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Fuzzy Computing in Data Science
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Fuzzy Computing in Data Science
by Sachi Nandan Mohanty, Prasenjit Chatterjee, Bui Thanh Hung
Fuzzy Computing in Data Science
Cover
Series Page
Title Page
Copyright Page
Dedication Page
Preface
Acknowledgement
1 Band Reduction of HSI Segmentation Using FCM
2 A Fuzzy Approach to Face Mask Detection
3 Application of Fuzzy Logic to Healthcare Industry
4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database
5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling
6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model
7 Modified m-Polar Fuzzy Set ELECTRE-I Approach
8 Fuzzy Decision Making: Concept and Models
9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India)
10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow
11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS
12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming
13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment
14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic
15 The m-Polar Fuzzy TOPSIS Method for NTM Selection
16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology
17 Fuzzy MCDM on CCPM for Decision Making: A Case Study
Index
Also of Interest
End User License Agreement
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Prev
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Cover
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Series Page
Table of Contents
Cover
Series Page
Title Page
Copyright Page
Dedication Page
Preface
Acknowledgement
1 Band Reduction of HSI Segmentation Using FCM
1.1 Introduction
1.2 Existing Method
1.3 Proposed Method
1.4 Experimental Results
1.5 Analysis of Results
1.6 Conclusions
References
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
3 Application of Fuzzy Logic to Healthcare Industry
3.1 Introduction
3.2 Background
3.3 Fuzzy Logic
3.4 Fuzzy Logic in Healthcare
3.5 Conclusions
References
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
4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries
4.5 Conclusion
References
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
5.6 Fuzzy Decision Trees
5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment
5.8 Conclusion
References
6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model
6.1 Introduction
6.2 Model Study
6.3 Methodology
6.4 Result Analysis
6.5 Conclusion
References
7 Modified m-Polar Fuzzy Set ELECTRE-I Approach
7.1 Introduction
7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculations
7.3 Application to Industrial Problems
7.4 Conclusions
References
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
8.6 Methods of Multiattribute Decision Making (MADM)
8.7 Applications of Fuzzy Logic
8.8 Conclusion
References
9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India)
9.1 Introduction
9.2 Objectives and Methodology
9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants
9.4 Findings
9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid
9.6 Conclusion
References
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
10.4 Edge AI: For Smart Transformation of Healthcare
10.5 Edge AI-Modernizing Human Machine Interface
10.6 Significance of Fuzzy in Healthcare
10.7 Conclusion and Discussions
References
11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS
11.1 Introduction
11.2 Video Conferencing Software and Its Major Features
11.3 Fuzzy TOPSIS
11.4 Sample Numerical Illustration
11.5 Conclusions
References
12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming
12.1 Introduction
12.2 Research Model
12.3 Result and Discussion
12.4 Conclusion
References
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
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
14.6 Conclusion
References
15 The m-Polar Fuzzy TOPSIS Method for NTM Selection
15.1 Introduction
15.2 Literature Review
15.3 Methodology
15.4 Case Study
15.5 Results and Discussions
15.6 Conclusions and Future Scope
References
16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology
16.1 Introduction
16.2 MCDM Techniques
16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches
16.4 Illustrative Example
16.5 Results and Discussions
16.6 Conclusions
References
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
17.5 Clustering
17.6 Methodology
17.7 TOPSIS Method
17.8 Fuzzy TOPSIS Method
17.9 Conclusion
17.10 Scope of Future Study
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Pixels clustered based on PSC (EEOC) with K-means and FCM for Sali...
Table 1.2 Pixels clustered based on PSC (EEOC) with K-means and FCM for Indi...
Table 1.3 Pixels clustered based on PSC (EEOC) with K-means and FCM for Sali...
Table 1.4 Pixels clustered based on PSC (EEOC) with K-means and FCM for Pavi...
Table 1.5 Pixels clustered based on PSC (EEOC) with K-means and FCM for Pavi...
Table 1.6 Elapsed time in seconds for PSC (EEOC) with K-means and FCM.
Table 1.7 Fitness value for PSC (EEOC) with K-means and FCM.
Chapter 4
Table 4.1 Prominent affiliations contributing toward fuzzy analysis.
Table 4.2 Top Journals publishing fuzzy-related works.
Table 4.3 Major contributing countries toward fuzzy research articles.
Table 4.4 Renowned authors contributing toward fuzzy analysis.
Chapter 5
Table 5.1 Tasks with parameters.
Table 5.2 Assigning fuzzy membership values.
Chapter 6
Table 6.1 Seed types.
Table 6.2 Location-based seed sales.
Table 6.3 Season-based seed data.
Chapter 7
Table 7.1 Methods applied for cutting fluid selection.
Table 7.2 Decision matrix for neat oil selection problem [3].
Table 7.3 Normalized decision matrix for the neat oil selection problem.
Table 7.4 Weight calculated by Shannon’s entropy method.
Table 7.5 Weight multiplied matrix for cutting fluid selection problem.
Table 7.6 Concordance set.
Table 7.7 Discordance set.
Table 7.8 Comparison between modified similarity and TOPSIS methods for neat...
Table 7.9 Outranking relationship between alternatives.
Table 7.10 Methods for FMS selection.
Table 7.11 Decision matrix for FMS selection problem [27].
Table 7.12 Normalized decision matrix for the FMS selection problem.
Table 7.13 Shannon’s entropy weight for variables
Table 7.14 Weight multiplied matrix for FMS selection problem.
Table 7.15 Concordance set.
Table 7.16 Discordance set.
Table 7.17 Comparison between EVAMIX, COPRAS, and m-polar fuzzy ELECTRE-I me...
Table 7.18 Outranking relationship between alternatives.
Chapter 8
Table 8.1 Variables for individual decision making of job selection
Table 8.2 Creating a decision matrix.
Chapter 9
Table 9.1 Variables considered for psychological and emotional effect of the...
Chapter 11
Table 11.1 Fuzzy linguistic ratings.
Table 11.2 Linguistic scale for alternative rankings.
Table 11.3 Importance weight of each criterion.
Table 11.4 Linguistic rating variable for evaluate the rating of alternative...
Table 11.5 The fuzzy decision matrix and fuzzy weights of alternatives.
Table 11.6 Fuzzy normalized decision matrix.
Table 11.7 Fuzzy weighted normalized decision matrix.
Table 11.8 Distance calculation.
Table 11.9 Closeness coefficient.
Chapter 12
Table 12.1 Gross NPA in priority and non-priority sectors.
Table 12.2 Interdependency of different economic sectors.
Table 12.3 Original Saaty’s scale for pairwise comparison.
Table 12.4 Extension of Saaty’s scale.
Table 12.5 Fuzzified matrix.
Table 12.6 Average growth rate in different sectors.
Table 12.7 Arrangement of priority and non-priority sectors as per weight.
Table 12.8 Priority arrangement.
Table 12.9 Comparison of gross NPA in SBI annual report 2020–2021 to gross N...
Chapter 13
Table 13.1 Five degrees of linguistic variables for assessing subjective att...
Table 13.2 Linguistic variables, abbreviation and TFN for weights.
Table 13.3 Decision matrix in linguistic variables assessed by user (expert)...
Table 13.4 Decision matrix in terms of linguistic variable estimated by user...
Table 13.5 Decision matrix in terms of linguistic variable estimated by user...
Table 13.6 Weights in linguistic variables estimated by users.
Table 13.7 Performance contribution of individual criterion.
Table 13.8 Performance index and ranking order of alternatives.
Chapter 14
Table 14.1 Fuzzy rules for optimization of parameters.
Chapter 15
Table 15.1 Comparison of different NTM process selection methods [11].
Table 15.2 Decision matrix (DM), Example from [5].
Table 15.3 Normalized DM with linear max N1 method [16].
Table 15.4 Pairwise comparison matrix for criteria.
Table 15.5 The rank of criteria based on weight their weights.
Table 15.6 Weight multiplied matrix.
Table 15.7 Euclidean distance of alternatives from a positive ideal solution...
Table 15.8 The rank of NTM processes using the m-polar fuzzy TOPSIS algorith...
Table 15.9 Decision matrix, example from [5].
Table 15.10 Normalized decision matrix with linear max N1 method.
Table 15.11 Weight multiplied matrix.
Table 15.12 Euclidean distance of alternatives from a positive ideal solutio...
Table 15.13 The rank of alternatives based on RCC.
Table 15.14 Result validation for m-polar fuzzy TOPSIS methodology with prev...
Table 15.15 Data input uncertainty margin for a positive ideal solution with...
Table 15.16 Data input uncertainty margin for a positive ideal solution with...
Chapter 16
Table 16.1 Fuzzy numbers, linguistic variables, and scale of TFNs for rating...
Table 16.2 Fuzzy numbers, linguistic variables, TFN, (TFN)
-1
for criteria we...
Table 16.3 Decision matrix for MHD selection problem.
Table 16.4 Decision matrix in terms of triangular fuzzy numbers.
Table 16.5 Weighted normalized performance ratings.
Table 16.6 MHD selection indices and ranking by proposed super hybrid approa...
Table 16.7 Spearman rank correlation coefficients.
Chapter 17
Table 17.1 Weighted normalized data.
Table 17.2 Grouping of policies into different clusters.
Table 17.3 Selection of alternative.
Table 17.4 (a) Ideal crop plan from the planning model.
Table 17.4 (b) Pay-off matrix.
Table 17.5 Fuzzy decision matrix.
Table 17.6 Normalized decision matrix.
Table 17.7 Weighted normalized decision matrix.
Table 17.8 Separation measures.
Table 17.9 The relative closeness to ideal solution.
Table 17.10 Interval performance measure.
Table 17.11 Crisp performance measure.
Table 17.12 Rank of different policies by fuzzy TOPSIS method.
List of Illustrations
Chapter 1
Figure 1.1 Flowchart for hyperspectral image segmentation using EEOC.
Figure 1.2 Band reduction using K-means algorithm.
Figure 1.3 Band reduction using Fuzzy C-means.
Figure 1.4 DB Index Graph. (a) Salina_A scene (b) Indian Pines (c) Salinas V...
Figure 1.5 Results for Salinas_A, (a) K-means + PSC (EEOC) (b) FCM + PSC (EE...
Figure 1.6 Results for Indian Pines (a) K-means + PSC (EEOC) (b) FCM +PSC (E...
Figure 1.7 Results for Salinas Valley. (a) K-means + PSC (EEOC) (b) FCM + PS...
Figure 1.8 Results for Pavia University. (a) K-means + PSC (EEOC) (b) FCM + ...
Figure 1.9 Results for Pavia Centre. (a) K-means + PSC (EEOC) (b) FCM + PSC ...
Chapter 2
Figure 2.1 The proposed framework.
Figure 2.2 Some of the libraries used.
Figure 2.3 The dataset and directory.
Figure 2.4 Training the model.
Figure 2.5 Training the model.
Figure 2.6 Evaluation of the networks and the precision scores.
Figure 2.7 Training loss and accuracy graph on the COVID-19 dataset.
Figure 2.8 In real time with cap, face detected without a mask with 100% acc...
Figure 2.9 In real time, with cap, face detected with the mask with 99% accu...
Figure 2.10 In real time, the persons face in the image is detected without ...
Figure 2.11 In real time the persons face in the image is detected with the ...
Figure 2.12 In real time, the persons face in the image is detected without ...
Figure 2.13 In real time ,the persons face in the image is detected with the...
Chapter 3
Figure 3.1 The basic components of FL.
Figure 3.2 The advantages of a Fuzzy Logic system.
Figure 3.3 The disadvantages of Fuzzy Logic system.
Figure 3.4 Fuzzy Logic in healthcare.
Figure 3.5 Lists of FL applications in healthcare industries.
Chapter 4
Figure 4.1 Stages of Bibliometric analysis for fuzzy logic.
Figure 4.2 Yearwise publication and citation count of fuzzy logic.
Figure 4.3 Major subject areas using fuzzy logic.
Figure 4.4 Overlay visualization of countries.
Figure 4.5 Network visualization of the co-authorship of authors contributin...
Figure 4.6 Cocitation analysis of cited authors.
Figure 4.7 Network visualization of co-occurrence of author keywords.
Figure 4.8 Overlay visualization of bibliographic coupling of documents.
Figure 4.9 Overlay visualization of bibliographic coupling of sources.
Figure 4.10 Overlay visualization of bibliographic coupling of authors.
Figure 4.11 Overlay visualization of bibliographic coupling of countries.
Chapter 5
Figure 5.1 Difference between the conventional set theory and fuzzy logic.
Figure 5.2 Comparison of the membership function of fuzzy sets with classica...
Figure 5.3 Fuzzy inference system.
Figure 5.4 Showing partially constructed fuzzy decision tree.
Chapter 6
Figure 6.1 Sample seed dataset.
Figure 6.2 A four-phase business cycle.
Figure 6.3 Seed sales data.
Figure 6.4 Location-based seed data.
Figure 6.5 Spinach sales.
Figure 6.6 Gingelly sales.
Figure 6.7 Black gram sales.
Figure 6.8 Fodder sorghum sales.
Figure 6.9 Prediction for black gram.
Figure 6.10 Prediction for spinach dataset.
Figure 6.11 Prediction for gingelly dataset.
Figure 6.12 Prediction for fodder dataset.
Chapter 7
Figure 7.1 Directed graph for cutting fluid selection.
Figure 7.2 Directed graph for FMS selection.
Chapter 8
Figure 8.1 Comparison of Boolean logic with fuzzy logic.
Chapter 10
Figure 10.1 Applications and usage of machine learning in healthcare.
Figure 10.2 Role of edge in healthcare.
Figure 10.3 The approach of multiattribute decision model.
Chapter 12
Figure 12.1 Intersection between two triangular fuzzy numbers (M
1
& M
2
).
Figure 12.2 Feasibility result of goal programming model.
Chapter 13
Figure 13.1 Performance index of alternatives.
Figure 13.2 Ranking order of the alternatives.
Chapter 14
Figure 14.1 Types of buoys for ocean wave.
Figure 14.2 Waves motion and depth.
Figure 14.3 Block diagram of a sea wave energy harvesting device.
Figure 14.4 Conceptual model wave energy generation.
Figure 14.5 Proposed wave optimization model.
Chapter 15
Figure 15.1 Graphical presentation of the rank performance of alternatives f...
Figure 15.2 Graphical presentation of the rank performance of alternatives f...
Chapter 16
Figure 16.1 Decision making framework for MHD selection.
Figure 16.2 Comparison of ranking order.
Figure 16.3 Ranks of MHDs by super hybrid approach.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Dedication Page
Preface
Acknowledgement
Table of Contents
Begin Reading
Index
Also of Interest
Wiley End User License Agreement
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