Fuzzy computing in data science : applications and challenges

Fuzzy computing in data science applications and challenges

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 prese...

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Detalles Bibliográficos
Otros Autores: Hung, Bui Thanh, editor (editor), Chatterjee, Prasenjit, editor, Mohanty, Sachi Nandan, editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons, Inc [2023]
Colección:Smart and Sustainable Intelligent Systems
Materias:
Fuzzy logic.
Fuzzy systems.
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009703307306719
Tabla de Contenidos:
  • 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.

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