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.
1. Uncertainty in Computational Intelligence-Based Decision Making - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Table of Contents - p. v 5. Copyright Page - p. iv 6. List of Contributors - p. xiii 7. 1 TOPSIS for the selection of the prediction model in forensic ink analysis - p. 1 8. 2 Effectiveness of artificial intelligence in determining foot pathologies and designing insoles using plain radiograph and digital photograph - p. 13 9. 3 Outbound logistics business process modeling: Analytic perspective with BPMN 2.0 - p. 23 10. 4 Revolutionizing diabetic foot ulcer treatment prediction: Harnessing the power of artificial intelligence and transfer learning - p. 55 11. 5 A systematic review on personalized hybrid diet recommendations - p. 65 12. 6 Impact of number and type of criteria on ranking abnormality in MCDM techniques - p. 77 13. 7 Comparison between some methods in fuzzy linear regression - p. 87 14. 8 Artificial intelligence and decision making in climate change studies: A review - p. 109 15. 9 Computational decision intelligence approaches for drought prediction: A review - p. 119 16. 10 A review of the applications of computational decision intelligence approaches in agrometeorology - p. 133 17. 11 A fuzzy logic design for self-driving vehicle to avoid obstacles - p. 143 18. 12 K-means clustering over distributed environment: A review - p. 173 19. 13 Advanced frequent itemset mining algorithm (AFIM) - p. 187 20. 14 TEAM: Trust evaluation and analysis of misbehavior in WSNs - p. 203 21. 15 Computational intelligence in decision support: Scope and techniques - p. 219 22. 16 Automatic parallelization for multicore architectures: Role, importance, and opportunities - p. 239 23. 17 Using tensor processing units to identify the relationship between hypothesis and premise: A case of natural language inference problem - p. 255 24. 18 Secure and cost-effective key management scheme for the Internet of Things-supported WSN - p. 277 25. 19 A deep learning-based integrated voice assistance system for partially disabled people - p. 293 26. Index - p. 311
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.
1. Uncertainty in Computational Intelligence-Based Decision Making - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Table of Contents - p. v 5. Copyright Page - p. iv 6. List of Contributors - p. xiii 7. 1 TOPSIS for the selection of the prediction model in forensic ink analysis - p. 1 8. 2 Effectiveness of artificial intelligence in determining foot pathologies and designing insoles using plain radiograph and digital photograph - p. 13 9. 3 Outbound logistics business process modeling: Analytic perspective with BPMN 2.0 - p. 23 10. 4 Revolutionizing diabetic foot ulcer treatment prediction: Harnessing the power of artificial intelligence and transfer learning - p. 55 11. 5 A systematic review on personalized hybrid diet recommendations - p. 65 12. 6 Impact of number and type of criteria on ranking abnormality in MCDM techniques - p. 77 13. 7 Comparison between some methods in fuzzy linear regression - p. 87 14. 8 Artificial intelligence and decision making in climate change studies: A review - p. 109 15. 9 Computational decision intelligence approaches for drought prediction: A review - p. 119 16. 10 A review of the applications of computational decision intelligence approaches in agrometeorology - p. 133 17. 11 A fuzzy logic design for self-driving vehicle to avoid obstacles - p. 143 18. 12 K-means clustering over distributed environment: A review - p. 173 19. 13 Advanced frequent itemset mining algorithm (AFIM) - p. 187 20. 14 TEAM: Trust evaluation and analysis of misbehavior in WSNs - p. 203 21. 15 Computational intelligence in decision support: Scope and techniques - p. 219 22. 16 Automatic parallelization for multicore architectures: Role, importance, and opportunities - p. 239 23. 17 Using tensor processing units to identify the relationship between hypothesis and premise: A case of natural language inference problem - p. 255 24. 18 Secure and cost-effective key management scheme for the Internet of Things-supported WSN - p. 277 25. 19 A deep learning-based integrated voice assistance system for partially disabled people - p. 293 26. Index - p. 311
