Fuzzy computing in data science : applications and challenges / edited by Sachi Nandan Mohanty, Prasenjit Chatterjee and Bui Thanh Hung.

Format:

Book

Contributor:

Hung, Bui Thanh, editor.

Chatterjee, Prasenjit, editor.

Mohanty, Sachi Nandan, editor.

Series:

Smart and Sustainable Intelligent Systems

Smart and sustainable intelligent systems

Language:

English

Subjects (All):

Fuzzy logic.

Fuzzy systems.

Data mining.

Physical Description:

1 online resource (363 pages)

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc., [2023]

Summary:

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

Notes:

Description based on print version record.

Includes bibliographical references and index.

Other Format:

Print version: Mohanty, Sachi Nandan Fuzzy Computing in Data Science

ISBN:

9781394156887

139415688X

9781394156870

1394156871

OCLC:

1349088895