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 algorithms
- Encourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision design
- Provides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision
Reihe
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Dateigröße
ISBN-13
978-0-443-21476-9 (9780443214769)
Schweitzer Klassifikation
1. TOPSIS for the selection of the prediction model in forensic ink analysis2. EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE IN DETERMINING FOOT PATHOLOGIES AND DESIGN INSOLES USING PLAIN RADIOGRAPH AND DIGITAL PHOTOGRAPH3. OUTBOUND LOGISTICS BUSINESS PROCESS MODELING: ANALYTIC PERSPECTIVE WITH BPMN 2.04. REVOLUTIONIZING DIABETIC FOOT ULCER TREATMENT: HARNESSING THE POWER OF ARTIFICIAL INTELLIGENCE AND TRANSFER LEARNING5. A Systematic Review on Personalized Hybrid Diet Recommendations6. Impact of Number and Type of Criteria on Ranking Abnormality in MCDM Techniques7. Comparison between some methods in fuzzy linear regression8. Artificial Intelligence and decision making in climate change studies: A review9. Computational Decision Intelligence approaches for drought prediction: A review10. A review of the Applications of Computational Decision Intelligence approaches in agrometeorology11. A Fuzzy Logic Design for Self-driving Vehicle to Avoid Obstacles12. K-Means Clustering Over Distributed Environment: A Review13. Advanced Frequent Itemsets Mining Algorithm (AFIM)14. TEAM: Trust Evaluation and Analysis of Misbehaviors in WSNs15. Computational Intelligence in Decision Support: Scope and Techniques16. Automatic Parallelization for Multicore Architectures: Role, Importance and Opportunities17. Using Tensor Processing Units to identify the relationship between hypothesis and premise: A case of natural language inference problem18. Secure and Cost-Effective Key Management Scheme for the Internet of Things supported WSN19. A deep learning-based integrated voice assistance system for partially disabled people
