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E-raamat: Fuzzy Intelligent Systems: Methodologies, Techniques, and Applications

Edited by , Edited by (Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India), Edited by (Vels Institute of Science, Technology & Advanced Studies), Edited by , Edited by (Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India)
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FUZZY INTELLIGENT SYSTEMS A comprehensive guide to Expert Systems and Fuzzy Logic that is the backbone of artificial intelligence.

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.

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.
Preface xiii
1 Fuzzy Fractals in Cervical Cancer 1(26)
T. Sudha
G. Jayalalitha
1.1 Introduction
2(5)
1.1.1 Fuzzy Mathematics
2(1)
1.1.1.1 Fuzzy Set
2(1)
1.1.1.2 Fuzzy Logic
2(1)
1.1.1.3 Fuzzy Matrix
3(1)
1.1.2 Fractals
3(1)
1.1.2.1 Fractal Geometry
4(1)
1.1.3 Fuzzy Fractals
4(1)
1.1.4 Cervical Cancer
5(2)
1.2 Methods
7(8)
1.2.1 Fuzzy Method
7(4)
1.2.2 Sausage Method
11(4)
1.3 Maximum Modulus Theorem
15(3)
1.4 Results
18(3)
1.4.1 Fuzzy Method
19(1)
1.4.2 Sausage Method
20(1)
1.5 Conclusion
21(2)
References
23(4)
2 Emotion Detection in IoT-Based E-Learning Using Convolution Neural Network 27(18)
Latha Parthiban
S. Selvakumara Samy
2.1 Introduction
28(2)
2.2 Related Works
30(1)
2.3 Proposed Methodology
31(4)
2.3.1 Students Emotion Recognition Towards the Class
31(1)
2.3.2 Eye Gaze-Based Student Engagement Recognition
31(3)
2.3.3 Facial Head Movement-Based Student Engagement Recognition
34(1)
2.4 Experimental Results
35(7)
2.4.1 Convolutional Layer
35(1)
2.4.2 ReLU Layer
35(1)
2.4.3 Pooling Layer
36(1)
2.4.4 Fully Connected Layer
36(6)
2.5 Conclusions
42(1)
References
42(3)
3 Fuzzy Quotient-3 Cordial Labeling of Some Trees of Diameter 5-Part III 45(28)
P. Sumathi
J. Suresh Kumar
3.1 Introduction
46(1)
3.2 Related Work
46(1)
3.3 Definition
47(1)
3.4 Notations
47(1)
3.5 Main Results
48(23)
3.6 Conclusion
71(1)
References
71(2)
4 Classifying Fuzzy Multi-Criterion Decision Making and Evolutionary Algorithm 73(20)
Kirti Seth
Ashish Seth
4.1 Introduction
74(9)
4.1.1 Classical Optimization Techniques
74(1)
4.1.2 The Bio-Inspired Techniques Centered on Optimization
75(8)
4.1.2.1 Swarm Intelligence
77(1)
4.1.2.2 The Optimization on Ant Colony
78(4)
4.1.2.3 Particle Swarm Optimization (PSO)
82(1)
4.1.2.4 Summary of PSO
83(1)
4.2 Multiple Criteria That is Used for Decision Making (MCDM)
83(8)
4.2.1 WSM Method
86(1)
4.2.2 WPM Method
86(1)
4.2.3 Analytic Hierarchy Process (AHP)
87(2)
4.2.4 TOPSIS
89(1)
4.2.5 VIKOR
90(1)
4.3 Conclusion
91(1)
References
91(2)
5 Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph J 6 2,3,4 of Diameter 5 93(62)
P. Sumathi
C. Monigeetha
5.1 Introduction
93(2)
5.2 Main Result
95(59)
5.3 Conclusion
154(1)
References
154(1)
6 Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph J 2,3,5 of Diameter 5 155(62)
P. Sumathi
C. Monigeetha
6.1 Introduction
155(2)
6.2 Main Result
157(58)
6.3 Conclusion
215(1)
References
215(2)
7 Ceaseless Rule-Based Learning Methodology for Genetic Fuzzy Rule-Based Systems 217(26)
B. Siva Kumar Reddy
R. Balakrishna
R. Anandan
7.1 Introduction
218(5)
7.1.1 Integration of Evolutionary Algorithms and Fuzzy Logic
219(1)
7.1.2 Fuzzy Logic-Aided Evolutionary Algorithm
220(1)
7.1.3 Adaptive Genetic Algorithm That Adapt Manage Criteria
220(1)
7.1.4 Genetic Algorithm With Fuzzified Genetic Operators
220(1)
7.1.5 Genetic Fuzzy Systems
220(3)
7.1.6 Genetic Learning Process
223(1)
7.2 Existing Technology and its Review
223(10)
7.2.1 Techniques for Rule-Based Understanding with Genetic Algorithm
223(1)
7.2.2 Strategy A: GA Primarily Based Optimization for Computerized Built FLC
223(1)
7.2.3 Strategy B: GA-Based Optimization of Manually Created FLC
224(1)
7.2.4 Methods of Hybridization for GFS
225(8)
7.2.4.1 The Michigan Strategy-Classifier System
226(3)
7.2.4.2 The Pittsburgh Method
229(4)
7.3 Research Design
233(4)
7.3.1 The Ceaseless Rule Learning Approach (CRL)
233(1)
7.3.2 Multistage Processes of Ceaseless Rule Learning
234(2)
7.3.3 Other Approaches of Genetic Rule Learning
236(1)
7.4 Findings or Result Discussion so for in the Area of GFS Hybridization
237(2)
7.5 Conclusion
239(1)
References
240(3)
8 Using Fuzzy Technique Management of Configuration and Status of VM for Task Distribution in Cloud System 243(26)
Yogesh Shukla
Pankaj Kumar Mishra
Ramakant Bhardwaj
8.1 Introduction
244(1)
8.2 Literature Review
244(2)
8.3 Logic System for Fuzzy
246(2)
8.4 Proposed Algorithm
248(9)
8.4.1 Architecture of System
248(2)
8.4.2 Terminology of Model
250(2)
8.4.3 Algorithm Proposed
252(2)
8.4.4 Explanations of Proposed Algorithm
254(3)
8.5 Results of Simulation
257(3)
8.5.1 Cloud System Numerical Model
257(1)
8.5.2 Evaluation Terms Definition
258(1)
8.5.3 Environment Configurations Simulation
259(1)
8.5.4 Outcomes of Simulation
259(1)
8.6 Conclusion
260(6)
References
266(3)
9 Theorems on Fuzzy Soft Metric Spaces 269(16)
Qazi Aftab Kabir
Ramakant Bhardwaj
Ritu Shrivastava
9.1 Introduction
269(1)
9.2 Preliminaries
270(1)
9.3 FSMS
271(2)
9.4 Main Results
273(5)
9.5 Fuzzy Soft α - ψ-Contractive Type Mappings and α - Admissible Mappings
278(4)
References
282(3)
10 Synchronization of Time-Delay Chaotic System with Uncertainties in Terms of Takagi-Sugeno Fuzzy System 285(30)
Sathish Kumar Kumaravel
Suresh Rasappan
Kala Raja Mohan
10.1 Introduction
285(1)
10.2 Statement of the Problem and Notions
286(5)
10.3 Main Result
291(11)
10.4 Numerical Illustration
302(10)
10.5 Conclusion
312(1)
References
312(3)
11 Trapezoidal Fuzzy Numbers (TrFN) and its Application in Solving Assignment Problem by Hungarian Method: A New Approach 315(20)
Rahul Kar
A.K. Shaw
J. Mishra
11.1 Introduction
316(1)
11.2 Preliminary
317(2)
11.2.1 Definition
317(1)
11.2.2 Some Arithmetic Operations of Trapezoidal Fuzzy Number
318(1)
11.3 Theoretical Part
319(6)
11.3.1 Mathematical Formulation of an Assignment Problem
319(1)
11.3.2 Method for Solving an Assignment Problem
320(3)
11.3.2.1 Enumeration Method
320(1)
11.3.2.2 Regular Simplex Method
321(1)
11.3.2.3 Transportation Method
321(1)
11.3.2.4 Hungarian Method
321(2)
11.3.3 Computational Processor of Hungarian Method (For Minimization Problem)
323(2)
11.4 Application With Discussion
325(6)
11.5 Conclusion and Further Work
331(1)
References
332(3)
12 The Connectedness of Fuzzy Graph and the Resolving Number of Fuzzy Digraph 335(30)
Mary Jiny D.
R. Shanmugapriya
12.1 Introduction
336(1)
12.2 Definitions
336(5)
12.3 An Algorithm to Find the Super Resolving Matrix
341(8)
12.3.1 An Application on Resolving Matrix
344(3)
12.3.2 An Algorithm to Find the Fuzzy Connectedness Matrix
347(2)
12.4 An Application of the Connectedness of the Modified Fuzzy Graph in Rescuing Human Life From Fire Accident
349(7)
12.4.1 Algorithm to Find the Safest and Shortest Path Between Two Landmarks
352(4)
12.5 Resolving Number Fuzzy Graph and Fuzzy Digraph
356(6)
12.5.1 An Algorithm to Find the Resolving Set of a Fuzzy Digraph
360(2)
12.6 Conclusion
362(1)
References
362(3)
13 A Note on Fuzzy Edge Magic Total Labeling Graphs 365(22)
R. Shanmugapriya
P.K. Hemalatha
13.1 Introduction
365(1)
13.2 Preliminaries
366(1)
13.3 Theorem
367(3)
13.3.1 Example
368(2)
13.4 Theorem
370(4)
13.4.1 Example
371(3)
13.4.1.1 Lemma
374(1)
13.4.1.2 Lemma
374(1)
13.4.1.3 Lemma
374(1)
13.5 Theorem
374(2)
13.5.1 Example as Shown in Figure 13.5 Star Graph S(1,9) is FEMT Labeling
374(2)
13.6 Theorem
376(1)
13.7 Theorem
377(3)
13.7.1 Example
378(2)
13.8 Theorem
380(1)
13.9 Theorem
381(2)
13.10 Application of Fuzzy Edge Magic Total Labeling
383(2)
13.11 Conclusion
385(1)
References
385(2)
14 The Synchronization of Impulsive Time-Delay Chaotic Systems with Uncertainties in Terms of Takagi-Sugeno Fuzzy System 387(26)
Balaji Dharmalingam
Suresh Rasappan
V. Vijayalakshmi
G. Suseendran
14.1 Introduction
387(2)
14.2 Problem Description and Preliminaries
389(2)
14.2.1 Impulsive Differential Equations
389(2)
14.3 The T-S Fuzzy Model
391(2)
14.4 Designing of Fuzzy Impulsive Controllers
393(1)
14.5 Main Result
394(6)
14.6 Numerical Example
400(10)
14.7 Conclusion
410(1)
References
410(3)
15 Theorems on Soft Fuzzy Metric Spaces by Using Control Function 413(18)
Sneha A. Khandait
Chitra Singh
Ramakant Bhardwaj
Amit Kumar Mishra
15.1 Introduction
413(1)
15.2 Preliminaries and Definition
414(1)
15.3 Main Results
415(14)
15.4 Conclusion
429(1)
References
429(2)
16 On Soft α(γ,β) -Continuous Functions in Soft Topological Spaces 431(30)
N. Kalaivani
E. Chandrasekaran
K. Fayaz Ur Rahman
16.1 Introduction
432(1)
16.2 Preliminaries
432(6)
16.2.1 Outline
432(1)
16.2.2 Soft αγ-Open Set
432(2)
16.2.3 Soft αγTi Spaces
434(2)
16.2.4 Soft (αγ, βs)-Continuous Functions
436(2)
16.3 Soft α(γ,β)-Continuous Functions in Soft Topological Spaces
438(21)
16.3.1 Outline
438(1)
16.3.2 Soft α(γ,β)-Continuous Functions
438(6)
16.3.3 Soft α(γ,β)-Open Functions
444(3)
16.3.4 Soft α(γ,β)-Closed Functions
447(3)
16.3.5 Soft α(γ,β)-Homeomorphism
450(1)
16.3.6 Soft (αγ,β)-Contra Continuous Functions
450(5)
16.3.7 Soft α(γ,β)-Contra Continuous Functions
455(4)
16.4 Conclusion
459(1)
References
459(2)
Index 461
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 a IBMS/390 Mainframe professional, a Chartered Engineer from the Institution of Engineers in India and 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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