Advances in Soft Computing Applications

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. 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