The proclivity of today’s technology to think like humans may be seen in new developing disciplines such as neural computing, fuzzy logic, evolutionary computation, Machine Learning, and probabilistic reasoning. These strategies are grouped together into one main technique known as "soft computing." This book discusses the most recent soft computing and fuzzy logic-based applications and innovations in industrial advancements, supply chain and logistics, system optimization, decision-making, Artificial Intelligence, smart systems, and other rapidly evolving technologies. In today’s competitive world, the book provides soft computing solutions to help companies overcome the obstacles posed by sophisticated decision-making systems.
“Uncertainty” is unquestionably a major feature of human thinking. This idea of how to convey “uncertainty” in programming resulted in the development of a theory known as fuzzy theory. The concept of fuzziness focuses around the representation of ideas that are somewhat ill-defined, unclear, or, as the term implies, uncertain. As a way, fuzzy theory may be said to be represented in such a manner that it describes both randomness and uncertainty at the same time. Fuzzy sets and soft computing offer multiple theoretical and practical tools for challenging linguistic and numerical modeling applications. When human judgments and modeling of human knowledge are required, fuzzy set techniques are typically the best choice.
Author(s): Shristi Kharola, Mangey Ram, Sachin K. Mangla
Publisher: River Publishers
Year: 2023
Language: English
Pages: 305
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgement
List of Figures
List of Tables
List of Contributors
List of Abbreviations
Chapter 1: Applying Fuzzy Logic to the Assessment of Latent Economic Features
1.1: Introduction
1.2: The Concept and Essence of Latent Economic Characteristics
1.3: Review of Existing Methods for Assessing Latent Economic Characteristics
1.4: Estimation of Latent Economic Features based on a Combination of Expert
1.5: Conclusion
Chapter 2: A Fuzzy-based Group Decision-making Approach for Supplier Selection
2.1: Introduction
2.2: Fuzzy MCDM Techniques for Supplier Selection
2.2.1: Fuzzy MOORA method
2.2.2: Fuzzy gray theory (FGT)
2.3: Illustrative Example
2.3.1: Illustration of the cited numerical with the FMOORA method
2.3.2: Illustration of the numerical with the FGT method
2.4: Overall Discussion and Relative Result Analysis
2.5: Conclusion
2.6: Acknowledgement
Chapter 3: The Use of Computational Intelligence in Process Management
3.1: Introduction
3.2: Proposed Solution
3.2.1: Methods of choosing solutions for determination output
3.2.2: Fuzzy cells
3.2.3: Mathematical model for fuzzy components
3.2.4: Elements of programming language FACCT
3.2.5: Results
3.2.6: Discussion
3.3: Conclusion
Chapter 4: Evaluating the Effectiveness of Enterprises’ Digital Transformation by Fuzzy Logic
4.1: Introduction
4.2: Results
4.3: Conclusion
Chapter 5: A New Decision-making Framework for Performance Evaluation of Industrial Robots
5.1: Introduction
5.2: Proposed Algorithm
5.3: Illustrative Example
5.3.1: Problem definition
5.3.2: Calculation
5.4: Discussions
5.5: Conclusion
Chapter 6: Determination of Launch Time for a Multi-generational Product: A Fuzzy Perspective
6.1: Introduction
6.2: Building Block
6.2.1: Innovation diffusion model
6.2.2: The cost model
6.3: The Fuzzy Optimization Problem
6.4: Numerical Illustration
6.5: Managerial Implications
6.6: Conclusion
Chapter 7: Securing the Key of Improved Playfair Cipher using the Diffie–Hellman Algorithm
7.1: Introduction
7.1.1: Introduction about cryptography and substitution techniques
7.1.2: Introduction of the traditional playfair cipher
7.2: Works and Modifications Done on the Playfair Cipher Till Now
7.3: What Makes This Paper Unique?
7.4: Building Blocks of the Proposed Work
7.4.1: The extended playfair cipher
7.4.1.1: Algorithm to encrypt the plaintext
7.4.1.2: Algorithm to decrypt the ciphertext
7.4.2: The diffie–hellman algorithm
7.5: Proposed Work
7.6: Methodology
7.7: Illustration
7.8: Advantages of Securing Key in the Extended Playfair Cipher using the Diffie–Hellman Algorithm
7.9: Conclusion
Chapter 8: Application of Multi-criteria Decision-making in Sustainable Resource Planning
8.1: Introduction
8.2: Literature Review
8.3: Methodology
8.4: Numerical Analysis
8.4.1: Economic factors
8.4.2: Technological factors
8.4.3: Environmental factors
8.5: Results and Discussion
8.6: Conclusion
Chapter 9: Fuzzy Logic based Decision Systems in the Healthcare Sector
9.1: Introduction
9.2: Fuzzy Sets
9.2.1: Membership function
9.2.2: Components of a fuzzy logic system
9.2.3: Operations on fuzzy sets
9.2.4: Fuzzy set properties
9.3: Fuzziness in Medical Field
9.3.1: Fuzzy in clinical decision support system (CDSS)
9.3.2: Fuzzy inference system (FIS) in disease diagnosis
9.3.3: Fuzzy classification
9.4: Disease Diagnosis in Healthcare Sector
9.4.1: Kidney disease
9.4.2: Breast cancer
9.4.3: Diabetes
9.4.4: Parkinson’s disease
9.4.5: Lung cancer
9.4.6: Thyroid
9.4.7: Skin disease
9.4.8: Alzheimer’s disease
9.4.9: Prostate cancer
9.4.10: Pneumonia disease
9.4.11: Anesthesia monitoring
9.4.12: Heart disease
9.5: Conclusion
Chapter 10: Novel Pythagorean Fuzzy Entropy-distance Measures using MCDM in the Selection of Face
10.1: Introduction
10.2: Preliminaries
10.3: Proposed Distance Measure
10.4: Numerical Illustration
10.5: Application Through TOPSIS
10.5.1: Stepwise explanation of the TOPSIS algorithm
10.6: Conclusion
Chapter 11: Prioritizing the Barriers of Manufacturing during COVID-19: using Fuzzy AHP
11.1: Introduction
11.2: Literature Review
11.2.1: Identification of Barriers in the Manufacturing Industry
11.3: Research Methodology
11.3.1: Analytical Hierarchy Process
11.3.2: Fuzzy Set Theory
11.3.3: Computational Procedure of Fuzzy AHP
11.4: Result and Discussion
11.5: Conclusion and Future Research Avenues
Chapter 12: Genetic Algorithms for Selection of Critical Cytological Features in Cancer Datasets
12.1: Introduction
12.2: Literature Survey
12.3: Research Methodology
12.3.1: Exploratory Analysis and Data Visualization
12.3.1.1: Data Analysis
12.3.1.2: Visual Analysis
12.3.2: Feature Selection using Genetic Algorithms
12.3.2.1: Comparison of Various Classification Models
12.3.2.2: Realization of Feature Importance Scores
12.3.2.3: Comparison of random forest classifier with logistic regression as evaluators for feature selection using genetic algorithms
12.3.3: Neural Network Construction and Performance Analysis
12.3.3.1: Model Design Description
12.3.3.2: Results and Observations
12.3.3.3: Exploration of Optimization of GA using Random Forest as an Estimator
12.4: Conclusion
Chapter 13: Role of AI in Various Industrial Managerial Disciplines
13.1: Introduction
13.2: Literature Review
13.2.1: What is Artificial Intelligence?
13.2.1.1: Artificial Intelligence in Manufacturing
13.2.1.2: Artificial Intelligence in Marketing
13.2.1.3: Significance of Artificial Intelligence in Supply Chain Management
13.2.1.4: Other Disciplines Where Artificial Intelligence Is or Can Play a Crucial Role
13.3: S.W.O.T. Analysis:
13.4: Managerial Implications
13.5: Discussion
13.6: Conclusion
Chapter 14: Evaluating Fuzzy System Reliability using a Time-Dependent Hexagonal Fuzzy Number
14.1: Introduction
14.2: Preliminaries
14.2.1: Definition
14.2.2: α-Cut set of time-dependent fuzzy set
14.2.3: Hexagonal fuzzy numbers
14.2.4: α-Cut hexagonal fuzzy number
14.2.5: Time-dependent hexagonal fuzzy number
14.3: Problem Formulation
14.4: Reliability evaluation with time-dependent hexagonal fuzzy number
14.5: Reliability of Different Systems under Fuzzy Conditions
14.5.1: Series system
14.5.2: Parallel system
14.6: Conclusion
Index
About the Editors
