DECISION MAKING MODELS : a perspective of fuzzy logic and machine learning.

This book is part of the Computational Techniques, and Decision Intelligence series and focuses on advanced computational methods and decision-making processes using artificial intelligence. It covers a range of topics including neural networks, artificial intelligence algorithms, metaheuristic algo...

Full description

Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Format:	eBook
Language:	English
Published:	[S.l.] : ELSEVIER ACADEMIC PRESS, 2024.
Series:	Uncertainty, computational techniques, and decision intelligence
Subjects:	Decision making > Mathematical models. Decision making > Computer simulation. Prise de décision > Modèles mathématiques. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Intro
Decision-Making Models
Copyright
Contents
Contributors
Preface
Section 1: Decision-making: New developments
Chapter 1: Neural networks
1.1. Introduction and motivation
1.2. Neural networks overview
1.3. Exploring advanced neural network concepts
1.4. Neural networks and decision-making
References
Chapter 2: Artificial intelligent algorithms, motivation, and terminology
2.1. Introduction to artificial intelligence
2.2. Types of AI algorithms
2.2.1. Supervised learning
2.2.2. Unsupervised learning
2.2.3. Semisupervised learning
2.2.4. Reinforcement learning
2.2.5. Ensemble learning
2.2.6. Deep learning
2.2.7. Neural networks
2.2.8. Convolutional neural networks (CNNs)
2.2.9. Recurrent neural networks (RNNs)
2.2.10. Generative adversarial networks (GANs)
2.2.11. Heuristic algorithms
2.2.12. Metaheuristic algorithms
2.3. Types of problems solved using artificial intelligence algorithms
2.4. Evolution of AI algorithms
References
Chapter 3: Decision process: A stakeholder-oriented video conferencing software selection in sustainable distance education
3.1. Introduction
3.2. Literature review
3.3. Background
3.3.1. Rational decision-making model
3.3.2. Bounded rationality decision-making model
3.3.3. Intuitive decision-making model
3.3.4. Creative decision-making model
3.4. Method
3.4.1. Preliminaries of picture fuzzy sets (PFSs)
3.4.2. MACTOR method
3.4.3. Proposed PF-MACTOR method
3.5. Analysis and findings
3.5.1. Stakeholders of SDE
3.5.2. Objectives of SDE
3.5.3. Application of PF-MACTOR method and findings
3.6. Discussion
3.7. Conclusions
References
Chapter 4: Learning theory
4.1. Introduction to learning theory
4.2. Ethical considerations in learning models.
4.3. Learning theory and decision-making
4.4. Future directions in learning theory
References
Section 2: Metaheuristic algorithms
Chapter 5: A comprehensive survey: Nature-inspired algorithms
5.1. Nature-inspired algorithms: What are they?
5.2. Motivation
5.3. Optimization
5.4. No-free-lunch
5.5. Nature-inspired metaheuristics
References
Chapter 6: A comprehensive survey: Physics-based algorithms
6.1. Introduction
6.2. Physics-based algorithms
6.2.1. Gravitational search algorithm
6.2.2. Atom search optimization
6.2.3. Sonar-inspired optimization
6.2.4. Vortex search algorithm
6.2.5. Lightning search algorithm
6.2.6. RIME: A physics-based optimization
6.2.7. Henry gas solubility optimization
6.2.8. Multiverse optimizer
6.3. Simulation results
References
Chapter 7: A comprehensive survey: Evolutionary-based algorithms
7.1. Introduction
7.2. Evolutionary strategy
7.3. Genetic algorithm
7.4. Genetic programming
7.5. Differential evolution
7.6. Discussion
References
Chapter 8: A comprehensive survey: Swarm-based algorithms
8.1. Introduction
8.2. Ant colony optimization
8.3. Cat swarm optimization
8.4. Elephant herding optimization
8.5. Gray wolf optimization
8.6. Harris hawks optimization
8.7. Marine predators algorithm
8.8. Particle swarm optimization
8.9. Sand cat swarm optimization
8.10. Case study: Kinematics PUMA 560
References
Chapter 9: Single and multi-objective metaheuristic algorithms and their applications in software maintenance
9.1. Introduction and motivation
9.2. Literature review
9.3. Heuristic-based software module clustering
9.3.1. Definition of the problem
9.3.2. Algorithm structure
9.3.3. Objective function
9.4. Experiments and results
9.4.1. Experiments platform
9.4.2. Results.
9.5. Conclusion
References
Chapter 10: Constraint-based heuristic algorithms for software test generation
10.1. Introduction and motivation
10.2. Literature review
10.3. Heuristic-based test data generation
10.3.1. Generating test data
10.3.2. Fitness function
10.4. Results and discussion
10.4.1. Implementation
10.4.2. Benchmarks
10.4.3. Evaluation
10.5. Conclusion
References
Chapter 11: Discretized optimization algorithms for finding the bug-prone locations of a program source code
11.1. Introduction and motivation
11.2. Literature review
11.3. Identifying the bug-prone paths of the programs
11.4. Experiments and results
11.5. Conclusion
References
Section 3: Optimization problems
Chapter 12: Mathematical programming
12.1. Introduction
12.2. Types of mathematical programming
12.3. Basic concepts
12.4. Simplex algorithm
12.5. Nonlinear programming
12.5.1. Unconstrained problems
12.6. Constrained problems with equality constraints
12.7. Lagrange multiplier method
12.8. Unconstrained problem with inequality constraints
12.9. Double search
12.10. Interval bisection method
12.11. Conclusion
Acknowledgment
References
Chapter 13: Discrete and combinatorial optimization
13.1. Introduction
13.1.1. Various optimization problems are divided into the following two categories
13.2. Examining search and optimization methods
13.2.1. Enumerative methods
13.2.2. Calculation methods (mathematical search or-based method calculus)
13.2.3. Innovative and meta-innovative methods (random search)
13.2.4. Combinational optimization problems
13.2.5. The method of solving combined optimization problems
13.2.5.1. Relaxation
13.2.5.2. Analysis
13.2.5.3. Column generation method
13.2.5.4. Constructive search.
13.2.5.5. Improving search
13.2.6. Neighborhood search method
13.2.7. Metaheuristic methods derived from nature
13.2.8. Traveling salesman problem (TSP)
13.3. Integer programming
13.3.1. Solution methods
13.3.1.1. Cutting plane method
13.3.1.2. Mixed algorithm
13.4. Branch-and-bound method
13.5. Additive algorithm for pure binary problem
13.5.1. Branch and bound zero-one tree
13.6. The Transportation problem
13.6.1. Fogel's approximation method
13.7. Find the optimum solution of transportation problem
13.8. Conclusion
Acknowledgment
References
Chapter 14: Data optimization and analysis
14.1. Introduction
14.2. Data envelopment analysis
14.3. Network data envelopment analysis
14.4. Progress and regress
14.5. Ranking
14.6. Data analysis and support vector machines
14.6.1. Separable state and data clustering
14.6.2. Nonseparable state
14.6.3. Nonlinear SVM
14.6.4. Separable SVMs in multiple categories
14.6.5. SVM applications
14.7. Conclusion
Acknowledgment
References
Chapter 15: Applied optimization problems
15.1. Introduction
15.2. Linear Programming
15.3. Integer programing
15.4. Nonlinear programming
15.5. Network programing
15.6. Inventory
15.7. Calculus of variations
15.8. Risk measurement
15.8.1. Standard risk measures, value-at-risk (VaR)
15.9. Mean-variance analysis
15.9.1. Diversification effect
15.10. Multiperiod binomial model
15.11. Queuing theory optimization
15.12. Supply chain concept and its applications
15.12.1. Applications of data envelopment analysis (DEA) in supply chain
15.13. Multiobjective optimization is an optimization
15.13.1. Applications of multiobjective optimization
15.13.2. Applications of optimization in reliability models.
15.13.3. Classic reliability optimization models
15.13.4. Presenting a series-parallel redundancy allocation problem (RAP)
15.13.5. Reliability optimization of a k-out-of-n series-parallel system
15.14. Conclusion
Acknowledgment
References
Chapter 16: Engineering optimization
16.1. Introduction
16.2. Types of optimization problems
16.2.1. ``Continuous´´ and ``discrete´´ optimization problems
16.2.2. ``Constrained´´ and ``unconstrained´´ optimization problems
16.2.3. ``Deterministic´´ and ``stochastic´´ optimization problems
16.2.4. Nonobjective, single-objective, and multiobjective optimization problems
16.2.5. Heuristic and meta-heuristic methods (random search)
16.3. Engineering optimization
16.3.1. Types of selected engineering optimization methods
16.3.2. Deterministic and probabilistic optimization algorithms
16.3.3. Direct and indirect optimization algorithms
16.3.4. Heuristic and metaheuristic optimization algorithm
16.3.4.1. Genetic algorithm
16.3.4.2. Simulated annealing
16.3.4.3. Tabu search
16.3.4.4. Particle swarm optimization algorithm
16.3.5. Comparison and selection of the appropriate optimization method
16.3.6. Advantages of the genetic algorithm compared to other optimization methods in engineering
16.4. Conclusion
References
Section 4: Machine learning
Chapter 17: Deep learning
17.1. Introduction and motivation
17.2. Background
17.3. Literature review
17.4. Minatar
17.5. Results and discussions
17.6. Conclusion
References
Chapter 18: (Artificial) neural networks
18.1. Introduction and motivation
18.2. Literature review
18.3. Artificial neural networks
18.4. Dataset
18.5. Experiments and results
18.6. Conclusion
References
Chapter 19: Reinforcement learning algorithms
19.1. Introduction and motivation.