Uncertainty in Computational Intelligence-Based Decision Making

Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others. The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science. Provides readers a thorough understanding of the uncertainty that arises in artificial intelligence (AI), computational intelligence (CI) paradigms, and algorithmsEncourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision designProvides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision INDICE: Section 1. Uncertainty 1. Data Analytics Streaming for Data Lakes 2. Supply Chain Management Problem Modelling in Hesitant Fuzzy Environment 3. Impact of Number and Type of Criteria on Ranking Abnormality in MCDM Techniques 4. Fuzzy Approach for the LEACH Protocol for Real Time Applications 5. Application of Intuitionistic fuzzy TOPSIS for Cloud Service Selection Problem 6. Role of Uncertainty in Artificial Intelligence and Machine Learning 7. On the Study of the System of Uncertain Linear Differential Equations Under Neutrosophic Sense of Uncertainty 8. A Fuzzy Logic Design for Self-Driving Vehicle to Avoid Obstacles Section 2. Computational Techniques 10. Soft Computing: A Systematic Review 11. K-Means Clustering Over Distributed Environment: A Review 12. Using TOPSIS to Select the Best Prediction Model Constructed Using Partial Least Squares-Discriminant Analysis (PLS-DA) Algorithms and Infrared Spectra: A Forensic Case Study 13. A Study on Blockchain-Based Security in UAV Fog Computing Networks 14. Advanced Frequent Itemsets Mining Algorithm (AFIM) 15. Bayesian Regularization Approach to Train Ann: An Application in Prediction of Air Temperature 16. SMS Spam Classification Using Machine Learning Techniques 17. Computational Intelligence in Decision Support: Scope and Techniques Section 3. Decision Intelligence 18. A Review of Computational Decision Intelligence Tools in Climatology 19. Computational Decision Intelligence Approaches for Drought Prediction: A Review 20. TEAM: Trust Evaluation and Analysis of Misbehaviors in WSNs 21. Automatic Parallelization for Multicore Architectures: Role, Importance and Opportunities 22. Gradient Boosting Decision Tree Model for Estimating and Localize Covid-19 Abnormalities on Chest Radiographs 23. A Personalised Hybrid Diet Recommender Systems 24. Artificial Intelligence Enabled Knowledge Health Engine for Retrieval Based Chatbots in Healthcare 25. Secure and Cost-Effective Key Management scheme for the Internet of Things Supported WSN

• ISBN: 978-0-443-21475-2
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 350
• Fecha Publicación: 27/09/2024
• Nº Volúmenes: 1
• Idioma: Inglés