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Submit a ProposalFuzzy Intelligent Systems
 | Methodologies, Techniques, and Applications Edited by E. Chandrasekaran, R. Anandan, G. Suseendran, S. Balamurugan and Hanaa Hachimi Series: Artificial Intelligence and Soft Computing for Industrial Transformation Copyright: 2021 | Status: Published ISBN: 9781119760450 | Hardcover | 484 pages Price: $225 USD  |
One Line Description
A comprehensive guide to Expert Systems and Fuzzy Logic that is the backbone of artificial intelligence.
Audience
Researchers and students in computer science, Internet of Things, artificial intelligence, machine learning, big data analytics and information and communication technology- related fields. Students will gain a thorough understanding of fuzzy control systems theory by mastering its contents.
Researchers and students in computer science, Internet of Things, artificial intelligence, machine learning, big data analytics and information and communication technology- related fields. Students will gain a thorough understanding of fuzzy control systems theory by mastering its contents.
Description
The objective in writing the book is to foster advancements in the field and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and those in education and research covering a broad cross section of technical disciplines.
Fuzzy Intelligent Systems: Methodologies, Techniques, and Applications comprises state-of-the-art chapters detailing how expert systems are built and how the fuzzy logic resembling human reasoning, powers them. Engineers, both current and future, need systematic training in the analytic theory and rigorous design of fuzzy control systems to keep up with and advance the rapidly evolving field of applied control technologies. As a consequence, expert systems with fuzzy logic capabilities make for a more versatile and innovative handling of problems. This book showcases the combination of fuzzy logic and neural networks known as a neuro-fuzzy system, which results in a hybrid intelligent system by combining a human-like reasoning style of neural networks.
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The objective in writing the book is to foster advancements in the field and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and those in education and research covering a broad cross section of technical disciplines.
Fuzzy Intelligent Systems: Methodologies, Techniques, and Applications comprises state-of-the-art chapters detailing how expert systems are built and how the fuzzy logic resembling human reasoning, powers them. Engineers, both current and future, need systematic training in the analytic theory and rigorous design of fuzzy control systems to keep up with and advance the rapidly evolving field of applied control technologies. As a consequence, expert systems with fuzzy logic capabilities make for a more versatile and innovative handling of problems. This book showcases the combination of fuzzy logic and neural networks known as a neuro-fuzzy system, which results in a hybrid intelligent system by combining a human-like reasoning style of neural networks.
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Author / Editor Details
E. Chandresekaran, PhD is a Professor of Mathematics at Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai India.
E. Chandresekaran, PhD is a Professor of Mathematics at Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai India.
R. Anandan, PhD is an IBMS/390 Mainframe professional, a Chartered Engineer from the Institution of Engineers in India, and has received a fellowship from Bose Science Society, India. He is currently a Professor in the Department of Computer Science and Engineering, School of Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai.
G. Suseendran, PhD was an assistant professor in the Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai and passed away as this book was being prepared.
S. Balamurugan, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India(RESI), India.
Hanaa Hachimi, PhD is an associate professor at the Ibn Tofail University in the National School of Applied Sciences ENSA in Kenitra, Morocco. She is President of the Moroccan Society of Engineering Sciences and Technology (MSEST).
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Table of Contents
1. Fuzzy Fractals in Cervical Cancer
T. Sudha and G. Jayalalitha
1.1 Introduction
1.1.1 Fuzzy Mathematics
1.1.1.1 Fuzzy Set
1.1.1.2 Fuzzy Logic
1.1.1.3 Fuzzy Matrix
1.1.2 Fractals
1.1.2.1 Fractal Geometry
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
Latha Parthiban and S. Selvakumara Samy
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
P. Sumathi and J. Suresh Kumar
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
Kirti Seth and Ashish Seth
4.1 Introduction
4.1.1 Classical Optimization Techniques
4.1.2 The Bio-Inspired Techniques Centered on Optimization
4.1.2.1 Swarm Intelligence
4.1.2.2 The Optimization on Ant Colony
4.1.2.3 Particle Swarm Optimization (PSO)
4.1.2.4 Summary of PSO
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 26,3,4 of Diameter 5
P. Sumathi and C. Monigeetha
5.1 Introduction
5.2 Main Result
5.3 Conclusion
References
6. Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph 26,3,5 of Diameter 5
P. Sumathi and C. Monigeetha
6.1 Introduction
6.2 Main Result
6.3 Conclusion
References
7. Ceaseless Rule-Based Learning Methodology for Genetic Fuzzy Rule-Based Systems
B. Siva Kumar Reddy, R. Balakrishna and R. Anandan
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
7.2.4.1 The Michigan Strategy—Classifier System
7.2.4.2 The Pittsburgh Method
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 of GFS Hybridization
7.5 Conclusion
References
8. Using Fuzzy Technique Management of Configuration and Status of VM for Task Distribution in Cloud System
Yogesh Shukla, Pankaj Kumar Mishra
and Ramakant Bhardwaj
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
Qazi Aftab Kabir, Ramakant Bhardwaj and Ritu Shrivastava
9.1 Introduction
9.2 Preliminaries
9.3 FSMS
9.4 Main Results
9.5 Fuzzy Soft α −ψ−Contractive Type Mappings and α − Admissible Mappings
References
10. Synchronization of Time-Delay Chaotic System with Uncertainties in Terms of Takagi–Sugeno Fuzzy System
Sathish Kumar Kumaravel, Suresh Rasappan
and Kala Raja Mohan
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 Problem by Hungarian Method: A New Approach
Rahul Kar, A.K. Shaw and J. Mishra
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
11.3.2.1 Enumeration Method
11.3.2.2 Regular Simplex Method
11.3.2.3Transportation Method
11.3.2.4 Hungarian Method
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
Mary Jiny D. and R. Shanmugapriya
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
R. Shanmugapriya and P.K. Hemalatha
13.1 Introduction
13.2 Preliminaries
13.3 Theorem
13.3.1 Example
13.4 Theorem
13.4.1 Example
13.4.1.1 Lemma
13.4.1.2 Lemma
13.4.1.3 Lemma
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 Time-Delay Chaotic Systems with Uncertainties in Terms of Takagi–Sugeno Fuzzy System
Balaji Dharmalingam, Suresh Rasappan, V. Vijayalakshmi and G. Suseendran
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
Sneha A. Khandait, Chitra Singh, Ramakant Bhardwaj and Amit Kumar Mishra
15.1 Introduction
15.2 Preliminaries and Definition
15.3 Main Results
15.4 Conclusion
References
16. On Soft α(γ,β)-Continuous Functions in Soft
Topological Spaces
N. Kalaivani, E. Chandrasekaran and K. Fayaz Ur Rahman
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, βs)-Continuous Functions
16.3 Soft α(γ,β)-Continuous Functions in Soft Topological Spaces
16.3.1 Outline
16.3.2 Soft α(γ,β)-Continuous Functions
16.3.3 Soft α(γ,β)-Open Functions
16.3.4 Soft α(γ,β)-Closed Functions
16.3.5 Soft α(γ,β)-Homeomorphism
16.3.6 Soft (αγ, βs)-Contra Continuous Functions
16.3.7 Soft α(γ,β)-Contra Continuous Functions
16.4 Conclusion
References
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1. Fuzzy Fractals in Cervical Cancer
T. Sudha and G. Jayalalitha
1.1 Introduction
1.1.1 Fuzzy Mathematics
1.1.1.1 Fuzzy Set
1.1.1.2 Fuzzy Logic
1.1.1.3 Fuzzy Matrix
1.1.2 Fractals
1.1.2.1 Fractal Geometry
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
Latha Parthiban and S. Selvakumara Samy
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
P. Sumathi and J. Suresh Kumar
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
Kirti Seth and Ashish Seth
4.1 Introduction
4.1.1 Classical Optimization Techniques
4.1.2 The Bio-Inspired Techniques Centered on Optimization
4.1.2.1 Swarm Intelligence
4.1.2.2 The Optimization on Ant Colony
4.1.2.3 Particle Swarm Optimization (PSO)
4.1.2.4 Summary of PSO
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 26,3,4 of Diameter 5
P. Sumathi and C. Monigeetha
5.1 Introduction
5.2 Main Result
5.3 Conclusion
References
6. Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph 26,3,5 of Diameter 5
P. Sumathi and C. Monigeetha
6.1 Introduction
6.2 Main Result
6.3 Conclusion
References
7. Ceaseless Rule-Based Learning Methodology for Genetic Fuzzy Rule-Based Systems
B. Siva Kumar Reddy, R. Balakrishna and R. Anandan
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
7.2.4.1 The Michigan Strategy—Classifier System
7.2.4.2 The Pittsburgh Method
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 of GFS Hybridization
7.5 Conclusion
References
8. Using Fuzzy Technique Management of Configuration and Status of VM for Task Distribution in Cloud System
Yogesh Shukla, Pankaj Kumar Mishra
and Ramakant Bhardwaj
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
Qazi Aftab Kabir, Ramakant Bhardwaj and Ritu Shrivastava
9.1 Introduction
9.2 Preliminaries
9.3 FSMS
9.4 Main Results
9.5 Fuzzy Soft α −ψ−Contractive Type Mappings and α − Admissible Mappings
References
10. Synchronization of Time-Delay Chaotic System with Uncertainties in Terms of Takagi–Sugeno Fuzzy System
Sathish Kumar Kumaravel, Suresh Rasappan
and Kala Raja Mohan
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 Problem by Hungarian Method: A New Approach
Rahul Kar, A.K. Shaw and J. Mishra
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
11.3.2.1 Enumeration Method
11.3.2.2 Regular Simplex Method
11.3.2.3Transportation Method
11.3.2.4 Hungarian Method
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
Mary Jiny D. and R. Shanmugapriya
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
R. Shanmugapriya and P.K. Hemalatha
13.1 Introduction
13.2 Preliminaries
13.3 Theorem
13.3.1 Example
13.4 Theorem
13.4.1 Example
13.4.1.1 Lemma
13.4.1.2 Lemma
13.4.1.3 Lemma
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 Time-Delay Chaotic Systems with Uncertainties in Terms of Takagi–Sugeno Fuzzy System
Balaji Dharmalingam, Suresh Rasappan, V. Vijayalakshmi and G. Suseendran
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
Sneha A. Khandait, Chitra Singh, Ramakant Bhardwaj and Amit Kumar Mishra
15.1 Introduction
15.2 Preliminaries and Definition
15.3 Main Results
15.4 Conclusion
References
16. On Soft α(γ,β)-Continuous Functions in Soft
Topological Spaces
N. Kalaivani, E. Chandrasekaran and K. Fayaz Ur Rahman
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, βs)-Continuous Functions
16.3 Soft α(γ,β)-Continuous Functions in Soft Topological Spaces
16.3.1 Outline
16.3.2 Soft α(γ,β)-Continuous Functions
16.3.3 Soft α(γ,β)-Open Functions
16.3.4 Soft α(γ,β)-Closed Functions
16.3.5 Soft α(γ,β)-Homeomorphism
16.3.6 Soft (αγ, βs)-Contra Continuous Functions
16.3.7 Soft α(γ,β)-Contra Continuous Functions
16.4 Conclusion
References
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