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

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. 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. 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. Audience This book will be read and used by a broad audience including applied mathematicians, computer scientists, and industry engineers.