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Fuzzy Logic in Computational Intelligence

Applications and Case Studies

Edited by Rahul Kar, Gunjan Mukherjee, Biswadip Basu Mallik and Ashok Kumar Shaw
Series: Advances in Learning Analytics for Intelligent Cloud-IoT Systems
Copyright: 2026   |   Expected Pub Date:2026/02/30
ISBN: 9781394345663  |  Hardcover  |  
488 pages

One Line Description
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.

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.

Description
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 Nissans anti-lock brakes, Honda’s auto engines, and Mitsubishi Electrics 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.

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Author / Editor Details
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.

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Table of Contents
Preface
Part 1: Introduction to Fuzzy Logic
1. Flood Prediction Using Fuzzy Logic in Computational Intelligence: Applications and Insights
Jyotirmoy Sau, Anwesha Das and Gunjan Mukherjee
1.1 Introduction
1.2 Methodology
1.3 Application
1.4 Conclusion
Acknowledgment
References
2. Fuzzy Promethee Analysis on Attributes in Procuring Gold
Kala Raja Mohan, Regan Murugesan, R. Narmada Devi and Sathish Kumar Kumaravel
2.1 Introduction
2.2 Standard Definitions
2.2.1 Attributes
2.2.2 Sub-Attributes
2.2.3 Criteria
2.2.4 Decision Matrix
2.2.5 Normalized Decision Matrix
2.2.6 Weights
2.2.7 Weighted Normalized Matrix
2.2.8 Preference Function
2.2.9 Aggregate Preference Function
2.2.10 Leaving and Entering Flow
2.2.11 Net Out Ranking Flow
2.2.12 Rank
2.3 Promethee Algorithm
2.4 Gold as a Metal
2.4.1 Various Forms
2.4.2 Measure of Gold
2.4.3 Jewelry Making Procedure
2.4.4 Methods of Jewelry Making
2.5 Gold Procure
2.6 Conclusion
References
3. Fuzzy-Based Model for the Study of Crop Production Optimization
Soumen Santra, Sudip Barik, Subrata Jana and Anirban Sarkar
3.1 Introduction
3.2 Fuzzy-Based Model for the Study of Optimization
3.3 Literature Study on Agricultural Problems
3.4 Objectives
3.5 Input Datasets with Case Study
3.6 Methodology
3.7 Algorithms
3.8 Results and Discussion
3.8.1 Predicted Yield
3.9 Limitations and Conclusions
References
4. Application of Neutrosophic Over Hypersoft Sets in the Selection of Food Shop Locations
R. Narmada Devi and Yamini Parthiban
4.1 Introduction
4.2 Preliminary
4.3 Neutrosophic Over Hypersoft Topological Space and Tangent Similarity Measure
4.4 Algorithm
4.5 Flow Chart
4.6 Numerical Illustrations
4.7 Conclusion
References
Part 2: Fuzzy Application with AI and ML Concept
5. Application of Fuzzy Set Theory to AI and Machine Learning Domain
Gourab Dutta, Rahul Kumar Ghosh and Gunjan Mukherjee
5.1 Introduction of Fuzzy Set Theory
5.2 Fuzzy Basic Definitions
5.2.1 Definition
5.3 Applications of Fuzzy Set Theory
5.4 Artificial Intelligence Methods
5.5 Fuzzy Sets Theory and Its Extensions
5.6 Intelligent Systems Integrated with Fuzzy Sets: A Literature Review
5.7 Automated Reasoning and Inference
5.7.1 Autonomous Agents and Multi-Agent Systems
5.7.2 Case-Based Reasoning
5.7.3 Machine Learning
5.7.4 Deep Learning
5.8 Real Applications and Patents on Artificial Intelligence
Techniques and Fuzzy Sets
5.9 Conclusion
References
6. Fuzzy Logic in Machine Learning and AI Applications
Ravi Sheth and Chandresh Parekha
6.1 Introduction
6.2 Foundation of Artificial Intelligence (AI) and Machine Learning
6.3 Foundation of Fuzzy Logic
6.4 Need of Fuzzy Logic in AI and ML
6.4.1 Application of Fuzzy Logic in AI and ML
6.5 Challenges and Future Direction
6.5.1 Challenges
6.5.2 Future Directions
6.6 Conclusion
References
7. Enhancing Machine Learning Models through Adaptive Fuzzy
Logic-Based Hyperparameter Tuning
M. Robinson Joel, V. Ebenezer, K. Martin Sagayam, E. Bijolin Edwin, M. Roshni Thanka and S. Stewart Kirubakaran
7.1 Introduction
7.2 Fuzzy Logic
7.3 Adjusting Hyperparameters
7.3.1 Grid Search
7.3.2 Bayesian Optimization
7.3.3 Automated Hyperparameter Tuning
7.3.4 Regularization Parameters
7.3.5 Activation Functions
7.4 Adaptive Fuzzy Logic Components
7.4.1 Fuzzification
7.4.2 Fuzzy Rule Base
7.4.3 Inference Mechanism
7.4.4 Defuzzification
7.4.5 Logic in Decisions
7.5 Adaptive Adjustment Method
7.5.1 Feedback Cycle
7.5.2 Dynamic Modification
7.5.3 Optimization of Neural Networks
7.5.4 Self-Improving
7.5.5 Optimization with Metaheuristics
7.6 Optimization for Fuzzy Systems
7.6.1 Genetic-Based Algorithm
7.6.2 PSO (Particle Swarms Optimization)
7.6.3 Simulated Annealing (SA)
7.6.4 Ant Colony Optimization
7.6.5 Hybrid Optimization
7.7 Conventional Approaches
7.7.1 Gradient Descent-Based Optimization
7.7.2 Least Squares Method
7.7.3 Statistical Methods
7.7.4 Heuristic-Based Methods
7.8 Conclusion
References
8. Machine Learning Models with Fuzzy Logic-Based Tuning
Bhanu Pratap Singh, Arun Kumar, Ankit, Vikanksha and Jatinder Singh
8.1 Introduction
8.1.1 Fuzzy Logic: A Paradigm Shift in Tuning
8.2 Fundamentals of Fuzzy Logic
8.2.1 Definition and Concepts of Fuzzy Logic
8.2.2 Fuzzy Sets and Membership Functions
8.2.3 Fuzzy Rules and Inference Systems
8.2.4 Defuzzification Methods
8.3 Integrating Fuzzy Logic with Machine Learning
8.3.1 Concept and Benefits of Fuzzy Logic-Based Tuning
8.3.2 Handling Uncertainty
8.3.3 Flexibility in Parameter Evaluation
8.3.4 Improved Model Robustness and Performance
8.4 Fuzzy Logic-Based Tuning for Supervised Machine Learning Models
8.4.1 Fuzzy Logic for Hyperparameter Tuning
8.4.2 Fuzzy Grid and Random Search
8.5 Fuzzy Logic-Based Tuning for Unsupervised Machine Learning Models
8.5.1 Fuzzy Logic in Clustering
8.5.2 Fuzzy C-Means Clustering
8.5.3 Tuning K-Means with Fuzzy Logic
8.6 Adaptive Tuning Parameters using Fuzzy Logic Towards Reinforcement Learning Models
8.6.1 Reward Function Design Using Fuzzy Logic
8.6.2 Adaptive Learning Rates with Fuzzy Logic
8.7 Advanced Applications of Fuzzy Logic-Based Tuning
8.7.1 Fuzzy Logic in Deep Learning
8.7.2 Tuning Deep Neural Networks
8.7.3 Fuzzy Logic in CNNs and RNNs
8.8 Challenges and Limitations
8.9 Conclusion
References
9. A Tuning Perspective of Fuzzy Logic Enhanced Machine
Learning Models and Its Applications
M. Jayanthi, S. T. Shenbagavalli, M. Sowmiya and P. Kasthuri Rengan
9.1 Introduction to Fuzzy Logic Integration in Machine Learning
9.2 Fuzzy Inference Systems: Principles and Applications
9.2.1 Principles of Fuzzy Inference Systems
9.2.2 Applications of Fuzzy Inference Systems
9.2.3 Enhancing Machine Learning Models with Fuzzy Inference Systems
9.2.4 Case Study: Fuzzy Logic Controller in Washing Machines
9.3 Fuzzy Clustering Techniques for Model Enhancement
9.3.1 Key Fuzzy Clustering Techniques
9.3.1.1 Fuzzy C-Means (FCM)
9.3.1.2 Gustafson-Kessel (GK) Algorithm
9.3.1.3 Fuzzy Subtractive Clustering
9.3.2 Enhancing Machine Learning Models with Fuzzy Clustering
9.3.3 Case Study: Image Segmentation with Fuzzy C-Means
9.4 Parameter Optimization Strategies for Fuzzy Logic Models
9.4.1 Gradient-Based Methods
9.4.1.1 Gradient Descent
9.4.1.2 Stochastic Gradient Descent (SGD)
9.4.1.3 Adaptive Gradient Methods
9.4.1.4 Application in Fuzzy Logic Models
9.4.1.5 Example
9.4.2 Evolutionary Algorithms
9.4.2.1 Genetic Algorithms
9.4.2.2 Differential Evolution (DE)
9.4.2.3 Particle Swarm Optimization
9.4.3 Simulated Annealing
9.4.4 Neural Network-Based Optimization
9.4.5 Gradient-Free Optimization Techniques
9.4.6 Metaheuristic Algorithms
9.4.6.1 Ant Colony Optimization (ACO)
9.4.6.2 Artificial Bee Colony (ABC)
9.4.6.3 Firefly Algorithm (FA)
9.4.7 Hybrid Approaches
9.5 Real-World Challenges in Fuzzy Logic Model Tuning
9.6 Evaluating Fuzzy Logic-Enhanced Models: Metrics and Methods
9.6.1 Evaluation Metrics
9.6.2 Methods for Evaluation
9.7 Future Directions in Fuzzy Logic Integration with Machine Learning
9.8 Conclusion
References
Part 3: Smart Fuzzy Applications
10. Smart Choices: Revolutionizing Menstrual Health Management Through Machine Learning
Shiny Irene D. and Indra Priyadharshini S.
10.1 Introduction
10.2 M-Health
10.3 Practical Applications
10.4 Conclusion
References
11. Industrial IoT Control Systems Using Fuzzy Logic: Research Trends and Challenges
Sunita Sunil Shinde, K.M. Baalamurugan, Vinay Kumar Nassa, S. Devikala, Prerana Nilesh Khairnar and Joshuva Arockia Dhanraj
11.1 Introduction
11.2 Related Works
11.3 Research Methodology
11.4 Results and Discussion
11.5 Conclusion and Future Direction
References
12. Energy Efficiency Optimization in IoT Networks Using Fuzzy Logic Control
Zatin Gupta, Y. Krishnapriya, Talari Manohar, Bhadrappa Haralayya, K. Suresh and Isha Chopra
12.1 Introduction
12.2 Related Works
12.3 Research Methodology
12.4 Results and Discussion
12.5 Conclusion
Bibliography
Part 4: Fuzzy Optimization
13. Fuzzy Mathematics Approaches in Multilevel Converter Design and Optimization
Yogeesh N.
13.1 Introduction
13.1.1 Multilevel Inverters and Fuzzy Mathematics
13.1.1.1 Power Electronics Fundamentals
13.1.2 Topologies of Multilevel Converters
13.1.3 Application and Scope of Fuzzy Mathematics
13.1.4 Importance of Fuzzy Logic in Multilevel Converter Design
13.2 Mathematical Foundations of Fuzzy Set Theory
13.2.1 Basics of Fuzzy Set Theory
13.2.2 Mathematical Framework for Fuzzy Logic Control
13.3 Fuzzy Logic Control Strategies for Multilevel Converters
13.3.1 Fuzzy-Based Modulation Techniques
13.3.2 Mathematical Formulation of Fuzzy Logic Controllers
13.4 Fuzzy Optimization Techniques in Multilevel Converter Design
13.4.1 Fuzzy-Based Parameter Optimization
13.4.2 Example: Fuzzy-Based Modulation Index Optimization
13.5 Fuzzy Decision-Making in Multilevel Converter Operation
13.5.1 Fuzzy Inference Systems for Decision-Making
13.5.2 Example: Fuzzy Decision-Making for Multilevel Converter
13.6 Approaches Hybrid: Fuzzy Mathematics and Mathematical Models
13.6.1 The Role of Fuzzy Logic in Mathematical Models
13.6.2 Synergies Between Fuzzy Mathematics and Mathematical Optimization
13.7 Case Studies: Using Fuzzy Logic in Multilevel Converter Design
13.7.1 Application of Fuzzy Logic in Switching Frequency Control
13.7.2 Mathematical Analysis of Fuzzy-Optimized Multilevel Converters
13.8 Challenges and Future Directions in Fuzzy Mathematics for Multilevel Converters
13.9 Conclusion
References
14. Optimizing Curriculum Design with Fuzzy Logic in Computational Intelligence: Insights from Educational Research
Visweswara Rao Vempali, Pallavi Sachin Patil, B. Umadevi, S. Someshwar, Joshuva Arockia Dhanraj and N. Rao Cheepurupalli
14.1 Introduction
14.2 Related Works
14.3 Research Methodology
14.4 Results and Discussion
14.5 Conclusion
References
15. Optimizing Supply Chain Management Using Fuzzy Logic
Control Systems
Neha Verma, Pandit B. Shinde, K. Suresh Kumar, M. K. Sharma, Bhadrappa Haralayya and Joshuva Arockia Dhanraj
15.1 Introduction
15.2 Related Works
15.3 Research Methodology
15.4 Results and Discussion
15.5 Conclusion
References
Part 5: Fuzzy Applications in the AI Paradigm 291
16. Personalized Learning Enhancement Through Fuzzy Logic-Based Adaptive Educational Systems
Mamta Thakur, M. Vanisree, Melanie Lourens, T. Vijay Muni,
Bhadrappa Haralayya and Joshuva Arockia Dhanraj
16.1 Introduction
16.2 Related Works
16.3 Research Methodology
16.4 Results and Discussion
16.5 Conclusion and Future Direction
References
17. Computational Intelligence Using Fuzzy Logic in Educational Data Analysis A Study of Methodologies and Applications
Hari Prasadarao Pydi, Hima Bindu Gogineni, Sammaiah Buhukya, Prasanta Chatterjee Biswas, Bhadrappa Haralayya and N. Rao Cheepurupalli
17.1 Introduction
17.2 Related Works
17.3 Research Methodology
17.4 Results and Discussion
17.5 Conclusion
References
18. Fuzzy Logic-Based Decision Support System for Strategic
Management in Dynamic Environments
Dilip S. Shelar, Deepali Suhas Jadhav, K. Suresh Kumar, M. K. Sharma, Bhadrappa Haralayya and Joshuva Arockia Dhanraj
18.1 Introduction
18.2 Related Works
18.3 Research Methodology
18.4 Results and Discussion
18.5 Conclusion
References
19. Analysis of Fuzzy-Based Image Enhancement Methods Toward Thyroid Cytopathology Diagnosis Through Fuzzy Logic: A Case Study
B. Gopinath and R. Santhi
19.1 Introduction
19.2 Fuzzy Image Enhancement Methods
19.2.1 Fuzzy Histogram Equalization
19.2.2 Fuzzy Filtering Techniques
19.2.3 Fuzzy Morphological Operations
19.2.4 Fuzzy Contrast Enhancement
19.3 Thyroid Cytopathology Diagnosis Through Fuzzy Logic: A Case Study
19.3.1 Methodology
19.4 Conclusion
References
Part 6: Fuzzy Techniques towards the Health Sector
20. Decision Making Framework with Extent Analysis Method of Fuzzy AHP for Eye Health Management
Ayan Shee, Subrata Jana, Ivnil Ghosh, Sudipta Banerjee, Anirban Sarkar and Partha Sen
20.1 Introduction
20.1.1 Importance of Eye Health Management
20.1.2 Context
20.1.3 MCDM Techniques
20.1.4 Beneficiaries
20.1.5 Novelties
20.1.6 Organization of the Work
20.2 Related Works
20.2.1 Research Gap
20.2.2 Objectives
20.3 Research Methodology
20.3.1 Criteria Selection
20.3.2 Arithmetic Operations and Triangular Fuzzy Numbers
20.3.3 Fuzzy Analytic Hierarchy Process
20.3.4 Extent Analysis Method of Fuzzy Analytic Hierarchy Process
20.4 Calculations
20.4.1 Scale of Relative Importance
20.4.2 Calculation of Pair-Wise Comparison Matrix
20.4.3 Fuzzy Scale of Relative Importance
20.4.4 Calculation of Fuzzy Pair-Wise Comparison Matrix
20.4.5 Calculations with “Extent Analysis Method of Fuzzy Analytic Hierarchy Process”
20.5 Findings
20.6 Conclusions
References
21. Intuitionistic Fuzzy Soft Set Framework for Diagnosing
Infectious Diseases
Devangi Sojitra, Minakshi Biswas Hathiwala, Gautam Hathiwala and Khanjan M. Trivedi
21.1 Introduction
21.2 Fuzziness in Medical Diagnosis
21.3 Preliminaries
21.3.1 Fuzzy Sets
21.3.2 Soft Sets
21.3.2.1 Definition
21.3.2.2 Definition
21.3.2.3 Example
21.3.2.4 Operations on SS
21.3.3 Fuzzy Soft Set
21.3.3.1 Definition
21.3.3.2 Definition
21.3.3.3 Example
21.3.3.4 Operations on FSSs
21.3.4 Intuitionistic Fuzzy Sets
21.3.4.1 Definition
21.3.4.2 Definitions
21.3.4.3 Definition
21.3.4.4 Example
21.3.4.5 Operations on IFSs
21.3.5 Intuitionistic Fuzzy Soft Set
21.3.5.1 Definition
21.3.5.2 Definition
21.3.5.3 Example
21.3.5.4 Operations on IFSSs
21.3.5.5 Remark
21.3.5.6 Definition
21.3.5.7 Definition
21.3.5.8 Remark
21.3.5.9 Definition
21.4 Infectious Diseases
21.5 IFSS Approach in Medical Diagnosis
21.5.1 Algorithm
21.5.2 Case Study
21.6 Conclusion
References
Part 7: Fuzzy Techniques in the Management Paradigm
22. Computational Intelligence in Fuzzy Logic-Based Financial
Risk Management: A Comparative Study of Methods and Models
Chetan Shelke, Harini. B., Bhadrappa Haralayya, Dharini Raje Sisodia, S. Shalini and Joshuva Arockia Dhanraj
22.1 Introduction
22.2 Related Works
22.3 Research Methodology
22.4 Results and Discussion
22.5 Conclusion
References
23. Risk Assessment in Business Management Using Computational Intelligence A Fuzzy Logic Perspective
Sakshi Khatri, Priyanka Salgotra, S. Subramanian, Bhadrappa Haralayya, K. Suresh Kumar and Geetha Manoharan
23.1 Introduction
23.2 Related Works
23.3 Research Methodology
23.4 Results and Discussion
23.5 Conclusion and Future Direction
References
Part 8: Fuzzy Logic in the Security Management
24. Fraud Detection in Financial Transactions: A Fuzzy Logic
Approach
Shaziya Islam, Priyanka Salgotra, Nitin Kulshrestha, S. Shalini, Bhadrappa Haralayya and Joshuva Arockia Dhanraj
24.1 Introduction
24.2 Related Works
24.3 Research Methodology
24.4 Results and Discussion
24.5 Conclusion
References
Index

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