Produktbild: Fuzzy Computing in Data Science

Fuzzy Computing in Data Science Applications and Challenges

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.12.2022

Herausgeber

Sachi Nandan Mohanty + weitere

Verlag

John Wiley & Sons

Seitenzahl

368

Maße (L/B/H)

23.3/15.5/2.4 cm

Gewicht

594 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-86492-9

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.12.2022

Herausgeber

Verlag

John Wiley & Sons

Seitenzahl

368

Maße (L/B/H)

23.3/15.5/2.4 cm

Gewicht

594 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-86492-9

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  • Produktbild: Fuzzy Computing in Data Science
  • Preface xvii

    Acknowledgement xxi

    1 Band Reduction of HSI Segmentation Using FCM 1
    V. Saravana Kumar, S. Anantha Sivaprakasam, E.R. Naganathan, Sunil Bhutada and M. Kavitha

    1.1 Introduction 2

    1.2 Existing Method 3

    1.2.1 K-Means Clustering Method 3

    1.2.2 Fuzzy C-Means 3

    1.2.3 Davies Bouldin Index 4

    1.2.4 Data Set Description of HSI 4

    1.3 Proposed Method 5

    1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid 5

    1.3.2 Band Reduction Using K-Means Algorithm 6

    1.3.3 Band Reduction Using Fuzzy C-Means 7

    1.4 Experimental Results 8

    1.4.1 DB Index Graph 8

    1.4.2 K-Means-Based PSC (EEOC) 9

    1.4.3 Fuzzy C-Means-Based PSC (EEOC) 10

    1.5 Analysis of Results 12

    1.6 Conclusions 16

    References 17

    2 A Fuzzy Approach to Face Mask Detection 21
    Vatsal Mishra, Tavish Awasthi, Subham Kashyap, Minerva Brahma, Monideepa Roy and Sujoy Datta

    2.1 Introduction 22

    2.2 Existing Work 23

    2.3 The Proposed Framework 26

    2.4 Set-Up and Libraries Used 26

    2.5 Implementation 27

    2.6 Results and Analysis 29

    2.7 Conclusion and Future Work 33

    References 34

    3 Application of Fuzzy Logic to the Healthcare Industry 37
    Biswajeet Sahu, Lokanath Sarangi, Abhinadita Ghosh and Hemanta Kumar Palo

    3.1 Introduction 38

    3.2 Background 41

    3.3 Fuzzy Logic 42

    3.4 Fuzzy Logic in Healthcare 45

    3.5 Conclusions 49

    References 50

    4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database 55
    Sugyanta Priyadarshini and Nisrutha Dulla

    4.1 Introduction 56

    4.2 Data Extraction and Interpretation 58

    4.3 Results and Discussion 59

    4.3.1 Per Year Publication and Citation Count 59

    4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic 60

    4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas 61

    4.3.4 Major Contributing Countries Toward Fuzzy Research Articles 63

    4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis 66

    4.3.6 Coauthorship of Authors 67

    4.3.7 Cocitation Analysis of Cited Authors 68

    4.3.8 Cooccurrence of Author Keywords 68

    4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries 70

    4.4.1 Bibliographic Coupling of Documents 70

    4.4.2 Bibliographic Coupling of Sources 71

    4.4.3 Bibliographic Coupling of Authors 72

    4.4.4 Bibliographic Coupling of Countries 73

    4.5 Conclusion 74

    References 76

    5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling 79
    Rekha A. Kulkarni, Suhas H. Patil and Bithika Bishesh

    5.1 Introduction 80

    5.2 History of Fuzzy Logic and Its Applications 81

    5.3 Approximate Reasoning 82

    5.4 Fuzzy Sets vs Classical Sets 83

    5.5 Fuzzy Inference System 84

    5.5.1 Characteristics of FIS 85

    5.5.2 Working of FIS 85

    5.5.3 Methods of FIS 86

    5.6 Fuzzy Decision Trees 86

    5.6.1 Characteristics of Decision Trees 87

    5.6.2 Construction of Fuzzy Decision Trees 87

    5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment 88

    5.8 Conclusion 90

    References 91

    6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model 93
    S. Mala and V. Umadevi

    6.1 Introduction 94

    6.1.1 Aim and Scope 94

    6.1.2 R-Tool 94

    6.1.3 Application of Fuzzy Logic 94

    6.1.4 Dataset 95

    6.2 Model Study 96

    6.2.1 Introduction to Machine Learning Method 96

    6.2.2 Time Series Analysis 96

    6.2.3 Components of a Time Series 97

    6.2.4 Concepts of Stationary 99

    6.2.5 Model Parsimony 100

    6.3 Methodology 100

    6.3.1 Exploratory Data Analysis 100

    6.3.1.1 Seed Types-Analysis 101

    6.3.1.2 Comparison of Location and Seeds 101

    6.3.1.3 Comparison of Season (Month) and Seeds 103

    6.3.2 Forecasting 103

    6.3.2.1 Auto Regressive Integrated Moving Average (ARIMA) 103

    6.3.2.2 Data Visualization 106

    6.3.2.3 Implementation Model 108

    6.4 Result Analysis 108

    6.5 Conclusion 110

    References 110

    7 Modified m-Polar Fuzzy Set ELECTRE-I Approach 113
    Madan Jagtap, Prasad Karande and Pravin Patil

    7.1 Introduction 114

    7.1.1 Objectives 114

    7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon's Entropy Weight Calculations 115

    7.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon's Entropy Weight Calculation Method 115

    7.3 Application to Industrial Problems 118

    7.3.1 Cutting Fluid Selection Problem 118

    7.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem 122

    7.3.3 FMS Selection Problem 125

    7.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection 130

    7.4 Conclusions 143

    References 143

    8 Fuzzy Decision Making: Concept and Models 147
    Bithika Bishesh

    8.1 Introduction 148

    8.2 Classical Set 149

    8.3 Fuzzy Set 150

    8.4 Properties of Fuzzy Set 151

    8.5 Types of Decision Making 153

    8.5.1 Individual Decision Making 153

    8.5.2 Multiperson Decision Making 157

    8.5.3 Multistage Decision Making 158

    8.5.4 Multicriteria Decision Making 160

    8.6 Methods of Multiattribute Decision Making (MADM) 162

    8.6.1 Weighted Sum Method (WSM) 162

    8.6.2 Weighted Product Method (WPM) 162

    8.6.3 Weighted Aggregates Sum Product Assessment (WASPAS) 163

    8.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) 166

    8.7 Applications of Fuzzy Logic 167

    8.8 Conclusion 169

    References 169

    9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India) 173
    Sanjaya Kumar Sahoo and Sukanta Chandra Swain

    9.1 Introduction 174

    9.2 Objectives and Methodology 175

    9.2.1 Objectives 175

    9.2.2 Methodology 176

    9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants 176

    9.3.1 Psychological Variables Identified 176

    9.3.2 Fuzzy Logic for Solace to Migrants 176

    9.4 Findings 178

    9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid 178

    9.6 Conclusion 179

    References 180

    10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow 181
    B. RaviKrishna, Sirisha Potluri, J. Rethna Virgil Jeny, Guna Sekhar Sajja and Katta Subba Rao

    10.1 Significance of Machine Learning in Healthcare 182

    10.2 Cloud-Based Artificial Intelligent Secure Models 183

    10.3 Applications and Usage of Machine Learning in Healthcare 183

    10.3.1 Detecting Diseases and Diagnosis 183

    10.3.2 Drug Detection and Manufacturing 183

    10.3.3 Medical Imaging Analysis and Diagnosis 184

    10.3.4 Personalized/Adapted Medicine 185

    10.3.5 Behavioral Modification 185

    10.3.6 Maintenance of Smart Health Data 185

    10.3.7 Clinical Trial and Study 185

    10.3.8 Crowdsourced Information Discovery 185

    10.3.9 Enhanced Radiotherapy 186

    10.3.10 Outbreak/Epidemic Prediction 186

    10.4 Edge AI: For Smart Transformation of Healthcare 186

    10.4.1 Role of Edge in Reshaping Healthcare 186

    10.4.2 How AI Powers the Edge 187

    10.5 Edge AI-Modernizing Human Machine Interface 188

    10.5.1 Rural Medicine 188

    10.5.2 Autonomous Monitoring of Hospital Rooms-A Case Study 188

    10.6 Significance of Fuzzy in Healthcare 189

    10.6.1 Fuzzy Logic-Outline 189

    10.6.2 Fuzzy Logic-Based Smart Healthcare 190

    10.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems 191

    10.6.4 Applications of Fuzzy Logic in Healthcare 193

    10.7 Conclusion and Discussions 193

    References 194

    11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS 197
    Rekha Gupta

    11.1 Introduction 197

    11.2 Video Conferencing Software and Its Major Features 199

    11.2.1 Video Conferencing/Meeting Software (VC/MS) for Higher Education Institutes 199

    11.3 Fuzzy TOPSIS 203

    11.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS 203

    11.4 Sample Numerical Illustration 207

    11.5 Conclusions 213

    References 213

    12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming 215
    Kandarp Vidyasagar and Rajiv Kr. Dwivedi

    12.1 Introduction 216

    12.1.1 Basic Concepts of Fuzzy AHP and Goal Programming 217

    12.2 Research Model 221

    12.2.1 Average Growth Rate Calculation 227

    12.3 Result and Discussion 233

    12.4 Conclusion 234

    References 234

    13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment 237
    Bipradas Bairagi

    13.1 Introduction 238

    13.2 Proposed Algorithm 240

    13.3 An Illustrative Example on Ergonomic Design Evaluation 245

    13.4 Conclusions 249

    References 249

    14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic 253
    S. B. Goyal, Pradeep Bedi, Jugnesh Kumar and Prasenjit Chatterjee

    14.1 Introduction 254

    14.2 Control Approach in Wave Energy Systems 255

    14.3 Related Work 257

    14.4 Mathematical Modeling for Energy Conversion from Ocean Waves 259

    14.5 Proposed Methodology 260

    14.5.1 Wave Parameters 261

    14.5.2 Fuzzy-Optimizer 262

    14.6 Conclusion 264

    References 264

    15 The m-Polar Fuzzy TOPSIS Method for NTM Selection 267
    Madan Jagtap and Prasad Karande

    15.1 Introduction 268

    15.2 Literature Review 268

    15.3 Methodology 270

    15.3.1 Steps of the mFS TOPSIS 270

    15.4 Case Study 272

    15.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method 273

    15.4.2 Effect of Shannon's Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method 277

    15.5 Results and Discussions 281

    15.5.1 Result Validation 281

    15.6 Conclusions and Future Scope 283

    References 284

    16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology 287
    Bipradas Bairagi

    16.1 Introduction 288

    16.2 MCDM Techniques 289

    16.2.1 Fahp 289

    16.2.2 Entropy Method as Weights (Influence) Evaluation Technique 290

    16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches 291

    16.3.1 Topsis 291

    16.3.2 FMOORA Method 292

    16.3.3 FVIKOR 292

    16.3.4 Fuzzy Grey Theory (FGT) 293

    16.3.5 COPRAS -G 293

    16.3.6 Super Hybrid Algorithm 294

    16.4 Illustrative Example 295

    16.5 Results and Discussions 298

    16.5.1 FTOPSIS 298

    16.5.2 FMOORA 298

    16.5.3 FVIKRA 298

    16.5.4 Fuzzy Grey Theory (FGT) 299

    16.5.5 COPRAS-G 299

    16.5.6 Super Hybrid Approach (SHA) 299

    16.6 Conclusions 302

    References 302

    17 Fuzzy MCDM on CCPM for Decision Making: A Case Study 305
    Bimal K. Jena, Biswajit Das, Amarendra Baral and Sushanta Tripathy

    17.1 Introduction 306

    17.2 Literature Review 307

    17.3 Objective of Research 308

    17.4 Cluster Analysis 308

    17.4.1 Hierarchical Clustering 309

    17.4.2 Partitional Clustering 309

    17.5 Clustering 310

    17.6 Methodology 314

    17.7 TOPSIS Method 316

    17.8 Fuzzy TOPSIS Method 318

    17.9 Conclusion 325

    17.10 Scope of Future Study 326

    References 326

    Index 329