Unlock the power to navigate real-world uncertainty with this comprehensive guide, blending foundational theory with practical case studies to show you exactly how to integrate fuzzy logic and machine learning for unmatched predictive accuracy.
Machine learning has emerged as a transformative force in modern research, and the integration of fuzzy logic with machine learning techniques has shown significant promise in improving accuracy and precision. Fuzzy logic is based on the principles of uncertainty and has applications across a wide range of predictive and probabilistic environments. From agriculture to healthcare and numerous other sectors, fuzzy logic can be utilized to develop innovative and effective solutions. This book examines the pivotal role of fuzzy logic in enhancing computational systems’ ability to handle uncertainty and imprecision. The book provides a thorough introduction to fuzzy logic, explaining its fundamental concepts and theories while demonstrating its capacity to mimic human reasoning to address vagueness and ambiguity. It explores practical applications in decision-making, pattern recognition, and control systems, with examples such as Nissan’s anti-lock brakes, Honda’s auto engines, and Mitsubishi Electric’s elevator controls. Beyond artificial intelligence and machine learning, fuzzy logic finds applications in fields like evolutionary computing, computer vision, and image processing, enabling advancements in classifier optimization and fine-tuning. By integrating theoretical foundations with practical applications and future directions, the book serves as an essential resource for understanding and leveraging fuzzy logic in computational intelligence and across diverse interdisciplinary domains.
The reader will find the volume:
- Explores the core principles of fuzzy logic and its integration into computational intelligence, providing a strong theoretical foundation for researchers and practitioners;
- Features diverse case studies demonstrating the practical implementation of fuzzy logic in fields such as healthcare, robotics, finance, and engineering;
- Showcases how fuzzy systems enhance decision-making in uncertain and complex environments, offering tools to tackle real-world challenges;
- Written to cater to both beginners seeking an introduction and experts aiming to deepen their knowledge in fuzzy logic systems.
Audience
Researchers, academics, graduate students, engineers, data scientists, and AI technologists in computer science, artificial intelligence, and applied mathematics seeking advanced insights into fuzzy logic systems and their applications.
Table of Contents:
Preface xix
Part 1: Introduction to Fuzzy Logic 1
1 Flood Prediction Using Fuzzy Logic in Computational Intelligence: Applications and Insights 3
Jyotirmoy Sau, Anwesha Das and Gunjan Mukherjee
2 Fuzzy Promethee Analysis on Attributes in Procuring Gold 21
Kala Raja Mohan, Regan Murugesan, R. Narmada Devi and Sathish Kumar Kumaravel
3 Fuzzy-Based Model for the Study of Crop Production Optimization 39
Soumen Santra, Sudip Barik, Subrata Jana and Anirban Sarkar
4 Application of Neutrosophic Over Hypersoft Sets in the Selection of Food Shop Locations 55
R. Narmada Devi and Yamini Parthiban
Part 2: Fuzzy Application with AI and ML Concept 67
5 Application of Fuzzy Set Theory to AI and Machine Learning Domain 69
Gourab Dutta, Rahul Kumar Ghosh and Gunjan Mukherjee
6 Fuzzy Logic in Machine Learning and AI Applications 87
Ravi Sheth and Chandresh Parekha
7 Enhancing Machine Learning Models through Adaptive Fuzzy Logic-Based Hyperparameter Tuning 109
M. Robinson Joel, V. Ebenezer, K. Martin Sagayam, E. Bijolin Edwin, M. Roshni Thanka and S. Stewart Kirubakaran
8 Machine Learning Models with Fuzzy Logic-Based Tuning 129
Bhanu Pratap Singh, Arun Kumar, Ankit, Vikanksha and Jatinder Singh
9 A Tuning Perspective of Fuzzy Logic Enhanced Machine Learning Models and Its Applications 157
M. Jayanthi, S. T. Shenbagavalli, M. Sowmiya and P. Kasthuri Rengan
Part 3: Smart Fuzzy Applications 187
10 Smart Choices: Revolutionizing Menstrual Health Management Through Machine Learning 189
Shiny Irene D. and Indra Priyadharshini S.
11 Industrial IoT Control Systems Using Fuzzy Logic: Research Trends and Challenges 197
Sunita Sunil Shinde, K.M. Baalamurugan, Vinay Kumar Nassa, S. Devikala, Prerana Nilesh Khairnar and Joshuva Arockia Dhanraj
12 Energy Efficiency Optimization in IoT Networks Using Fuzzy Logic Control 213
Zatin Gupta, Y. Krishnapriya, Talari Manohar, Bhadrappa Haralayya, K. Suresh and Isha Chopra
Part 4: Fuzzy Optimization 231
13 Fuzzy Mathematics Approaches in Multilevel Converter Design and Optimization 233
Yogeesh N.
14 Optimizing Curriculum Design with Fuzzy Logic in Computational Intelligence: Insights from Educational Research 261
Visweswara Rao Vempali, Pallavi Sachin Patil, B. Umadevi, S. Someshwar, Joshuva Arockia Dhanraj and N. Rao Cheepurupalli
15 Optimizing Supply Chain Management Using Fuzzy Logic Control Systems 275
Neha Verma, Pandit B. Shinde, K. Suresh Kumar, M. K. Sharma, Bhadrappa Haralayya and Joshuva Arockia Dhanraj
Part 5: Fuzzy Applications in the AI Paradigm 291
16 Personalized Learning Enhancement Through Fuzzy Logic-Based Adaptive Educational Systems 293
Mamta Thakur, M. Vanisree, Melanie Lourens, T. Vijay Muni, Bhadrappa Haralayya and Joshuva Arockia Dhanraj
17 Computational Intelligence Using Fuzzy Logic in Educational Data Analysis A Study of Methodologies and Applications 309
Hari Prasadarao Pydi, Hima Bindu Gogineni, Sammaiah Buhukya, Prasanta Chatterjee Biswas, Bhadrappa Haralayya and N. Rao Cheepurupalli
18 Fuzzy Logic-Based Decision Support System for Strategic Management in Dynamic Environments 323
Dilip S. Shelar, Deepali Suhas Jadhav, K. Suresh Kumar, M. K. Sharma, Bhadrappa Haralayya and Joshuva Arockia Dhanraj
19 Analysis of Fuzzy-Based Image Enhancement Methods Toward Thyroid Cytopathology Diagnosis Through Fuzzy Logic: A Case Study 341
B. Gopinath and R. Santhi
Part 6: Fuzzy Techniques towards the Health Sector 357
20 Decision Making Framework with Extent Analysis Method of Fuzzy AHP for Eye Health Management 359
Ayan Shee, Subrata Jana, Ivnil Ghosh, Sudipta Banerjee, Anirban Sarkar and Partha Sen
21 Intuitionistic Fuzzy Soft Set Framework for Diagnosing Infectious Diseases 383
Devangi Sojitra, Minakshi Biswas Hathiwala, Gautam Hathiwala and Khanjan M. Trivedi
Part 7: Fuzzy Techniques in the Management Paradigm 411
22 Computational Intelligence in Fuzzy Logic-Based Financial Risk Management: A Comparative Study of Methods and Models 413
Chetan Shelke, Harini. B., Bhadrappa Haralayya, Dharini Raje Sisodia, S. Shalini and Joshuva Arockia Dhanraj
23 Risk Assessment in Business Management Using Computational Intelligence A Fuzzy Logic Perspective 429
Sakshi Khatri, Priyanka Salgotra, S. Subramanian, Bhadrappa Haralayya, K. Suresh Kumar and Geetha Manoharan
Part 8: Fuzzy Logic in the Security Management 443
24 Fraud Detection in Financial Transactions: A Fuzzy Logic Approach 445
Shaziya Islam, Priyanka Salgotra, Nitin Kulshrestha, S. Shalini, Bhadrappa Haralayya and Joshuva Arockia Dhanraj
References 457
Index 463
About the Author :
Rahul Kar works in the School of Mathematics, Kalyani Mahavidyalaya, Nadia, West Bengal, India and is pursuing a Ph.D. in Mathematics from Bankura University. With eight years of teaching experience, he has published many research papers and edited 15 books. He is a member of the Indian Mathematical Society and an honorary member of the Carmels Research Institute, Dakar, Senegal.
Gunjan Mukherjee, PhD is an associate professor in the Department of Computational Sciences, Brainware University, Kolkata, India. He has published many papers in journals and international conferences of repute and several books, including a series of textbooks that he solely authored. His research interests include computer vision, machine learning, soft computing, and image processing.
Biswadip Basu Mallik, PhD is an Associate Professor of Mathematics in the Department of Basic Sciences and Humanities, the Institute of Engineering and Management, Kolkata, India. He has been involved in teaching and research for more than 21 years and has published several research papers and book chapters in various scientific journals, more than 15 books, and five patents. His fields of research focus on computational fluid dynamics and mathematical modeling.
Ashok Kumar Shaw, PhD is a Professor of Mathematics, Head of the Department of Mathematics and Basic Science and Humanities, and the Dean of Research and Development at the Budge Budge Institute of Technology, Kolkata, India. He has published about 30 papers in international journals of repute, and three books. His research focuses on applied mathematics, reliability optimization, maintenance, fuzzy mathematics, inventory and supply chain management.
