Intro -- Title Page -- Copyright -- Preface -- 1 Fuzzy Fractals in Cervical Cancer -- 1.1 Introduction -- 1.2 Methods -- 1.3 Maximum Modulus Theorem -- 1.4 Results -- 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 -- 2.4 Experimental Results -- 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-4.2 Multiple Criteria That is Used for Decision Making (MCDM)-4.3 Conclusion-References-5 Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph of Diameter 5-5.1 Introduction-5.2 Main Result-5.3 Conclusion-References-6 Fuzzy Tri-Magic Labeling of Isomorphic Caterpillar Graph 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 -- 7.2 Existing Technology and its Review -- 7.3 Research Design -- 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 -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Logic System for Fuzzy -- 8.4 Proposed Algorithm -- 8.5 Results 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 -Contractive Type Mappings and -- 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 Problem by Hungarian Method: A New Approach -- 11.1 Introduction -- 11.2 Preliminary -- 11.3 Theoretical Part -- 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-12.4 An Application of the Connectedness of the Modified Fuzzy Graph in Rescuing Human Life From Fire Accident-12.5 Resolving Number Fuzzy Graph and 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-13.4 Theorem-13.5 Theorem-13.6 Theorem-13.7 Theorem-13.8 Theorem-13.9 Theorem.

13.10 Application of Fuzzy Edge Magic Total Labeling.

Includes bibliographical references and index.

Online resource; title from PDF title page (John Wiley, viewed August 30, 2021).

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.-- Provided by publisher.

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