Fuzzy Intelligent Systems: Methodologies, Techniques, and Applications (Artificial Intelligence and Soft Computing for Industrial Transformation)

✍ Scribed by R. Anandan (editor), E. Chandrasekaran (editor), G. Suseendran (editor), S. Balamurugan (editor), Hanaa Hachimi (editor)

• Publisher: Wiley-Scrivener
• Year: 2021
• Tongue: English
• Leaves: 496
• Edition: 1
• Category: Library

✦ Synopsis

Fuzzy Intelligent Systems: Methodologies, Techniques and Applications comprises state-of-the-art chapters detailing how expert systems are built and the fuzzy logic resembling human reasoning powering them. Hybrid and neuro-fuzzy intelligent systems are discussed along with Evolutionary and, in particular, Genetic Algorithms. This approach has been extended by using Multiobjective Evolutionary Algorithms, which can consider multiple conflicting objectives instead of a single one. The book also discusses the hybridization between Multiobjective Evolutionary Algorithms and Fuzzy Systems which is known as Multiobjective Evolutionary Fuzzy Systems.

✦ Table of Contents

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Fuzzy Fractals in Cervical Cancer
1.1 Introduction
1.1.1 Fuzzy Mathematics
1.1.2 Fractals
1.1.3 Fuzzy Fractals
1.1.4 Cervical Cancer
1.2 Methods
1.2.1 Fuzzy Method
1.2.2 Sausage Method
1.3 Maximum Modulus Theorem
1.4 Results
1.4.1 Fuzzy Method
1.4.2 Sausage Method
1.5 Conclusion
References
2 Emotion Detection in IoT-Based E-Learning Using Convolution Neural Network
2.1 Introduction
2.2 Related Works
2.3 Proposed Methodology
2.3.1 Students Emotion Recognition Towards the Class
2.3.2 Eye Gaze-Based Student Engagement Recognition
2.3.3 Facial Head Movement-Based Student Engagement Recognition
2.4 Experimental Results
2.4.1 Convolutional Layer
2.4.2 ReLU Layer
2.4.3 Pooling Layer
2.4.4 Fully Connected Layer
2.5 Conclusions
References
3 Fuzzy Quotient-3 Cordial Labeling of Some Trees of Diameter 5—Part III
3.1 Introduction
3.2 Related Work
3.3 Definition
3.4 Notations
3.5 Main Results
3.6 Conclusion
References
4 Classifying Fuzzy Multi-Criterion Decision Making and Evolutionary Algorithm
4.1 Introduction
4.1.1 Classical Optimization Techniques
4.1.2 The Bio-Inspired Techniques Centered on Optimization
4.2 Multiple Criteria That is Used for Decision Making (MCDM)
4.2.1 WSM Method
4.2.2 WPM Method
4.2.3 Analytic Hierarchy Process (AHP)
4.2.4 TOPSIS
4.2.5 VIKOR
4.3 Conclusion
References
5 Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph J6 2,3,4, of Diameter 5
5.1 Introduction
5.2 Main Result
5.3 Conclusion
References
6 Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph J6 2,3,4, of Diameter 5
6.1 Introduction
6.2 Main Result
6.3 Conclusion
References
7 Ceaseless Rule-Based Learning Methodology for Genetic Fuzzy Rule-Based Systems
7.1 Introduction
7.1.1 Integration of Evolutionary Algorithms and Fuzzy Logic
7.1.2 Fuzzy Logic-Aided Evolutionary Algorithm
7.1.3 Adaptive Genetic Algorithm That Adapt Manage Criteria
7.1.4 Genetic Algorithm With Fuzzified Genetic Operators
7.1.5 Genetic Fuzzy Systems
7.1.6 Genetic Learning Process
7.2 Existing Technology and its Review
7.2.1 Techniques for Rule-Based Understanding with Genetic Algorithm
7.2.2 Strategy A: GA Primarily Based Optimization for Computerized Built FLC
7.2.3 Strategy B: GA Based Optimization of Manually Created FLC
7.2.4 Methods of Hybridization for GFS
7.3 Research Design
7.3.1 The Ceaseless Rule Learning Approach (CRL)
7.3.2 Multistage Processes of Ceaseless Rule Learning
7.3.3 Other Approaches of Genetic Rule Learning
7.4 Findings or Result Discussion so for in the Area
7.5 Conclusion
References
8 Using Fuzzy Technique Management of Configuration and Status of VM for Task Distribution in Cloud System
8.1 Introduction
8.2 Literature Review
8.3 Logic System for Fuzzy
8.4 Proposed Algorithm
8.4.1 Architecture of System
8.4.2 Terminology of Model
8.4.3 Algorithm Proposed
8.4.4 Explanations of Proposed Algorithm
8.5 Results of Simulation
8.5.1 Cloud System Numerical Model
8.5.2 Evaluation Terms Definition
8.5.3 Environment Configurations Simulation
8.5.4 Outcomes of Simulation
8.6 Conclusion
References
9 Theorems on Fuzzy Soft Metric Spaces
9.1 Introduction
9.2 Preliminaries
9.3 FSMS
9.4 Main Results
9.5 Fuzzy Soft ᾶ – ψ – Contractive Type Mappings and ᾶ – Admissible Mappings
References
10 Synchronization of Time-Delay Chaotic System with Uncertainties in Terms of Takagi–Sugeno Fuzzy System
10.1 Introduction
10.2 Statement of the Problem and Notions
10.3 Main Result
10.4 Numerical Illustration
10.5 Conclusion
References
11 Trapezoidal Fuzzy Numbers (TrFN) and its Application in Solving Assignment Problems by Hungarian Method: A New Approach
11.1 Introduction
11.2 Preliminary
11.2.1 Definition
11.2.2 Some Arithmetic Operations of Trapezoidal Fuzzy Number
11.3 Theoretical Part
11.3.1 Mathematical Formulation of an Assignment Problem
11.3.2 Method for Solving an Assignment Problem
11.3.3 Computational Processor of Hungarian Method (For Minimization Problem)
11.4 Application With Discussion
11.5 Conclusion and Further Work
References
12 The Connectedness of Fuzzy Graph and the Resolving Number of Fuzzy Digraph
12.1 Introduction
12.2 Definitions
12.3 An Algorithm to Find the Super Resolving Matrix
12.3.1 An Application on Resolving Matrix
12.3.2 An Algorithm to Find the Fuzzy Connectedness Matrix
12.4 An Application of the Connectedness of the Modified Fuzzy Graph in Rescuing Human Life From Fire Accident
12.4.1 Algorithm to Find the Safest and Shortest Path Between Two Landmarks
12.5 Resolving Number Fuzzy Graph and Fuzzy Digraph
12.5.1 An Algorithm to Find the Resolving Set of a Fuzzy Digraph
12.6 Conclusion
References
13 A Note on Fuzzy Edge Magic Total Labeling Graphs
13.1 Introduction
13.2 Preliminaries
13.3 Theorem
13.3.1 Example
13.4 Theorem
13.4.1 Example
13.5 Theorem
13.5.1 Example as Shown in Figure 13.5 Star Graph S(1,9) is FEMT Labeling
13.6 Theorem
13.7 Theorem
13.7.1 Example
13.8 Theorem
13.9 Theorem
13.10 Application of Fuzzy Edge Magic Total Labeling
13.11 Conclusion
References
14 The Synchronization of Impulsive TimeDelay Chaotic Systems with Uncertainties in Terms of Takagi–Sugeno Fuzzy System
14.1 Introduction
14.2 Problem Description and Preliminaries
14.2.1 Impulsive Differential Equations
14.3 The T–S Fuzzy Model
14.4 Designing of Fuzzy Impulsive Controllers
14.5 Main Result
14.6 Numerical Example
14.7 Conclusion
References
15 Theorems on Soft Fuzzy Metric Spaces by Using Control Function
15.1 Introduction
15.2 Preliminaries and Definition
15.3 Main Results
15.4 Conclusion
References
16 On Soft a(γ,ß)-Continuous Functions in Soft Topological Spaces
16.1 Introduction
16.2 Preliminaries
16.2.1 Outline
16.2.2 Soft αγ-Open Set
16.2.3 Soft αγ Ti Spaces
16.2.4 Soft (αγ, ßs)-Continuous Functions
16.3 Soft α(γ,ß)-Continuous Functions in Soft Topological Spaces
16.3.1 Outline
16.3.2 Soft α(γ,ß)-Continuous Functions
16.3.3 Soft α(γ,ß)-Open Functions
16.3.4 Soft α(γ,ß)-Closed Functions
16.3.5 Soft α(γ,ß)-Homeomorphism
16.3.6 Soft (α., ßs)-Contra Continuous Functions
16.3.7 Soft α(γ,ß)-Contra Continuous Functions
16.4 Conclusion
References
Index
EULA