Fuzzy Computing in Data Science: Applications and Challenges

Cover
Title Page
Copyright Page
Dedication Page
Contents
Preface
Acknowledgement
Chapter 1 Band Reduction of HSI Segmentation Using FCM
	1.1 Introduction
	1.2 Existing Method
		1.2.1 K-Means Clustering Method
		1.2.2 Fuzzy C-Means
		1.2.3 Davies Bouldin Index
		1.2.4 Data Set Description of HSI
	1.3 Proposed Method
		1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid
		1.3.2 Band Reduction Using K-Means Algorithm
		1.3.3 Band Reduction Using Fuzzy C-Means
	1.4 Experimental Results
		1.4.1 DB Index Graph
		1.4.2 K-Means–Based PSC (EEOC)
		1.4.3 Fuzzy C-Means–Based PSC (EEOC)
	1.5 Analysis of Results
	1.6 Conclusions
	References
Chapter 2 A Fuzzy Approach to Face Mask Detection
	2.1 Introduction
	2.2 Existing Work
	2.3 The Proposed Framework
	2.4 Set-Up and Libraries Used
	2.5 Implementation
	2.6 Results and Analysis
	2.7 Conclusion and Future Work
	References
Chapter 3 Application of Fuzzy Logic to the Healthcare Industry
	3.1 Introduction
	3.2 Background
	3.3 Fuzzy Logic
	3.4 Fuzzy Logic in Healthcare
	3.5 Conclusions
	References
Chapter 4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database
	4.1 Introduction
	4.2 Data Extraction and Interpretation
	4.3 Results and Discussion
		4.3.1 Per Year Publication and Citation Count
		4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic
		4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas
		4.3.4 Major Contributing Countries Toward Fuzzy Research Articles
		4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis
		4.3.6 Coauthorship of Authors
		4.3.7 Cocitation Analysis of Cited Authors
		4.3.8 Cooccurrence of Author Keywords
	4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries
		4.4.1 Bibliographic Coupling of Documents
		4.4.2 Bibliographic Coupling of Sources
		4.4.3 Bibliographic Coupling of Authors
		4.4.4 Bibliographic Coupling of Countries
	4.5 Conclusion
	References
Chapter 5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling
	5.1 Introduction
	5.2 History of Fuzzy Logic and Its Applications
	5.3 Approximate Reasoning
	5.4 Fuzzy Sets vs Classical Sets
	5.5 Fuzzy Inference System
		5.5.1 Characteristics of FIS
		5.5.2 Working of FIS
		5.5.3 Methods of FIS
	5.6 Fuzzy Decision Trees
		5.6.1 Characteristics of Decision Trees
		5.6.2 Construction of Fuzzy Decision Trees
	5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment
	5.8 Conclusion
	References
Chapter 6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model
	6.1 Introduction
		6.1.1 Aim and Scope
		6.1.2 R-Tool
		6.1.3 Application of Fuzzy Logic
		6.1.4 Dataset
	6.2 Model Study
		6.2.1 Introduction to Machine Learning Method
		6.2.2 Time Series Analysis
		6.2.3 Components of a Time Series
		6.2.4 Concepts of Stationary
		6.2.5 Model Parsimony
	6.3 Methodology
		6.3.1 Exploratory Data Analysis
			6.3.1.1 Seed Types—Analysis
			6.3.1.2 Comparison of Location and Seeds
			6.3.1.3 Comparison of Season (Month) and Seeds
		6.3.2 Forecasting
			6.3.2.1 Auto Regressive Integrated Moving Average (ARIMA)
			6.3.2.2 Data Visualization
			6.3.2.3 Implementation Model
	6.4 Result Analysis
	6.5 Conclusion
	References
Chapter 7 Modified m-Polar Fuzzy Set ELECTRE-I Approach
	7.1 Introduction
		7.1.1 Objectives
	7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculations
		7.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculation Method
	7.3 Application to Industrial Problems
		7.3.1 Cutting Fluid Selection Problem
		7.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem
		7.3.3 FMS Selection Problem
		7.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection
	7.4 Conclusions
	References
Chapter 8 Fuzzy Decision Making: Concept and Models
	8.1 Introduction
	8.2 Classical Set
	8.3 Fuzzy Set
	8.4 Properties of Fuzzy Set
	8.5 Types of Decision Making
		8.5.1 Individual Decision Making
		8.5.2 Multiperson Decision Making
		8.5.3 Multistage Decision Making
		8.5.4 Multicriteria Decision Making
	8.6 Methods of Multiattribute Decision Making (MADM)
		8.6.1 Weighted Sum Method (WSM)
		8.6.2 Weighted Product Method (WPM)
		8.6.3 Weighted Aggregates Sum Product Assessment (WASPAS)
		8.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS)
	8.7 Applications of Fuzzy Logic
	8.8 Conclusion
	References
Chapter 9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India)
	9.1 Introduction
	9.2 Objectives and Methodology
		9.2.1 Objectives
		9.2.2 Methodology
	9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants
		9.3.1 Psychological Variables Identified
		9.3.2 Fuzzy Logic for Solace to Migrants
	9.4 Findings
	9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid
	9.6 Conclusion
	References
Chapter 10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow
	10.1 Significance of Machine Learning in Healthcare
	10.2 Cloud-Based Artificial Intelligent Secure Models
	10.3 Applications and Usage of Machine Learning in Healthcare
		10.3.1 Detecting Diseases and Diagnosis
		10.3.2 Drug Detection and Manufacturing
		10.3.3 Medical Imaging Analysis and Diagnosis
		10.3.4 Personalized/Adapted Medicine
		10.3.5 Behavioral Modification
		10.3.6 Maintenance of Smart Health Data
		10.3.7 Clinical Trial and Study
		10.3.8 Crowdsourced Information Discovery
		10.3.9 Enhanced Radiotherapy
		10.3.10 Outbreak/Epidemic Prediction
	10.4 Edge AI: For Smart Transformation of Healthcare
		10.4.1 Role of Edge in Reshaping Healthcare
		10.4.2 How AI Powers the Edge
	10.5 Edge AI-Modernizing Human Machine Interface
		10.5.1 Rural Medicine
		10.5.2 Autonomous Monitoring of Hospital Rooms—A Case Study
	10.6 Significance of Fuzzy in Healthcare
		10.6.1 Fuzzy Logic—Outline
		10.6.2 Fuzzy Logic-Based Smart Healthcare
		10.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems
		10.6.4 Applications of Fuzzy Logic in Healthcare
	10.7 Conclusion and Discussions
	References
Chapter 11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS
	11.1 Introduction
	11.2 Video Conferencing Software and Its Major Features
		11.2.1 Video Conferencing/Meeting Software (VC/MS) for Higher Education Institutes
	11.3 Fuzzy TOPSIS
		11.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS
	11.4 Sample Numerical Illustration
	11.5 Conclusions
	References
Chapter 12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming
	12.1 Introduction
		12.1.1 Basic Concepts of Fuzzy AHP and Goal Programming
	12.2 Research Model
		12.2.1 Average Growth Rate Calculation
	12.3 Result and Discussion
	12.4 Conclusion
	References
Chapter 13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment
	13.1 Introduction
	13.2 Proposed Algorithm
	13.3 An Illustrative Example on Ergonomic Design Evaluation
	13.4 Conclusions
	References
Chapter 14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic
	14.1 Introduction
	14.2 Control Approach in Wave Energy Systems
	14.3 Related Work
	14.4 Mathematical Modeling for Energy Conversion from Ocean Waves
	14.5 Proposed Methodology
		14.5.1 Wave Parameters
		14.5.2 Fuzzy-Optimizer
	14.6 Conclusion
	References
Chapter 15 The m-Polar Fuzzy TOPSIS Method for NTM Selection
	15.1 Introduction
	15.2 Literature Review
	15.3 Methodology
		15.3.1 Steps of the mFS TOPSIS
	15.4 Case Study
		15.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method
		15.4.2 Effect of Shannon’s Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method
	15.5 Results and Discussions
		15.5.1 Result Validation
	15.6 Conclusions and Future Scope
	References
Chapter 16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology
	16.1 Introduction
	16.2 MCDM Techniques
		16.2.1 FAHP
		16.2.2 Entropy Method as Weights (Influence) Evaluation Technique
	16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches
		16.3.1 TOPSIS
		16.3.2 FMOORA Method
		16.3.3 FVIKOR
		16.3.4 Fuzzy Grey Theory (FGT)
		16.3.5 COPRAS –G
		16.3.6 Super Hybrid Algorithm
	16.4 Illustrative Example
	16.5 Results and Discussions
		16.5.1 FTOPSIS
		16.5.2 FMOORA
		16.5.3 FVIKOR
		16.5.4 Fuzzy Grey Theory (FGT)
		16.5.5 COPRAS-G
		16.5.6 Super Hybrid Approach (SHA)
	16.6 Conclusions
	References
Chapter 17 Fuzzy MCDM on CCPM for Decision Making: A Case Study
	17.1 Introduction
	17.2 Literature Review
	17.3 Objective of Research
	17.4 Cluster Analysis
		17.4.1 Hierarchical Clustering
		17.4.2 Partitional Clustering
	17.5 Clustering
	17.6 Methodology
	17.7 TOPSIS Method
	17.8 Fuzzy TOPSIS Method
	17.9 Conclusion
	17.10 Scope of Future Study
	References
Index
EULA