Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems, optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision making mechanisms realized under uncertainty.
1. Decision-Making Models - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. Preface - p. Page viii 8. List of Illustrations - p. Page ix 9. List of Tables - p. Page x 10. Chapter 1 : Neural networks - p. Page x 11. Chapter 2 : Artificial intelligent algorithms, motivation, and terminology - p. Page 8 12. Chapter 3 : Decision process: A stakeholder-oriented video conferencing software selection in sustainable distance education - p. Page 16 13. Chapter 4 : Learning theory - p. Page 40 14. Chapter 5 : A comprehensive survey: Nature-inspired algorithms - p. Page 50 15. Chapter 6 : A comprehensive survey: Physics-based algorithms - p. Page 57 16. Chapter 7 : A comprehensive survey: Evolutionary-based algorithms - p. Page 73 17. Chapter 8 : A comprehensive survey: Swarm-based algorithms - p. Page 82 18. Chapter 9 : Single and multi-objective metaheuristic algorithms and their applications in software maintenance - p. Page 93 19. Chapter 10 : Constraint-based heuristic algorithms for software test generation - p. Page 109 20. Chapter 11 : Discretized optimization algorithms for finding the bug-prone locations of a program source code - p. Page 121 21. Chapter 12 : Mathematical programming - p. Page 134 22. Chapter 13 : Discrete and combinatorial optimization - p. Page 172 23. Chapter 14 : Data optimization and analysis - p. Page 205 24. Chapter 15 : Applied optimization problems - p. Page 231 25. Chapter 16 : Engineering optimization - p. Page 292 26. Chapter 17 : Deep learning - p. Page 312 27. Chapter 18 : (Artificial) neural networks - p. Page 325 28. Chapter 19 : Reinforcement learning algorithms - p. Page 336 29. Chapter 20a : Classification and clustering - p. Page 347 30. Chapter 20b : Estimating the growth or depreciation on exchange rates using sentiment analysis method from social media comments - p. Page 358 31. Chapter 21 : Uncertainty theory - p. Page 375 32. Chapter 22 : Fuzzy sets - p. Page 389 33. Chapter 23 : Computations with words - p. Page 404 34. Chapter 24 : Soft modeling - p. Page 414 35. Chapter 25 : Uncertain optimization (with a special focus on data envelopment analysis) - p. Page 446 36. Chapter 26 : Chaos theory and chaotic systems - p. Page 463 37. Chapter 27 : Comparison of machine learning models for lung cancer prediction using different feature selection methodologies - p. Page 477 38. Chapter 28 : Early detection of cardiovascular disease: Data visualization, feature selection, and machine learning algorithms for predictive diagnosis - p. Page 493 39. Chapter 29 : Optimization of tree-based machine learning algorithms for improving the predictive accuracy of hepatitis C disease - p. Page 520 40. Chapter 30 : Prediction of software faults using machine learning algorithms and mitigating risks with feature selection - p. Page 544 41. Chapter 31 : The interaction of fuzzy logic with machine learning and artificial intelligence in decision-making models - p. Page 560 42. Chapter 32 : Approximate reasoning - p. Page 570 43. Chapter 33 : Effectiveness in fuzzy logic: Applications of fuzzy fractional differential equations - p. Page 586 44. Chapter 34 : Neuro-fuzzy systems - p. Page 604 45. Chapter 35 : Fuzzy rule-based systems: How to construct a FRBS with MATLAB, R, and Python - p. Page 620 46. Index - p. Page 634
Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems, optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision making mechanisms realized under uncertainty.
1. Decision-Making Models - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. Preface - p. Page viii 8. List of Illustrations - p. Page ix 9. List of Tables - p. Page x 10. Chapter 1 : Neural networks - p. Page x 11. Chapter 2 : Artificial intelligent algorithms, motivation, and terminology - p. Page 8 12. Chapter 3 : Decision process: A stakeholder-oriented video conferencing software selection in sustainable distance education - p. Page 16 13. Chapter 4 : Learning theory - p. Page 40 14. Chapter 5 : A comprehensive survey: Nature-inspired algorithms - p. Page 50 15. Chapter 6 : A comprehensive survey: Physics-based algorithms - p. Page 57 16. Chapter 7 : A comprehensive survey: Evolutionary-based algorithms - p. Page 73 17. Chapter 8 : A comprehensive survey: Swarm-based algorithms - p. Page 82 18. Chapter 9 : Single and multi-objective metaheuristic algorithms and their applications in software maintenance - p. Page 93 19. Chapter 10 : Constraint-based heuristic algorithms for software test generation - p. Page 109 20. Chapter 11 : Discretized optimization algorithms for finding the bug-prone locations of a program source code - p. Page 121 21. Chapter 12 : Mathematical programming - p. Page 134 22. Chapter 13 : Discrete and combinatorial optimization - p. Page 172 23. Chapter 14 : Data optimization and analysis - p. Page 205 24. Chapter 15 : Applied optimization problems - p. Page 231 25. Chapter 16 : Engineering optimization - p. Page 292 26. Chapter 17 : Deep learning - p. Page 312 27. Chapter 18 : (Artificial) neural networks - p. Page 325 28. Chapter 19 : Reinforcement learning algorithms - p. Page 336 29. Chapter 20a : Classification and clustering - p. Page 347 30. Chapter 20b : Estimating the growth or depreciation on exchange rates using sentiment analysis method from social media comments - p. Page 358 31. Chapter 21 : Uncertainty theory - p. Page 375 32. Chapter 22 : Fuzzy sets - p. Page 389 33. Chapter 23 : Computations with words - p. Page 404 34. Chapter 24 : Soft modeling - p. Page 414 35. Chapter 25 : Uncertain optimization (with a special focus on data envelopment analysis) - p. Page 446 36. Chapter 26 : Chaos theory and chaotic systems - p. Page 463 37. Chapter 27 : Comparison of machine learning models for lung cancer prediction using different feature selection methodologies - p. Page 477 38. Chapter 28 : Early detection of cardiovascular disease: Data visualization, feature selection, and machine learning algorithms for predictive diagnosis - p. Page 493 39. Chapter 29 : Optimization of tree-based machine learning algorithms for improving the predictive accuracy of hepatitis C disease - p. Page 520 40. Chapter 30 : Prediction of software faults using machine learning algorithms and mitigating risks with feature selection - p. Page 544 41. Chapter 31 : The interaction of fuzzy logic with machine learning and artificial intelligence in decision-making models - p. Page 560 42. Chapter 32 : Approximate reasoning - p. Page 570 43. Chapter 33 : Effectiveness in fuzzy logic: Applications of fuzzy fractional differential equations - p. Page 586 44. Chapter 34 : Neuro-fuzzy systems - p. Page 604 45. Chapter 35 : Fuzzy rule-based systems: How to construct a FRBS with MATLAB, R, and Python - p. Page 620 46. Index - p. Page 634
