Fuzzy Computing in Data Science: Applications and Challenges

✍ Scribed by Sachi Nandan Mohanty, Prasenjit Chatterjee, Bui Thanh Hung

• Publisher: Wiley-Scrivener
• Year: 2022
• Tongue: English
• Leaves: 363
• Series: Sustainable Computing and Optimization
• Category: Library

✦ Synopsis

FUZZY COMPUTING IN DATA SCIENCE

This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges.

The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation.

Audience

Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.

✦ Table of Contents

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