Each chapter has been produced in a very lucid manner, with grading from simple to complex to accommodate the anticipated different audiences. The application-oriented approach is the unique feature of this book. <p><b>Audience</b> <p>This book will be read and used by a broad audience including applied mathematicians, computer scientists, and industry engineers.

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