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 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.

Inhaltsverzeichnis:

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 Tow ard 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 Reasoni ng 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