Fuzzy logic applications in computer science and mathematics

FUZZY LOGIC APPLICATIONS IN COMPUTER SCIENCE AND MATHEMATICSTICS The prime objective of developing this book is to provide meticulous details about the basic and advanced concepts of fuzzy logic and its all-around applications to different fields of mathematics and engineering. The basic steps of fu...

Descripción completa

Detalles Bibliográficos
Otros Autores: Kar, Rahul, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley & Sons, Inc [2023]
Edición:1st ed
Colección:Advances in learning analytics for intelligent cloud-IoT systems
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811326506719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Chapter 1 Decision Making Using Fuzzy Logic Using Multicriteria
  • 1.1 Introduction
  • 1.2 Fuzzy Logic
  • 1.3 Decision Making
  • 1.4 Literature Review
  • 1.5 Conclusion
  • Acknowledgment
  • References
  • Chapter 2 Application of Fuzzy Logic in the Context of Risk Management
  • 2.1 Introduction
  • 2.2 Objectives of Risk Management
  • 2.3 Improved Risk Estimation
  • 2.3.1 Point-Wise Calculations on a Curve
  • 2.3.2 Estimation of a Curve
  • 2.3.3 Accuracy in Quantification is Raised
  • 2.3.4 The Problems with the Basic Quantification Approach
  • 2.4 Threat at Quantification Matrix
  • 2.4.1 Qualitative Matrix
  • 2.4.2 Errors in Scaling
  • 2.4.3 Band Width at Various Scales
  • 2.5 Fundamental Definitions
  • 2.5.1 Positioning Statement
  • 2.5.2 Risk Under the Level of Tolerance
  • 2.5.3 Risk Elimination
  • 2.6 Fuzzy Logic
  • 2.7 Risk Related to Fuzzy Matrix
  • 2.8 Conclusion
  • Bibliography
  • Chapter 3 Use of Fuzzy Logic for Controlling Greenhouse Environment: A Study Through the Lens of Web Monitoring
  • 3.1 Introduction
  • 3.2 Design (Hardware)
  • 3.2.1 Sensor for Measuring Soil Moisture
  • 3.2.2 Sensor for Measuring Humidity and Temperature
  • 3.3 Programming Arduino Mega Board
  • 3.3.1 Fuzzification
  • 3.3.2 Fuzzy Inference
  • 3.3.3 Communication via Remote Connections and a Web Server
  • 3.4 Implementation of a Prototype
  • 3.5 Results
  • 3.6 Conclusion
  • Bibliography
  • Chapter 4 Fuzzy Logics and Marketing Decisions
  • 4.1 Introduction
  • 4.2 Literature
  • 4.2.1 Fuzzy Logic (FL)
  • 4.2.2 FL Application in Marketing
  • 4.2.2.1 Communication and Advertising
  • 4.2.2.2 Customer Service and Satisfaction
  • 4.2.2.3 Customer Segmentation
  • 4.2.2.4 CRM
  • 4.2.2.5 Pricing
  • 4.2.2.6 Evaluation of a Product
  • 4.2.2.7 Uncertainty in the Development of New Products.
  • 4.2.2.8 Decision Making
  • 4.2.2.9 Consumer Nation Identity (CNI)
  • 4.2.2.10 Quality of Service
  • 4.3 Conclusion
  • 4.4 Further Studies
  • References
  • Chapter 5 A Method for Ranking Fuzzy Numbers Based on Their Value, Ambiguity, Fuzziness, and Vagueness
  • 5.1 Introduction
  • 5.2 Preliminaries
  • 5.2.1 Definitions and Concepts
  • 5.3 The Designed Method
  • 5.4 Validate the Reasonableness of the Suggested Ranking Algorithm
  • 5.5 Comparative Analysis and Numerical Examples
  • 5.6 Application
  • 5.7 Conclusions
  • References
  • Chapter 6 Evacuation of Attributes to Translucent TNSET in Mathematics Using Rough Topology
  • 6.1 Introduction
  • 6.2 Basic Concepts of Rough Topology
  • 6.2.1 Conditional Attribute
  • 6.2.2 Decision Attribute
  • 6.2.3 Rough Topology
  • 6.2.4 Lower Approximation
  • 6.2.5 Upper Approximation
  • 6.2.6 Boundary Region
  • 6.2.7 Basis
  • 6.2.8 Information System
  • 6.2.9 Core
  • 6.3 Algorithm
  • 6.4 Information System
  • 6.5 Working Procedure
  • 6.6 Conclusion
  • References
  • Chapter 7 Design of Type-2 Fuzzy Controller for Hybrid Multi-Area Power System
  • 7.1 Introduction
  • 7.2 Plant Model
  • 7.3 Controller Design
  • 7.3.1 Proportional Integral Derivative (PID) Controller
  • 7.3.2 Fractional Order Proportional Integral Derivative (FOPID) Controller
  • 7.3.3 Type-2-Fuzzy Logic
  • 7.4 Levenberg-Marquardt Algorithm
  • 7.5 Optimization of Controller Parameters Using CASO Algorithm
  • 7.6 Result and Analysis
  • 7.6.1 Without Disturbances
  • 7.6.2 With Disturbances
  • 7.7 Conclusion
  • Appendix
  • References
  • Chapter 8 Alzheimer's Detection and Classification Using Fine-Tuned Convolutional Neural Network
  • 8.1 Introduction
  • 8.2 Literature Review
  • 8.3 Methodology
  • 8.3.1 Dataset
  • 8.3.2 Pre-Processing
  • 8.4 Implementation and Results
  • 8.5 Conclusion
  • References.
  • Chapter 9 Design of Fuzzy Logic-Based Smart Cars Using Scilab
  • 9.1 Introduction
  • 9.2 Literature Survey
  • 9.2.1 Fuzzy Logic for Automobile Industry
  • 9.2.2 Fuzzy Logic for Smart Cars
  • 9.2.3 Fuzzy Logic for Driver Behavior Detection
  • 9.2.4 Fuzzy Logic Applications for Common Industry
  • 9.3 Proposed Fuzzy Inference System for Smart Cars
  • 9.3.1 Fuzzification
  • 9.3.2 Membership Functions
  • 9.3.3 Rule Base
  • 9.3.4 Example Rules
  • 9.3.5 Defuzzification
  • 9.4 Implementation Details and Results
  • 9.5 Conclusion and Future Work
  • References
  • Chapter 10 Financial Planning and Decision Making for Students Using Fuzzy Logic
  • 10.1 Introduction
  • 10.2 Literature Review
  • 10.3 System Architecture
  • 10.3.1 Input
  • 10.3.2 Fuzzification
  • 10.3.3 Membership Function
  • 10.3.3.1 Necessity
  • 10.3.3.2 Cost Percentage
  • 10.3.3.3 Quality
  • 10.3.4 Fuzzy Rule Base
  • 10.3.5 Fuzzy Output
  • 10.3.6 Defuzzification
  • 10.4 Conclusion and Future Scope
  • References
  • Chapter 11 A Novel Fuzzy Logic (FL) Algorithm for the Automatic Detection of Oral Cancer
  • 11.1 Introduction
  • 11.1.1 Significance of Pre-Processing
  • 11.2 Image Enhancement
  • 11.3 Gabor Transform
  • 11.4 Image Transformation
  • 11.5 Adaptive Networks: Architecture
  • 11.5.1 Classification of Images
  • 11.6 Results and Discussions
  • 11.7 Conclusion
  • Bibliography
  • Chapter 12 A Study on Decision Making of Difficulties Faced by Indian Workers Abroad by Using Rough Topology
  • 12.1 Introduction
  • 12.1.1 Problems Faced by the Indian Workers
  • 12.2 Fundamental Idea of Rough Topology
  • 12.2.1 Conditional Attribute
  • 12.2.2 Decision Attribute
  • 12.2.3 Rough Topology
  • 12.2.4 Lower Approximation
  • 12.2.5 Upper Approximation
  • 12.2.6 Boundary Region
  • 12.2.7 Basis
  • 12.2.8 Information System
  • 12.2.9 Core
  • 12.3 Algorithm
  • 12.4 Information System
  • 12.5 Working Procedure.
  • 12.6 Conclusion
  • References
  • Chapter 13 Case Study on Fuzzy Logic: Fuzzy Logic-Based PID Controller to Tune the DC Motor Speed
  • 13.1 Introduction
  • 13.1.1 DC Motor
  • 13.1.2 DC Motor Speed Control Methods
  • 13.1.2.1 PID Controller
  • 13.1.2.2 Fuzzy-Based PID Controller
  • 13.1.2.3 Micro Controller-Based PID Controller
  • 13.1.2.4 Genetic Algorithm-Based PID Controller
  • 13.2 Literature Review
  • 13.2.1 Common Findings
  • 13.2.2 Comparative Analysis of Research Works Reviewed
  • 13.2.3 Strengths in the Literature Reviewed
  • 13.2.4 Weaknesses in the Literature Reviewed
  • 13.2.5 Findings in the Literature Reviewed
  • 13.3 Design of Fuzzy-Based PID Controller
  • 13.3.1 Fuzzy Controller
  • 13.3.2 Flowchart for Fuzzy Controller
  • 13.3.3 Fuzzy Logic Controller Membership Function and FAM Table
  • 13.3.4 Rules for the Fuzzy Controller
  • 13.3.5 Simulation Diagram of FLC
  • 13.3.6 Fuzzy-Based PID Controller
  • 13.3.6.1 Fuzzy Block Design
  • 13.3.6.2 Flowchart for Fuzzy-PID Controller
  • 13.3.6.3 Simulation Diagram of Fuzzy-PID Controller
  • 13.4 Experimental Work and Results Analysis
  • 13.5 Conclusion and Future Scope
  • References
  • Chapter 14 Application of Intuitionistic Fuzzy Network Using Efficient Domination
  • 14.1 Introduction
  • 14.2 Efficient Domination in Intuitionistic Fuzzy Graph (IFG)
  • 14.3 Main Frame Work
  • 14.3.1 Construction of IFN from Sub IFN
  • 14.4 Secret Key
  • 14.4.1 Encryption Algorithm
  • 14.4.2 Decryption Algorithm
  • 14.5 Illustration
  • 14.5.1 Construction of IFN from Sub IFN
  • 14.5.2 Secret Key
  • 14.5.3 Encryption Algorithm
  • 14.5.4 Decryption Algorithm
  • 14.6 Conclusion
  • References
  • Chapter 15 Analysis of Parameters Related to Malaria with Comparative Study on Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps
  • 15.1 Introduction
  • 15.2 Parameters of Malaria
  • 15.3 Fuzzy Cognitive Map.
  • 15.3.1 Matrix Representation of FCM
  • 15.4 Neutrosophic Cognitive Map
  • 15.4.1 Matrix Representation of NCM
  • 15.5 Comparison and Discussion
  • 15.6 Conclusion
  • References
  • Chapter 16 Applications of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Analysis of Dengue Fever
  • 16.1 Introduction
  • 16.2 Parameters of Dengue
  • 16.3 Fuzzy Cognitive Maps
  • 16.3.1 Matrix Representation of FCM
  • 16.4 Neutrosophic Cognitive Map
  • 16.4.1 Matrix Representation of NCM
  • 16.5 Comparison and Discussion
  • 16.6 Conclusion
  • References
  • Chapter 17 A Comprehensive Review and Analysis of the Plethora of Branches of Medical Science and Bioinformatics Based on Fuzzy Logic
  • 17.1 Introduction
  • 17.2 Previous Work
  • 17.3 Fuzzy Logic in Medical Fields and Bioinformatics
  • 17.3.1 Applied Fuzzy Logic in Medical Areas
  • 17.3.2 Applied Fuzzy Logic in Bioinformatics
  • 17.4 Review of Published Work and In-Depth Analysis
  • 17.5 Conclusion
  • References
  • Index
  • EULA.