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E-grāmata: Machine Learning and Probabilistic Graphical Models for Decision Support Systems

Edited by (ENSAIT & GEMTEX, France)
  • Formāts: 330 pages
  • Izdošanas datums: 13-Oct-2022
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781000771466
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Machine Learning and Probabilistic Graphical Models for Decision Support SystemsPalielināt
  • Formāts: 330 pages
  • Izdošanas datums: 13-Oct-2022
  • Izdevniecība: CRC Press
  • Valoda: eng
  • ISBN-13: 9781000771466
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"This book presents recent advancements of research, new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models. The book undertakes to stimulate scientific exchange, ideas, andexperiences in the field of DSS applications. Researchers, postgraduate students, and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their use in DSS in the context ofdecision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications are introduced in each chapter"--

This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.



This book presents recent advancements in research, new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models. The book undertakes to stimulate scientific exchange, ideas, and experiences in the field of DSS applications.

Preface iii
Acronyms xi
1 Introduction to Machine Learning and Probabilistic Graphical Models for Decision Support Systems
1(4)
Kim Phuc Tran
1 Scope of the Research Domain
1(1)
2 Structure of the Book
2(1)
3 Conclusion
3(2)
References
4(1)
2 Decision Support Systems for Healthcare based on Probabilistic Graphical Models: A Survey and Perspective
5(29)
All Raza
Kim Phuc Tran
Ludovic Koehl
Shujun Li
1 Introduction
5(3)
1.1 Probabilistic Modeling
6(1)
1.2 Applications of PGMs
7(1)
2 Decision Support Systems in Healthcare
8(9)
2.1 Probabilistic Graphical Models
9(1)
2.2 Bayesian Networks: Directed Graphical Models
10(2)
2.3 Markov Random Fields
12(2)
2.4 Deep Neural Networks
14(2)
2.5 Neural Networks with Probabilistic Graphical Models
16(1)
3 Artificial Intelligence in Healthcare Applications
17(2)
4 Healthcare Decision Support Systems based on Probabilistic Graphical Models
19(1)
5 Perspectives for Healthcare Decision Support Systems based on Probabilistic Graphical Models
20(1)
6 Case Studies
21(5)
6.1 Logistic Regression for ECG Classification
21(1)
6.2 Variational Autoencoder for ECG Anomaly Detection
22(4)
7 Conclusions
26(8)
References
27(7)
3 Decision Support Systems for Anomaly Detection with the Applications in Smart Manufacturing: A Survey and Perspective
34(28)
Quoc-Thong Nguyen
TungNhi Tran
Cedric Heuchenne
Kim Phuc Tran
1 Introduction
34(1)
2 Decision Support Systems for Smart Manufacturing
35(2)
3 Anomaly Detection in Smart Manufacturing
37(5)
3.1 Smart Predictive Maintenance
37(1)
3.2 Integrated Wearable Technology
38(2)
3.3 Production Monitoring
40(1)
3.4 Real-time Cybersecurity
41(1)
4 Difficulties and Challenges of Anomaly Detection Applications in Smart Manufacturing
42(1)
5 Perspectives for Anomaly Detection in Smart Manufacturing
43(4)
6 Case Studies
47(15)
6.1 Anomaly Detection in Production Monitoring
47(2)
6.2 Anomaly Detection in Predictive Maintenance
49(3)
7 Concluding Remarks
52(1)
References
53(9)
4 Decision Support System for Complex Systems Risk Assessment with Bayesian Networks
62(26)
Ayeley Tchangani
1 Introduction
62(2)
2 Bayesian Technology
64(1)
3 BN Model for Event Oriented Risk Management
64(7)
3.1 Variables Identification
64(2)
3.2 Relationships Identification
66(1)
3.3 Usage of the model
67(1)
3.4 Illustrative Case Study in Natural Risk Management
67(4)
4 BN for Risk Management in Industrial Systems
71(4)
5 DBN for Risk Management of Industrial Systems
75(3)
5.1 Brief Presentation of DBN
75(1)
5.2 Illustrative Case Study
76(2)
6 EOOBN for Risk Management
78(7)
6.1 Extended Object Oriented Bayesian Network
80(1)
6.1.1 Construction of an EOOBN
80(3)
6.1.2 Case Study
83(2)
7 Conclusion
85(3)
References
86(2)
5 Decision Support System using LSTM with Bayesian Optimization for Predictive Maintenance: Remaining Useful Life Prediction
88(19)
Huu Du Nguyen
Kim Phuc Tran
1 Introduction
88(1)
2 Predictive Maintenance and Remaining Useful Life Prediction
89(3)
3 Machine Learning based Decision Support System for Predictive Maintenance
92(1)
4 Long Short Term Memory Networks using Bayesian Optimization
93(3)
4.1 Long Short Term Memory Networks
93(2)
4.2 Bayesian Optimization
95(1)
5 Decision Support System for Remaining Useful Life Prediction using LSTM with Bayesian Optimization
96(2)
6 A Case Study
98(1)
7 Conclusion and Perspectives
99(8)
References
101(6)
6 Decision Support Systems for Textile Manufacturing Process with Machine
107(17)
Learning Zaohao Lu
Zhenglei He
Kim Phuc Tran
Sebastien Thomassey
Xianyi Zeng
MengnaHong
1 Introduction
107(1)
2 Relevant Literatures
108(6)
2.1 Intelligent Techniques used for Textile Process Modeling
109(1)
2.1.1 Artificial Neural Networks
109(1)
2.1.2 Fuzzy Logic
110(1)
2.1.3 Fuzzy Inference System
111(1)
2.1.4 Support Vector Machine
111(1)
2.1.5 Gene Expression Programming
111(1)
2.2 Decision-making of Textile Manufacturing Process
111(1)
2.2.1 Classic Methods
112(1)
2.2.2 Meta-heuristic Methods
112(1)
2.2.3 Multi-criteria Meta-heuristic Methods
112(2)
3 Case Study: Decision-making of Denim Ozonation
114(6)
3.1 Problem Formulation
114(1)
3.2 Methodology
115(1)
3.2.1 ANN Model
115(1)
3.2.2 Determining the Criteria Weights using the AHP
116(1)
3.2.3 The Markov Decision Process
117(1)
3.2.4 The RL Algorithm: Q-learning
118(1)
3.3 Case Study
118(1)
3.3.1 Results and Discussion
119(1)
4 Conclusion
120(4)
References
121(3)
7 Anomaly Detection Enables Cybersecurity with Machine Learning Techniques
124(60)
Truong Thu Huong
Nguyen Minh Dan
Le Anh Quang
Nguyen Xuan Hoang
Le Thanh Cong
Kieu-Ha Phung
Kim Phuc Tran
1 Introduction
124(1)
2 Cybersecurity of Industrial Systems
125(3)
2.1 Cyberattack Detection for Industrial Control Systems
126(1)
2.2 Anomaly Detection for Time-series Data
127(1)
3 Machine Learning-based Anomaly Detection for Cybersecurity Applications
128(4)
3.1 Data Driven Hyperparameter Optimization of One-Class Support Vector Machines for Anomaly Detection in Wireless Sensor Networks
129(1)
3.1.1 Anomaly Detection Scheme
129(3)
3.1.2 Illustrative Example in WSN Anomaly Detection
132(1)
3.2 Real Time Data-Driven Approaches for Credit Card Fraud Detection
132(10)
3.2.1 Anomaly Detection Scheme
132(1)
3.2.2 Illustrative Example in Credit Card Fraud Detection
133(1)
3.3 Nested One-Class Support Vector Machines for Network Anomaly Detection
134(1)
3.3.1 Nested OCSVMs and Anomaly Detection Scheme
134(2)
3.3.2 Illustrative Example in Network Anomaly Detection
136(1)
3.4 A Data-Driven Approach for Network Anomaly Detection and Monitoring Based on Kernel Null Space
137(1)
3.4.1 Anomaly Detection Scheme
138(3)
3.4.2 Illustrative Example in Network Anomaly Detection
141(1)
4 Federated Learning-based Anomaly Detection for Cybersecurity Applications
142(1)
4.1 Security System Architecture for IoT Systems
142(15)
4.1.1 Design of Edge-Cloud System Architecture
142(2)
4.1.2 Data Pre-processing at the Edge
144(1)
4.1.3 Detection Mechanism
144(5)
4.1.4 Performance Evaluation
149(8)
4.1.5 Summary
157(1)
4.2 Anomaly Detection in Industrial Control System--Smart Manufacturing
157(18)
4.2.1 Federated Learning-based Architecture for Smart Manufacturing
158(3)
4.2.2 Anomaly Detection Algorithm using Hybrid VAE-LSTM Model at Edge Devices
161(5)
4.2.3 Data Pre-processing
166(1)
4.2.4 Detection Performance Evaluation
167(2)
4.2.5 Evaluation on Edge Computing Efficiency
169(5)
4.2.6 Summary
174(1)
5 Difficulties, Challenges, and Perspectives for Machine Learning-based Anomaly Detection for Cybersecurity Applications
175(2)
6 Conclusion
177(7)
References
178(6)
8 Machine Learning for Compositional Data Analysis in Support of the Decision Making Process
184(32)
Thi Thuy Van Nguyen
Cedric Heuchenne
Kim Phuc Tran
1 Introduction
184(1)
2 Modeling of Compositional Data
185(2)
3 Machine Learning for Multivariate Compositional Data
187(16)
3.1 Principal Component Analysis
188(3)
3.2 Clustering
191(2)
3.3 Classification
193(1)
3.3.1 Support Vector Machine Classification using Ilr--Transformation
194(2)
3.3.2 Support Vector Machine Classification using Dirichlet Feature Embedding Transformation
196(2)
3.4 Regression
198(5)
4 Anomaly Detection using Support Vector Data Description
203(6)
4.1 Support Vector Data Description
203(1)
4.2 Anomaly Detection using SVDD with Dirichlet Density Estimation
204(1)
4.2.1 Transform CoDa using Dirichlet Density Estimation
204(2)
4.2.2 Anomaly Detection using SVDD with Dirichlet Density-transformed Data
206(1)
4.2.3 An Example of Anomaly Detection using SVDD
207(2)
5 Conclusion
209(7)
References
210(6)
9 Decision Support System with Genetic Algorithm for Economic Statistical Design of Nonparametric Control Chart
216(36)
Alejandro Marcos Alvarez
Cedric Heuchenne
Phuong Hanh Tran
Alireza Faraz
1 Introduction
216(2)
2 Background
218(4)
2.1 Statistical Process Monitoring with Control Chart
218(1)
2.2 Parametric and Nonparametric Control Charts
219(1)
2.2.1 The x Chart
219(1)
2.2.2 The SN Chart
219(1)
2.2.3 The SR Chart
220(1)
2.3 Related Works
221(1)
3 Economic Statistical Design of SN & SR Control Charts
222(3)
4 Experiments
225(3)
5 Results Discussion
228(3)
6 Conclusions
231(21)
References
232(2)
Appendix
234(18)
10 Jamming Detection in Electromagnetic Communication with Machine Learning: A Survey and Perspective
252(20)
Jonathan Villain
Virginie Deniau
Christophe Gransart
1 Introduction
252(1)
2 Electromagnetic Waves Communication Jamming
253(3)
2.1 Susceptibility of the Physical Layer in Presence of a Jamming Signal
253(2)
2.2 Smart Jamming
255(1)
3 Difficulties and Challenges of Electromagnetic Waves Communication Anomaly Detection
256(2)
3.1 Detection on Physical Layers
256(1)
3.2 Smart Jamming Detection
256(1)
3.3 Transmission and Mobility
257(1)
3.4 Transmitter Location
257(1)
4 Machine Learning Techniques for Electromagnetic Waves Communication Anomaly Detection
258(4)
4.1 Classification Algorithms Specificities
258(2)
4.2 ML for Jamming Detection Algorithm for a TETRA Base Station Receiver
260(1)
4.3 ML for Jamming Detection in 5G Radio Communication
260(1)
4.4 ML for Jamming Detection in IoT Network
261(1)
4.5 More Applications of ML for Jamming
261(1)
5 A Case Study
262(5)
5.1 Preliminary Description of the Measurement Test Site
262(1)
5.2 Jamming Signals
263(1)
5.3 Device Setting
263(2)
5.4 Spectrum Analysis
265(1)
5.5 Learning and Result
266(1)
6 Conclusion
267(5)
References
269(3)
11 Intellectual Support with Machine Learning for Decision-making in Garment Manufacturing Industry: A Review
272(22)
Yanni Xu
Xiaofen Ji
1 Introduction
272(1)
2 Problems in Garment Manufacturing
273(3)
3 Garment Manufacturing using Machine Learning
276(1)
4 Popular Machine Learning Algorithms
277(3)
5 Potential Machine Learning Applications in Garment Manufacturing
280(4)
6 Case Study
284(3)
7 Conclusion
287(7)
References
288(6)
12 Enabling Smart Supply Chain Management with Artificial Intelligence
294(17)
Thi Hien Nguyen
Huu Du Nguyen
Kim Due Tran
Dinh Duy Kha Nguyen
Kim Phuc Tran
1 Introduction
294(2)
2 AI for Demand Forecasting
296(1)
3 AI for Logistics
297(1)
4 AI for Production
298(1)
5 AI for Decision Support Systems in SCM
299(2)
6 Blockchain Technique for SCM
301(2)
7 Case Study
303(1)
8 Conclusion
304(7)
References
305(6)
Index 311(6)
About the Editor 317
Kim Phuc Tran is an Associate Professor of Artificial Intelligence and Data Science at ENSAIT & GEMTEX, University of Lille, France, and a Senior Scientific Advisor at Dong A University, Vietnam. He obtained a Ph.D. in Automation and Applied Informatics at the University of Nantes, and an HDR (Dr. Habil.) in Computer Science and Automation at the University of Lille, France. His research focuses on Artificial Intelligence and applications. He has published more than 60 papers in SCIE peer-reviewed international journals and proceedings of international conferences. He edited 3 books with Springer Nature and CRC Press, Taylor & Francis Group.
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