Decision-Making Models

A Perspective of Fuzzy Logic and Machine Learning

1st Edition - July 24, 2024
Editors: Tofigh Allahviranloo, Witold Pedrycz, Amir Seyyedabbasi
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 6 1 4 7 - 6
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 6 1 4 8 - 3

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 a… Read more

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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.

Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty.

Cover image
Title page
Table of Contents
Front Matter
Copyright
Contributors
Preface
Section 1: Decision-making: New developments
Chapter 1 Neural networks
Abstract
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
Abstract
2.1 Introduction to artificial intelligence
2.2 Types of AI 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
Abstract
3.1 Introduction
3.2 Literature review
3.3 Background
3.4 Method
3.5 Analysis and findings
3.6 Discussion
3.7 Conclusions
References
Chapter 4 Learning theory
Abstract
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
Abstract
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
Abstract
6.1 Introduction
6.2 Physics-based algorithms
6.3 Simulation results
References
Chapter 7 A comprehensive survey: Evolutionary-based algorithms
Abstract
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
Abstract
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
Abstract
9.1 Introduction and motivation
9.2 Literature review
9.3 Heuristic-based software module clustering
9.4 Experiments and results
9.5 Conclusion
References
Chapter 10 Constraint-based heuristic algorithms for software test generation
Abstract
10.1 Introduction and motivation
10.2 Literature review
10.3 Heuristic-based test data generation
10.4 Results and discussion
10.5 Conclusion
References
Chapter 11 Discretized optimization algorithms for finding the bug-prone locations of a program source code
Abstract
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
Abstract
Acknowledgment
12.1 Introduction
12.2 Types of mathematical programming
12.3 Basic concepts
12.4 Simplex algorithm
12.5 Nonlinear programming
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
References
Chapter 13 Discrete and combinatorial optimization
Abstract
Acknowledgment
13.1 Introduction
13.2 Examining search and optimization methods
13.3 Integer programming
13.4 Branch-and-bound method
13.5 Additive algorithm for pure binary problem
13.6 The Transportation problem
13.7 Find the optimum solution of transportation problem
13.8 Conclusion
References
Chapter 14 Data optimization and analysis
Abstract
Acknowledgment
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
14.7 Conclusion
References
Chapter 15 Applied optimization problems
Abstract
Acknowledgment
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
15.9 Mean–variance analysis
15.10 Multiperiod binomial model
15.11 Queuing theory optimization
15.12 Supply chain concept and its applications
15.13 Multiobjective optimization is an optimization
15.14 Conclusion
References
Chapter 16 Engineering optimization
Abstract
16.1 Introduction
16.2 Types of optimization problems
16.3 Engineering optimization
16.4 Conclusion
References
Section 4: Machine learning
Chapter 17 Deep learning
Abstract
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
Abstract
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
Abstract
19.1 Introduction and motivation
19.2 Literature review
19.3 Background
19.4 Training schemes
19.5 Experiment
19.6 Results and discussions
19.7 Conclusion
References
Chapter 20a Classification and clustering
Abstract
20a.1 Introduction and motivation
20a.2 Literature review
20a.3 Datasets
20a.4 Experiments and results
20a.5 Conclusion
References
Chapter 20b Estimating the growth or depreciation on exchange rates using sentiment analysis method from social media comments
Abstract
20b.1 Introduction
20b.2 Dataset
20b.3 Data preprocessing
20b.4 Methods
20b.5 Implementations of algorithms
20b.6 Discussion and conclusion
References
Section 5: Soft computation
Chapter 21 Uncertainty theory
Abstract
21.1 Introduction
21.2 Uncertainty
21.3 Uncertainty space
21.4 Uncertain set
References
Chapter 22 Fuzzy sets
Abstract
22.1 Introduction
22.2 Basic properties of fuzzy sets
22.3 Different representations of fuzzy sets
22.4 Set operators
22.5 α − cuts
22.6 Fuzzy number
References
Chapter 23 Computations with words
Abstract
23.1 Introduction
23.2 The gray shades
23.3 Fuzzy principle
23.4 Fuzzy at present
23.5 Fuzzy systems in industry
23.6 Uses
References
Chapter 24 Soft modeling
Abstract
24.1 Introduction and motivation
24.2 Fuzzy fractional diffusion model for cancer tumors
24.3 Fuzzy modeling automatic detection of human dendritic cells
24.4 Fuzzy production-inventory model with decay under Marxist principle
24.5 Modeling the impact of learning and credit financing on supply chain performance for items with quality issues under fuzzy environment
24.6 Fuzzy modeling of market equilibrium price
24.7 Conclusion
References
Chapter 25 Uncertain optimization (with a special focus on data envelopment analysis)
Abstract
25.1 Stochastic type of uncertainty
25.2 Mathematical expectation
25.3 The normal distribution
25.4 Stochastic programming
25.5 Stochastic data envelopment analysis
25.6 Stochastic BCC model of Banker et al. (1984)
25.7 A value-based measure of stochastic technical efficiency
References
Chapter 26 Chaos theory and chaotic systems
Abstract
26.1 Introduction
26.2 History
26.3 Presented definitions
References
Section 6: Data analysis
Chapter 27 Comparison of machine learning models for lung cancer prediction using different feature selection methodologies
Abstract
27.1 Introduction
27.2 Literature review
27.3 Methods and materials
27.4 Results and discussion
27.5 Conclusion
References
Chapter 28 Early detection of cardiovascular disease: Data visualization, feature selection, and machine learning algorithms for predictive diagnosis
Abstract
28.1 Introduction
28.2 Literature survey
28.3 Methodology
28.4 Model evaluation
28.5 Results and discussions
28.6 Conclusion
References
Chapter 29 Optimization of tree-based machine learning algorithms for improving the predictive accuracy of hepatitis C disease
Abstract
29.1 Introduction
29.2 Machine learning process
29.3 Model evaluation
29.4 Model testing
29.5 Results and discussion
29.6 Conclusion
References
Chapter 30 Prediction of software faults using machine learning algorithms and mitigating risks with feature selection
Abstract
30.1 Introduction and motivation
30.2 Literature review
30.3 Methodology
30.4 Software fault prediction process
30.5 Results and discussion
30.6 Conclusion
References
Section 7: Fuzzy decision system
Chapter 31 The interaction of fuzzy logic with machine learning and artificial intelligence in decision-making models
Abstract
31.1 Introduction and motivation
31.2 Literature review
31.3 Machine learning and artificial intelligence
31.4 Fuzzy logic and machine learning
31.5 Decision-making development
31.6 Conlusion
References
Chapter 32 Approximate reasoning
Abstract
32.1 Introduction
32.2 From two-logic reasoning schemes to multivalued and fuzzy logic
32.3 Approximate reasoning
32.4 Relational calculus in the realization of approximate reasoning
32.5 Approximate solutions to relational equations
32.6 Type-2 information granules in the development of solutions
32.7 Approximate reasoning and rule-based computing: An implementation environment
32.8 Conclusions and prospects
References
Chapter 33 Effectiveness in fuzzy logic: Applications of fuzzy fractional differential equations
Abstract
33.1 Introduction
33.2 Fuzzy optimal control problem
33.3 Fuzzy fractional diffusion equations
33.4 Fundamental solution of fuzzy fractional diffusion equation
References
Chapter 34 Neuro-fuzzy systems
Abstract
34.1 Introduction
34.2 Background and definitions
34.3 Neuro-fuzzy systems
34.4 Recent applications of neuro-fuzzy system
34.5 Conclusion
References
Chapter 35 Fuzzy rule-based systems: How to construct a FRBS with MATLAB, R, and Python
Abstract
35.1 Introduction
35.2 Literature review
35.3 Preliminaries of fuzzy rule-based systems
35.4 Case study
35.5 Conclusions
References
Index

Tofigh Allahviranloo

Tofigh Allahviranloo is a full professor of applied mathematics at Istinye University, Turkey. As a trained mathematician and computer scientist, Prof. Allahviranloo has developed a passion for multi- and interdisciplinary research. He is not only deeply involved in fundamental research in fuzzy applied mathematics, especially fuzzy differential equations, but he also aims at innovative applications in the applied biological sciences. He is the author of several books and many papers published by Elsevier and Springer. He actively serves the research community, as Editor-in-Chief of the International J. of Industrial Mathematics, and Associate Editor or editorial board member of several other journals, including Information Sciences, Fuzzy Sets and Systems, Journal of Intelligent and Fuzzy Systems, Iranian J. of Fuzzy Systems and Mathematical Sciences.

Affiliations and expertise

Full Professor, Istinye University, Istanbul, Turkey

Witold Pedrycz

Dr. Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in computational intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. In 2012 he was elected a fellow of the Royal Society of Canada. His main research directions involve computational intelligence, fuzzy modeling and granular computing, knowledge discovery and data science, pattern recognition, data science, knowledge-based neural networks, and control engineering. He is also an author of 18 research monographs and edited volumes covering various aspects of computational intelligence, data mining, and software engineering. Dr. Pedrycz is vigorously involved in editorial activities. He is the editor-in-chief of Information Sciences, editor-in-chief of WIREs Data Mining and Knowledge Discovery, and co-editor-in-chief of International Journal of Granular Computing, and Journal of Data Information and Management. He serves on the advisory board of IEEE Transactions on Fuzzy Systems.

Affiliations and expertise

Professor, Department of Electrical and Computer Engineering, University of Alberta, Canada

Amir Seyyedabbasi

Amir Seyyedabbasi is an assistant professor of software engineering at İstinye University, Turkey. He received his B.Sc., M.Sc., and Ph.D. degrees in computer engineering. His research interests include optimization algorithms, routing protocol design in wireless sensor networks, and IoT. Dr. Seyyedabbasi has several articles in Springer and Elsevier. He serves as a reviewer in some respected journals. He aims to develop and propose new and the hybrid optimization algorithm in engineering.

Affiliations and expertise

Assistant Professor, Istinye University, Istanbul, Turkey

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Decision-Making Models

A Perspective of Fuzzy Logic and Machine Learning

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Institutional subscription on ScienceDirect

Tofigh Allahviranloo

Witold Pedrycz

Amir Seyyedabbasi