Applications of Fuzzy Theory in Applied Sciences and Computer Applications
Published on 13. May 2024
165 pages
979-8-89113-740-0 (ISBN)
Description
In the realm of computational intelligence, the age-old adage, "e;not everything is black and white,"e; has never been more pertinent. Through the lens of fuzzy logic and neutrosophic systems, Applications of Fuzzy Theory in Applied Sciences and Computer Applications unravels the complex tapestry of uncertainty, imprecision, and subjectivity in real-world scenarios. This book stands as a testament to the power of fuzzy systems in bridging the gap between theoretical concepts and their pragmatic applications. Chapter one introduces readers to the ever-evolving realm of biometrics, specifically focusing on face recognition. The complexities of human features demand a system that can navigate the nuances, and fuzzy pattern recognition presents a promising solution. Diving deeper into the world of health and demography in chapter two, the authors explore the potential of fuzzy inference systems in mortality studies. By incorporating a degree of uncertainty, these systems offer a more holistic view of mortality trends and predictions. From biometrics and health, we shift our focus to the mathematical underpinnings of neutrosophic logic in chapter three. Here, the application of neutrosophy over digraphs is meticulously dissected, offering readers an intricate understanding of this unique conjunction. The subsequent chapters delve into various other fields, from reliability analysis and higher education to fisheries and supply chain management, demonstrating the pervasive influence of fuzzy and neutrosophic systems in diverse domains. Chapter twelve concludes this enlightening journey with a life-altering application - the utilization of fuzzy logic in the detection and classification of brain tumors. Through this chapter, the profound impact of fuzzy systems on healthcare and diagnostics is underscored. This promises a transformative journey, ensuring that by the end, readers will look at uncertainty not as a challenge, but as an opportunity to explore, innovate, and excel. Whether you're a seasoned researcher, a professional seeking innovative solutions, or a curious student, this book has insights to offer to everyone. Welcome to a world where the lines are blurred, but the visions are clear.
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Animesh Kumar Sharma
Applications of Fuzzy Theory in Applied Sciences and Computer Applications
Book
05/2024
Nova Science Publishers Inc
€126.26
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Content
- Intro
- Contents
- Preface
- Acknowledgment
- List of Tables
- List of Figures
- Chapter 1
- Fuzzy Pattern Recognition for Face Recognition in Biometrics
- Abstract
- 1.1. Introduction
- 1.2. Different Kinds of Biometrics
- 1.2.1. Deoxyribonucleic Acid (DNA)
- 1.2.2. Ear
- 1.2.3. Face
- 1.2.4. Fingerprint
- 1.2.5. Retinal Scan
- 1.2.6. Voice
- 1.3. Face Recognition
- 1.3.1. Face Detection
- 1.3.2. Feature Extraction
- 1.3.3. Feature Matching
- 1.3.4. Face Recognition
- 1.3.5. Granting/Restricting Access
- 1.4. Pattern Determining and Face Recognition Methods
- 1.4.1. The Eigenfaces Method
- 1.4.1.1. Generating Eigenfaces
- 1.4.1.2. Eigenfaces Method Benefits
- 1.4.1.3. Limitations of the Eigenfaces Method
- 1.4.2. The Fisherfaces Method
- 1.4.2.1. Fisherfaces Method Benefits
- 1.4.2.2. Fisherfaces Method Constraints
- 1.4.3. The Laplacianfaces Method
- 1.4.3.1. Laplacianfaces Method Benefits
- 1.4.3.2. Laplacianfaces Method Constraints
- 1.4.4. The Fuzzy Method
- 1.4.4.1. Fuzzy Method Benefits
- 1.4.4.2. Fuzzy Method Constraints
- 1.4.5. FPBL (Fuzzy Pattern-Based with Laplacianfaces)
- 1.4.5.1. Image Pixel-Wise Value Retrieval Using Fuzzy Patterns
- 1.4.5.2. Nearest Neighbor Classification
- 1.4.5.3. Principle Component Analysis
- 1.4.5.4. Adding Laplacianfaces Features
- Conclusion
- References
- Chapter 2
- Application of Fuzzy Inference System in Mortality Studies: A Review
- Abstract
- 2.1. Introduction
- 2.2. Fuzzy Inference System
- 2.3. Fuzzy Inference System in Mortality Studies
- Conclusion
- References
- Chapter 3
- Application of Neutrosophic Over Digraphs
- Abstract
- 3.1. Introduction
- 3.2. Type of Nover Digraph
- 3.2.1. Definition
- 3.2.2. Definition
- 3.2.3. Definition
- 3.2.4. Definition
- 3.2.5. Definition
- 3.2.6. Definition
- 3.2.7. Definition
- 3.2.8. Definition
- 3.2.8.1. Remarks
- 3.3. Regularity of Nover Digraph
- 3.3.1. Definition
- 3.3.1.1. Properties of Reg-Nover Digraph
- 3.4. Algorithm and Flow Chart
- 3.4.1. Proposed Algorithm
- 3.4.2. Flowchart
- 3.5. Shortest Path of Nover Digraph
- 3.5.1. Calculation for Shortest Path of Nover Digraph
- Conclusion
- References
- Chapter 4
- Reliability Analysis of a Two-Unit Repairable System Using Fuzzy Linguistic Approach
- Abstract
- 4.1. Introduction
- 4.2. System Description
- 4.3. The Function for Reliability and ATTF
- 4.4. The Steady-State Availability
- 4.5. Fuzzy Linguistic Approach
- 4.6. Numerical Illustration
- Conclusion
- References
- Chapter 5
- Application of Fuzzy Analytical Hierarchy Process in Academic Institution or University Selection for Higher Studies
- Abstract
- 5.1. Introduction
- 5.2. Preliminaries
- 5.2.1. Fuzzy Set
- 5.2.2. Triangular Fuzzy Number
- 5.2.3. Fuzzy Decision Making
- 5.2.4. Fuzzy Preferences
- 5.3. The Problem's Statement
- 5.4. Methodology
- 5.5. Illustration of the Approach
- 5.6. Proposed Methodology
- 5.6.1. The Various Criteria We Have Used Are
- 5.6.2. Universities as an Alternative
- 5.7. Evaluation of Work
- Conclusion
- References
- Chapter 6
- Impact behind Students Choosing Fisheries Course Using Neutrosophic Analysis
- Abstract
- 6.1. Introduction
- 6.2. Methodology
- 6.2.1. Problem Description
- 6.2.2. Statistical Analysis
- 6.2.3. Neutrosophic Analysis
- 6.2.4. Procedure Carried Out
- 6.2.5. Results and Discussion
- Conclusion
- References
- Chapter 7
- Industrial Internet of Things Sensor Location Using Intuitionistic Fuzzy Methods
- Abstract
- 7.1. Introduction
- 7.2. Architectures for Industrial IoT
- 7.3. Mathematical Framework
- 7.4. Sink Location Restrictions Are Intuitionistic Fuzzy
- Conclusion
- References
- Chapter 8
- Fuzzy Optimization for Decision-Making in Supply Chain Management
- Abstract
- 8.1. Introduction
- 8.2. Review of Literature
- 8.2.1. Overview of Previous Studies on Decision-Making in SCM
- 8.2.2. Discussion on Fuzzy Logic and Its Applications in Various Fields
- 8.2.3. Previous Implementations or Studies of Fuzzy Decision-Making in SCM
- 8.3. Theoretical Background
- 8.3.1. Basics of Fuzzy Logic
- 8.3.2. Introduction to Fuzzy Set Theory
- 8.3.3. Brief on Traditional Decision-Making Models in SCM
- 8.4. Methodology
- 8.4.1. Explanation of the Research Design
- 8.4.2. Data Collection Sources and Methods
- 8.4.3. Description of the Fuzzy Decision-Making Model/Tool/Framework Developed or Studied
- 8.4.4. Criteria and Variables Considered
- 8.5. Case Study/Application
- 8.5.1. Presentation of a Real-life Case Study
- 8.5.2. Background and Problem Statement
- 8.5.3. Application of the Fuzzy Decision-making Model
- 8.5.4. Data Analysis and Interpretation
- 8.6. Results
- 8.6.1. Supplier Selection
- 8.6.2. Inventory Management
- 8.6.3. Distribution Planning
- 8.6.4. Comprehensive Analysis
- 8.7. Discussion
- 8.7.1. Significance of Fuzzy Decision-Making in Tackling SCM Complexities
- 8.7.2. Benefits, Challenges, and Limitations of the Applied Model
- Conclusion
- Future Research Directions
- Suggestions for Potential Future Studies
- Areas That Could Be Explored Further
- References
- Chapter 9
- Overview of Fuzzy Logic in Wireless Sensor Networks
- Abstract
- 9.1 Introduction
- 9.2. Outline of Fuzzy Logic
- 9.3. Understanding Fuzzy Logic
- 9.4. Characteristics of Fuzzy Logic
- 9.5. Fuzzification
- 9.6. Decision Making
- 9.7. Defuzzification
- 9.8. Fuzzy Logic in WSN
- 9.9. Initial Deployment of Sensor Nodes
- 9.10. Clustering
- 9.11. Communication
- 9.13. Safety
- 9.14. Limitations
- Conclusion
- References
- Chapter 10
- Inventory Management Using Fuzzy Random Variables
- Abstract
- 10.1. Introduction
- 10.2. EOQ Model
- 10.3. Fuzzy EOQ Models
- 10.4. EPQ Model
- 10.5. Fuzzy EPQ Models
- Conclusion
- References
- Chapter 11
- A Study on the Decision-Making Factors of Challenges Facing Working Women by Using Fuzzy Rough Set Topology
- Abstract
- 11.1. Introduction
- 11.2. Basics of Rough Topology
- 11.3. Algorithm
- 11.4. Information System
- 11.5. Working Procedure
- Conclusion
- References
- Chapter 12
- Fuzzy Logic Based System for Brain Tumor Detection and Classification
- Abstract
- 12.1. Introduction
- 12.2. Literature Survey
- 12.3. Materials and Methods
- 12.3.1. Data and Its Processing
- 12.3.2. Tumor Region Detection
- 12.3.3. Thresholding
- 12.3.4. Fuzzy Logic Control
- 12.4. Result and Discussion
- Conclusion
- References
- About The Editor
- Index
- Blank Page
