-
Browse Book Series
- Adhesion and Adhesives: Fundamental and Applied Aspects
- Advances in Additive Manufacturing Technologies
- Advances in Antenna, Microwave and Communication Engineering
- Advances in Biofeedstocks and Biofuels
- Advances in Cyber Security
- Advances in Data Engineering and Machine Learning
- Advances in E-Mobility
- Advances in Hydrogen Production and Storage
- Advances in Intelligent and Scientific Computing (AISC)
- Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Advances in Membrane Processes
- Advances in Nanotechnology and Applications
- Advances in Natural Gas Engineering
- Advances in Pipes and Pipelines
- Advances in Production Engineering
- Advances in Solar Cell Materials and Storage
- Applied Functional Materials
- Artificial Intelligence and Soft Computing for Industrial Transformation
- Astrobiology Perspectives on Life in the Universe
- Bioprocessing in Food Science
- Computational Intelligence and Biomedical Engineering
- Concise Introductions to AI and Data Science
- Cyber Systems and Quantum Engineering
- Decentralized Systems and Next-Generation Internet
- Diatoms: Biology and Applications
- Digital Convergence in Engineering Systems
- Emerging Trends in Medicinal and Pharmaceutical Chemistry
- Engineering Systems Design for Sustainable Development
- Fintech in a Sustainable Digital Society
- Industry 5.0 Transformation Applications
- Infectious and Rare Diseases
- Innovations in Materials and Manufacturing
- Machine Learning in Biomedical Science and Healthcare Informatics
- Materials Degradation and Failure
- Mathematics and Computer Science
- Next-Generation Computing and Communication Engineering
- Performability Engineering Series
- Rotating Machinery Fundamentals and Advances
- Studies in Computational Intelligence and Smart Technologies
- Sustainable Computing and Optimization
Browse Subject Areas
For Authors
Submit a ProposalFuzzy Computing in Data Science
 | Applications and Challenges By Sachi Nandan Mohanty, Prasenjit Chatterjee and Bui Thanh Hung Series: Sustainable Computing and Optimization Copyright: 2022 | Status: Published ISBN: 9781119864929 | Hardcover | 356 pages Price: $195 USD  |
One Line Description
This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges.
Audience
Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.
Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.
Description
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.
Back to Top
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.
Back to Top
Author / Editor Details
Sachi Nandan Mohanty, PhD, received his doctorate from IIT Kharagpur in 2015 and his PostDoc from IIT Kanpur in 2019. He has recently joined as an associate professor at VIT-AP University, Andhra Pradesh. He has edited 24 books and published more than 100 research papers in international journals and has been elected as Fellow of the Institute of Engineers and Senior member of IEEE Computer Society Hyderabad chapter. His research areas include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, and computational intelligence.
Sachi Nandan Mohanty, PhD, received his doctorate from IIT Kharagpur in 2015 and his PostDoc from IIT Kanpur in 2019. He has recently joined as an associate professor at VIT-AP University, Andhra Pradesh. He has edited 24 books and published more than 100 research papers in international journals and has been elected as Fellow of the Institute of Engineers and Senior member of IEEE Computer Society Hyderabad chapter. His research areas include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, and computational intelligence.
Prasenjit Chatterjee, PhD, is an associate professor in the Mechanical Engineering Department at MCKV Institute of Engineering, India. He has more than 80 research papers in various international SCI journals. Dr. Chatterjee is one of the developers of a new multiple-criteria decision-making method called Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS).
Bui Thanh Hung, PhD, is the Director of Artificial Intelligence Laboratory, Faculty of Information Technology, Ton Duc Thang University, Vietnam, and received his doctorate from Japan Advanced Institute of Science and Technology (JAIST) in 2013. He has published numerous research articles in international journals and conferences as well as 14 book chapters. His main research interests are natural language processing, machine learning, machine translation, text processing, data analytics, computer vision, and artificial intelligence.
Back to Top
Table of Contents
Preface
Acknowledgement
1. Band Reduction of HSI Segmentation Using FCM
V. Saravana Kumar, S. Anantha Sivaprakasam, E.R. Naganathan, Sunil Bhutada and M. Kavitha
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
2. A Fuzzy Approach to Face Mask Detection
Vatsal Mishra, Tavish Awasthi, Subham Kashyap, Minerva Brahma, Monideepa Roy and Sujoy Datta
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 the Healthcare Industry
Biswajeet Sahu, Lokanath Sarangi, Abhinadita Ghosh and Hemanta Kumar Palo
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
Sugyanta Priyadarshini and Nisrutha Dulla
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
5. Fuzzy Decision Making in Predictive Analytics and Resource Scheduling
Rekha A. Kulkarni, Suhas H. Patil and Bithika Bishesh
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
6. Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model
S. Mala and V. Umadevi
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
7. Modified m-Polar Fuzzy Set ELECTRE-I Approach
Madan Jagtap, Prasad Karande and Pravin Patil
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
8. Fuzzy Decision Making: Concept and Models
Bithika Bishesh
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
9. Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India)
Sanjaya Kumar Sahoo and Sukanta Chandra Swain
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
10. Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow
B. RaviKrishna, Sirisha Potluri, J. Rethna Virgil Jeny, Guna Sekhar Sajja and Katta Subba Rao
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
11. Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS
Rekha Gupta
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
12. Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming
Kandarp Vidyasagar and Rajiv Kr. Dwivedi
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
13. Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment
Bipradas Bairagi
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
S. B. Goyal, Pradeep Bedi, Jugnesh Kumar and Prasenjit Chatterjee
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
15. The m-Polar Fuzzy TOPSIS Method for NTM Selection
Madan Jagtap and Prasad Karande
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
16. Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology
Bipradas Bairagi
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
17. Fuzzy MCDM on CCPM for Decision Making: A Case Study
Bimal K. Jena, Biswajit Das, Amarendra Baral and Sushanta Tripathy
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
17.7 TOPSIS Method
17.8 Fuzzy TOPSIS Method
17.9 Conclusion
17.10 Scope of Future Study
References
Index
Back to Top
Preface
Acknowledgement
1. Band Reduction of HSI Segmentation Using FCM
V. Saravana Kumar, S. Anantha Sivaprakasam, E.R. Naganathan, Sunil Bhutada and M. Kavitha
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
2. A Fuzzy Approach to Face Mask Detection
Vatsal Mishra, Tavish Awasthi, Subham Kashyap, Minerva Brahma, Monideepa Roy and Sujoy Datta
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 the Healthcare Industry
Biswajeet Sahu, Lokanath Sarangi, Abhinadita Ghosh and Hemanta Kumar Palo
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
Sugyanta Priyadarshini and Nisrutha Dulla
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
5. Fuzzy Decision Making in Predictive Analytics and Resource Scheduling
Rekha A. Kulkarni, Suhas H. Patil and Bithika Bishesh
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
6. Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model
S. Mala and V. Umadevi
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
7. Modified m-Polar Fuzzy Set ELECTRE-I Approach
Madan Jagtap, Prasad Karande and Pravin Patil
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
8. Fuzzy Decision Making: Concept and Models
Bithika Bishesh
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
9. Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India)
Sanjaya Kumar Sahoo and Sukanta Chandra Swain
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
10. Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow
B. RaviKrishna, Sirisha Potluri, J. Rethna Virgil Jeny, Guna Sekhar Sajja and Katta Subba Rao
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
11. Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS
Rekha Gupta
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
12. Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming
Kandarp Vidyasagar and Rajiv Kr. Dwivedi
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
13. Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment
Bipradas Bairagi
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
S. B. Goyal, Pradeep Bedi, Jugnesh Kumar and Prasenjit Chatterjee
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
15. The m-Polar Fuzzy TOPSIS Method for NTM Selection
Madan Jagtap and Prasad Karande
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
16. Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology
Bipradas Bairagi
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
17. Fuzzy MCDM on CCPM for Decision Making: A Case Study
Bimal K. Jena, Biswajit Das, Amarendra Baral and Sushanta Tripathy
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
17.7 TOPSIS Method
17.8 Fuzzy TOPSIS Method
17.9 Conclusion
17.10 Scope of Future Study
References
Index
Back to Top