John Wiley & Sons Fuzzy Intelligent Systems Cover Fuzzy Intelligent Systems: Methodologies, Techniques and Applications comprises state-of-the-art cha.. Product #: 978-1-119-76045-0 Regular price: $195.33 $195.33 Auf Lager

Fuzzy Intelligent Systems

Methodologies, Techniques, and Applications

Chandrasekaran, E. / Anandan, R. / Suseendran, G. / Balamurugan, S. / Hachimi, Hanaa (Herausgeber)

Artificial Intelligence and Soft Computing for Industrial Transformation

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1. Auflage November 2021
480 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-76045-0
John Wiley & Sons

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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.

Preface xiii

1 Fuzzy Fractals in Cervical Cancer 1
T. Sudha and G. Jayalalitha

1.1 Introduction 2

1.1.1 Fuzzy Mathematics 2

1.1.1.1 Fuzzy Set 2

1.1.1.2 Fuzzy Logic 2

1.1.1.3 Fuzzy Matrix 3

1.1.2 Fractals 3

1.1.2.1 Fractal Geometry 4

1.1.3 Fuzzy Fractals 4

1.1.4 Cervical Cancer 5

1.2 Methods 7

1.2.1 Fuzzy Method 7

1.2.2 Sausage Method 11

1.3 Maximum Modulus Theorem 15

1.4 Results 18

1.4.1 Fuzzy Method 19

1.4.2 Sausage Method 20

1.5 Conclusion 21

References 23

2 Emotion Detection in IoT-Based E-Learning Using Convolution Neural Network 27
Latha Parthiban and S. Selvakumara Samy

2.1 Introduction 28

2.2 Related Works 30

2.3 Proposed Methodology 31

2.3.1 Students Emotion Recognition Towards the Class 31

2.3.2 Eye Gaze-Based Student Engagement Recognition 31

2.3.3 Facial Head Movement-Based Student Engagement Recognition 34

2.4 Experimental Results 35

2.4.1 Convolutional Layer 35

2.4.2 ReLU Layer 35

2.4.3 Pooling Layer 36

2.4.4 Fully Connected Layer 36

2.5 Conclusions 42

References 42

3 Fuzzy Quotient-3 Cordial Labeling of Some Trees of Diameter 5--Part III 45
P. Sumathi and J. Suresh Kumar

3.1 Introduction 46

3.2 Related Work 46

3.3 Definition 47

3.4 Notations 47

3.5 Main Results 48

3.6 Conclusion 71

References 71

4 Classifying Fuzzy Multi-Criterion Decision Making and Evolutionary Algorithm 73
Kirti Seth and Ashish Seth

4.1 Introduction 74

4.1.1 Classical Optimization Techniques 74

4.1.2 The Bio-Inspired Techniques Centered on Optimization 75

4.1.2.1 Swarm Intelligence 77

4.1.2.2 The Optimization on Ant Colony 78

4.1.2.3 Particle Swarm Optimization (PSO) 82

4.1.2.4 Summary of PSO 83

4.2 Multiple Criteria That is Used for Decision Making (MCDM) 83

4.2.1 WSM Method 86

4.2.2 WPM Method 86

4.2.3 Analytic Hierarchy Process (AHP) 87

4.2.4 TOPSIS 89

4.2.5 VIKOR 90

4.3 Conclusion 91

References 91

5 Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph J¯62,3,4 of Diameter 5 93
P. Sumathi and C. Monigeetha

5.1 Introduction 93

5.2 Main Result 95

5.3 Conclusion 154

References 154

6 Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph J¯6 2,3,5 of Diameter 5 155
P. Sumathi and C. Monigeetha

6.1 Introduction 155

6.2 Main Result 157

6.3 Conclusion 215

References 215

7 Ceaseless Rule-Based Learning Methodology for Genetic Fuzzy Rule-Based Systems 217
B. Siva Kumar Reddy, R. Balakrishna and R. Anandan

7.1 Introduction 218

7.1.1 Integration of Evolutionary Algorithms and Fuzzy Logic 219

7.1.2 Fuzzy Logic-Aided Evolutionary Algorithm 220

7.1.3 Adaptive Genetic Algorithm That Adapt Manage Criteria 220

7.1.4 Genetic Algorithm With Fuzzified Genetic Operators 220

7.1.5 Genetic Fuzzy Systems 220

7.1.6 Genetic Learning Process 223

7.2 Existing Technology and its Review 223

7.2.1 Techniques for Rule-Based Understanding with Genetic Algorithm 223

7.2.2 Strategy A: GA Primarily Based Optimization for Computerized Built FLC 223

7.2.3 Strategy B: GA-Based Optimization of Manually Created FLC 224

7.2.4 Methods of Hybridization for GFS 225

7.2.4.1 The Michigan Strategy--Classifier System 226

7.2.4.2 The Pittsburgh Method 229

7.3 Research Design 233

7.3.1 The Ceaseless Rule Learning Approach (CRL) 233

7.3.2 Multistage Processes of Ceaseless Rule Learning 234

7.3.3 Other Approaches of Genetic Rule Learning 236

7.4 Findings or Result Discussion so for in the Area of GFS Hybridization 237

7.5 Conclusion 239

References 240

8 Using Fuzzy Technique Management of Configuration and Status of VM for Task Distribution in Cloud System 243
Yogesh Shukla, Pankaj Kumar Mishra and Ramakant Bhardwaj

8.1 Introduction 244

8.2 Literature Review 244

8.3 Logic System for Fuzzy 246

8.4 Proposed Algorithm 248

8.4.1 Architecture of System 248

8.4.2 Terminology of Model 250

8.4.3 Algorithm Proposed 252

8.4.4 Explanations of Proposed Algorithm 254

8.5 Results of Simulation 257

8.5.1 Cloud System Numerical Model 257

8.5.2 Evaluation Terms Definition 258

8.5.3 Environment Configurations Simulation 259

8.5.4 Outcomes of Simulation 259

8.6 Conclusion 260

References 266

9 Theorems on Fuzzy Soft Metric Spaces 269
Qazi Aftab Kabir, Ramakant Bhardwaj and Ritu Shrivastava

9.1 Introduction 269

9.2 Preliminaries 270

9.3 FSMS 271

9.4 Main Results 273

9.5 Fuzzy Soft Contractive Type Mappings and Admissible Mappings 278

References 282

10 Synchronization of Time-Delay Chaotic System with Uncertainties in Terms of Takagi-Sugeno Fuzzy System 285
Sathish Kumar Kumaravel, Suresh Rasappan and Kala Raja Mohan

10.1 Introduction 285

10.2 Statement of the Problem and Notions 286

10.3 Main Result 291

10.4 Numerical Illustration 302

10.5 Conclusion 312

References 312

11 Trapezoidal Fuzzy Numbers (TrFN) and its Application in Solving Assignment Problem by Hungarian Method: A New Approach 315
Rahul Kar, A.K. Shaw and J. Mishra

11.1 Introduction 316

11.2 Preliminary 317

11.2.1 Definition 317

11.2.2 Some Arithmetic Operations of Trapezoidal Fuzzy Number 318

11.3 Theoretical Part 319

11.3.1 Mathematical Formulation of an Assignment Problem 319

11.3.2 Method for Solving an Assignment Problem 320

11.3.2.1 Enumeration Method 320

11.3.2.2 Regular Simplex Method 321

11.3.2.3 Transportation Method 321

11.3.2.4 Hungarian Method 321

11.3.3 Computational Processor of Hungarian Method (For Minimization Problem) 323

11.4 Application With Discussion 325

11.5 Conclusion and Further Work 331

References 332

12 The Connectedness of Fuzzy Graph and the Resolving Number of Fuzzy Digraph 335
Mary Jiny D. and R. Shanmugapriya

12.1 Introduction 336

12.2 Definitions 336

12.3 An Algorithm to Find the Super Resolving Matrix 341

12.3.1 An Application on Resolving Matrix 344

12.3.2 An Algorithm to Find the Fuzzy Connectedness Matrix 347

12.4 An Application of the Connectedness of the Modified Fuzzy Graph in Rescuing Human Life From Fire Accident 349

12.4.1 Algorithm to Find the Safest and Shortest Path Between Two Landmarks 352

12.5 Resolving Number Fuzzy Graph and Fuzzy Digraph 356

12.5.1 An Algorithm to Find the Resolving Set of a Fuzzy Digraph 360

12.6 Conclusion 362

References 362

13 A Note on Fuzzy Edge Magic Total Labeling Graphs 365
R. Shanmugapriya and P.K. Hemalatha

13.1 Introduction 365

13.2 Preliminaries 366

13.3 Theorem 367

13.3.1 Example 368

13.4 Theorem 370

13.4.1 Example 371

13.4.1.1 Lemma 374

13.4.1.2 Lemma 374

13.4.1.3 Lemma 374

13.5 Theorem 374

13.5.1 Example as Shown in Figure 13.5 Star Graph S(1,9) is FEMT Labeling 374

13.6 Theorem 376

13.7 Theorem 377

13.7.1 Example 378

13.8 Theorem 380

13.9 Theorem 381

13.10 Application of Fuzzy Edge Magic Total Labeling 383

13.11 Conclusion 385

References 385

14 The Synchronization of Impulsive Time-Delay Chaotic Systems with Uncertainties in Terms of Takagi-Sugeno Fuzzy System 387
Balaji Dharmalingam, Suresh Rasappan, V. Vijayalakshmi and G. Suseendran

14.1 Introduction 387

14.2 Problem Description and Preliminaries 389

14.2.1 Impulsive Differential Equations 389

14.3 The T-S Fuzzy Model 391

14.4 Designing of Fuzzy Impulsive Controllers 393

14.5 Main Result 394

14.6 Numerical Example 400

14.7 Conclusion 410

References 410

15 Theorems on Soft Fuzzy Metric Spaces by Using Control Function 413
Sneha A. Khandait, Chitra Singh, Ramakant Bhardwaj and Amit Kumar Mishra

15.1 Introduction 413

15.2 Preliminaries and Definition 414

15.3 Main Results 415

15.4 Conclusion 429

References 429

16 On Soft alpha(gamma,ß)-Continuous Functions in Soft Topological Spaces 431
N. Kalaivani, E. Chandrasekaran and K. Fayaz Ur Rahman

16.1 Introduction 432

16.2 Preliminaries 432

16.2.1 Outline 432

16.2.2 Soft alphagamma-Open Set 432

16.2.3 Soft alphagamma Ti Spaces 434

16.2.4 Soft (alphagamma, ßs)-Continuous Functions 436

16.3 Soft alpha(gamma,ß)-Continuous Functions in Soft Topological Spaces 438

16.3.1 Outline 438

16.3.2 Soft alpha(gamma,ß)-Continuous Functions 438

16.3.3 Soft alpha(gamma,ß)-Open Functions 444

16.3.4 Soft alpha(gamma,ß)-Closed Functions 447

16.3.5 Soft alpha(gamma,ß)-Homeomorphism 450

16.3.6 Soft (alphagamma, ßs)-Contra Continuous Functions 450

16.3.7 Soft alpha(gamma,ß)-Contra Continuous Functions 455

16.4 Conclusion 459

References 459

Index 461
E. Chandresekaran PhD is a Professor of Mathematics at Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai India. He has published more than 45 research papers in national and international journals and his research interests include operations research and stochastic processes.

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. He has published more than 110 research papers in international journals, has received 13 awards, authored 9 books in the computer science and engineering disciplines, and has filed 5 patents.

G. Suseendran PhD is an Assistant Professor in the Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai. He has published more than 100 research papers in international journals and edited/authored 14.

S. Balamurugan PhD is the Director of Research and Development, Intelligent Research Consultancy Services(iRCS), Coimbatore, Tamilnadu, India. He also serves as R&D Consultant for many companies, startups, SMEs and MSMEs. He has published 40 books, 200+ articles in international journals/conferences as well as 27 patents. He is Editor-in-Chief of Information Science Letters and International Journal of Robotics and Artificial Intelligence. His research interests include artificial intelligence, IoT, big data analytics, cloud computing, industrial automation and wearable computing. He is a life member of IEEE, ACM, ISTE and CSI.

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).

E. Chandrasekaran, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India; R. Anandan, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India; G. Suseendran, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India; S. Balamurugan, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India; H. Hachimi, Ibn Tofail University, Morocco