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Applications of fuzzy logic in decision making and management science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applications of fuzzy logic in decision making and management scienceedited by Subrata Jana ... [et al.].
other author:	Jana, Subrata.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	vii, 511 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Fuzzy logic.
Online resource:	https://doi.org/10.1007/978-3-031-77719-6
ISBN:	9783031777196$q(electronic bk.)

Applications of fuzzy logic in decision making and management science
Applications of fuzzy logic in decision making and management science[electronic resource] /edited by Subrata Jana ... [et al.]. - Cham :Springer Nature Switzerland :2025. - vii, 511 p. :ill. (chiefly color), digital ;24 cm. - Information systems engineering and management,v. 293004-9598 ;. - Information systems engineering and management ;2..

1. An Introduction to Fuzzy Logic in Real Time Application Paradigm -- 2. Application of Bayesian Algorithm for Impacting Social Media Marketing on Smart Electric Motorcycle Purchase Intention -- 3. Optimizing Traffic Signal Control using Fuzzy Logic: A Solution for Urban Congestion Management -- 4. Optimizing Energy Efficiency in Smart Grids Using Deep Fuzzy Nets: A Comprehensive Approach to Power Regulation and Control -- 5. Making Sense of the Messy: How Fuzzy Logic Can Help Us Solve Real-Life Problems -- 6. FUZZY BASED MACHINE LEARNING MODEL FOR SMART INFORMATION ACCUMULATION DEVELOPMENT -- 7. Synthesis of Fuzzy Sets and Their Practical Applications -- 8. Optimizing Inventory Levels in Retail: Fuzzy Logic-Based Decision Support Systems for Adaptive Inventory Management -- 9. Neutrosophic Numbers in Identifying Best Teacher Awardee using SWOT Analysis -- 10. Fuzzy Logics in Multi-Criteria Decision Making Problems -- 11. A Case Study on Pharmaceutical supplier Selection by using the Integrated Fuzzy AHP MABAC Method -- 12. A Novel Approach for Multi-Cluster-Based River Flood Early Warning System Using Fuzzy-Logic-Based Learning and Rule Optimization -- 13. Evaluating Gamification Tools for Operating Management in Industrial Engineering: A Dual-Model Approach Using Fuzzy AHP and MACBETH -- 14. Exploring the Efficacy of Machine Learning Algorithms across Diverse Feature Selection Strategies in Rice Classification Tasks -- 15. Industrial Intelligence: A Fuzzy Logic Approach -- 16. Sensors and Data driven approaches in Precision Agriculture: A Comprehensive Review -- 17. Fuzzy Logic Control for Adaptive Fan Speed Regulation Based on Temperature Variations in Cold, Warm, and Hot Seasons -- 18. Improving Diabetic Patient Care through Fuzzy Logic: A Comprehensive Approach -- 19. Causal Analysis of Heart Failure Mortality among Females: A Fuzzy Logic Approach -- 20. Design and Realization of Interval Type-2 Fuzzy Logic Controller for a TITO Aero-Dynamic System -- 21. Modeling a Fuzzy Transportation Problem for Securing Mission Critical Servers using Attack Graph -- 22. The ranking function-based defuzzication technique and application of type-2 fuzzy logic to study a triple goal-based 4D-transportation problem with carbon emission effect -- 23. Exploring Machine Learning Techniques for Enhanced Chronic Kidney Disease Diagnosis: A Comprehensive Survey -- 24. Enhancing Fuzzy Multi Criteria Decision Making Technique in Engineering Design Problem -- 25. The Assessment of Customer Satisfaction of a Product in a Manufacturing System using Adaptive Neuro Fuzzy Interference System -- 26. Application of Neutrosophic Pythagorean Supra Topological Spaces in Attribute Decision-Making -- 27. Application of Fuzzy Logic to Model and Control Rotavirus Spread Among Vaccinated and Unvaccinated Children.

The fuzzy logic theory is a branch of mathematics dealing with uncertainty in measurement of any quantity or any estimation. The concept of fuzzy logic uses membership functions. The range of values from various functions or operations determines their construction. A defined rules set can create an application process and membership controls. Fuzzy applications include control system engineering, image processing, power engineering, industrial automation, robotics, consumer electronics and AI. Artificial intelligence, machine learning and expert systems have various applications that address complicated issues. The fuzzy logic inference rules have solved many problems in manufacturing and other industries. Auto engines by Honda, lift control by Mitsubishi Electric, palmtop computers by Hitachi, dishwashers by Matsushita and anti-lock brakes by Nissan are examples of corporations using machine-learning techniques with fuzzy principles. Fuzzy approaches and rule sets interpret computer vision, machine learning and evolution. Fuzzy sets can govern decision rules. Several areas use fuzzy systems in different ways. Computer vision, image processing and meta heuristic evolutionary computing are typical face research applications. Fuzzy theories can optimise and fine-tune the classifier model. Fuzzy theory is used in management, stock market analysis, information retrieval, linguistics, and behavioural science with good results. Fuzzy applications are seen in data mining and stock market prediction. The fuzzy machine learning model in the ensemble pattern accurately classifies and predicts all kinds of tasks. Fuzzy theories help maintain high accuracy. For categorisation and prediction, the ensemble pattern uses fuzzy concepts. The constant growth of fuzzy domain leads to several categorisation and prediction methods. Fuzzy type 2 and intuitionistic fuzzy logic exhibit promise accuracy and versatility. Such fuzzy logic variations can readily overcome the drawbacks of the simple fuzzy model. The book has been developed keeping in view about readers of different categories starting from the students to the professionals and researchers as well. The development of the book and its content layout will be done so meticulously proving the enough insights of the subjects to the readers so that the readers can easily pursue their research concept from the book. Overall the book serve as the purpose of repository of good amount of information and their technical presentations.

ISBN: 9783031777196$q(electronic bk.)

Standard No.: 10.1007/978-3-031-77719-6doiSubjects--Topical Terms:

181981
Fuzzy logic.

LC Class. No.: QA9.64

Dewey Class. No.: 511.313

Applications of fuzzy logic in decision making and management science
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505 0 $a 1. An Introduction to Fuzzy Logic in Real Time Application Paradigm -- 2. Application of Bayesian Algorithm for Impacting Social Media Marketing on Smart Electric Motorcycle Purchase Intention -- 3. Optimizing Traffic Signal Control using Fuzzy Logic: A Solution for Urban Congestion Management -- 4. Optimizing Energy Efficiency in Smart Grids Using Deep Fuzzy Nets: A Comprehensive Approach to Power Regulation and Control -- 5. Making Sense of the Messy: How Fuzzy Logic Can Help Us Solve Real-Life Problems -- 6. FUZZY BASED MACHINE LEARNING MODEL FOR SMART INFORMATION ACCUMULATION DEVELOPMENT -- 7. Synthesis of Fuzzy Sets and Their Practical Applications -- 8. Optimizing Inventory Levels in Retail: Fuzzy Logic-Based Decision Support Systems for Adaptive Inventory Management -- 9. Neutrosophic Numbers in Identifying Best Teacher Awardee using SWOT Analysis -- 10. Fuzzy Logics in Multi-Criteria Decision Making Problems -- 11. A Case Study on Pharmaceutical supplier Selection by using the Integrated Fuzzy AHP MABAC Method -- 12. A Novel Approach for Multi-Cluster-Based River Flood Early Warning System Using Fuzzy-Logic-Based Learning and Rule Optimization -- 13. Evaluating Gamification Tools for Operating Management in Industrial Engineering: A Dual-Model Approach Using Fuzzy AHP and MACBETH -- 14. Exploring the Efficacy of Machine Learning Algorithms across Diverse Feature Selection Strategies in Rice Classification Tasks -- 15. Industrial Intelligence: A Fuzzy Logic Approach -- 16. Sensors and Data driven approaches in Precision Agriculture: A Comprehensive Review -- 17. Fuzzy Logic Control for Adaptive Fan Speed Regulation Based on Temperature Variations in Cold, Warm, and Hot Seasons -- 18. Improving Diabetic Patient Care through Fuzzy Logic: A Comprehensive Approach -- 19. Causal Analysis of Heart Failure Mortality among Females: A Fuzzy Logic Approach -- 20. Design and Realization of Interval Type-2 Fuzzy Logic Controller for a TITO Aero-Dynamic System -- 21. Modeling a Fuzzy Transportation Problem for Securing Mission Critical Servers using Attack Graph -- 22. The ranking function-based defuzzication technique and application of type-2 fuzzy logic to study a triple goal-based 4D-transportation problem with carbon emission effect -- 23. Exploring Machine Learning Techniques for Enhanced Chronic Kidney Disease Diagnosis: A Comprehensive Survey -- 24. Enhancing Fuzzy Multi Criteria Decision Making Technique in Engineering Design Problem -- 25. The Assessment of Customer Satisfaction of a Product in a Manufacturing System using Adaptive Neuro Fuzzy Interference System -- 26. Application of Neutrosophic Pythagorean Supra Topological Spaces in Attribute Decision-Making -- 27. Application of Fuzzy Logic to Model and Control Rotavirus Spread Among Vaccinated and Unvaccinated Children.
520 $a The fuzzy logic theory is a branch of mathematics dealing with uncertainty in measurement of any quantity or any estimation. The concept of fuzzy logic uses membership functions. The range of values from various functions or operations determines their construction. A defined rules set can create an application process and membership controls. Fuzzy applications include control system engineering, image processing, power engineering, industrial automation, robotics, consumer electronics and AI. Artificial intelligence, machine learning and expert systems have various applications that address complicated issues. The fuzzy logic inference rules have solved many problems in manufacturing and other industries. Auto engines by Honda, lift control by Mitsubishi Electric, palmtop computers by Hitachi, dishwashers by Matsushita and anti-lock brakes by Nissan are examples of corporations using machine-learning techniques with fuzzy principles. Fuzzy approaches and rule sets interpret computer vision, machine learning and evolution. Fuzzy sets can govern decision rules. Several areas use fuzzy systems in different ways. Computer vision, image processing and meta heuristic evolutionary computing are typical face research applications. Fuzzy theories can optimise and fine-tune the classifier model. Fuzzy theory is used in management, stock market analysis, information retrieval, linguistics, and behavioural science with good results. Fuzzy applications are seen in data mining and stock market prediction. The fuzzy machine learning model in the ensemble pattern accurately classifies and predicts all kinds of tasks. Fuzzy theories help maintain high accuracy. For categorisation and prediction, the ensemble pattern uses fuzzy concepts. The constant growth of fuzzy domain leads to several categorisation and prediction methods. Fuzzy type 2 and intuitionistic fuzzy logic exhibit promise accuracy and versatility. Such fuzzy logic variations can readily overcome the drawbacks of the simple fuzzy model. The book has been developed keeping in view about readers of different categories starting from the students to the professionals and researchers as well. The development of the book and its content layout will be done so meticulously proving the enough insights of the subjects to the readers so that the readers can easily pursue their research concept from the book. Overall the book serve as the purpose of repository of good amount of information and their technical presentations.
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