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Fusion of Machine Learning Paradigms: Theory and Applications

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This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms.

 

This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems.

 

It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.


Author(s): Ioannis K. Hatzilygeroudis, George A. Tsihrintzis, Lakhmi C. Jain
Series: Intelligent Systems Reference Library, 236
Publisher: Springer
Year: 2023

Language: English
Pages: 203
City: Cham

Foreword
References
Preface
Contents
1 Introduction to Fusion of Machine Learning Paradigms
1.1 Editorial
References
Part I Recent Application Areas of Fusion of Machine Learning Paradigms
2 Artificial Intelligence as Dual-Use Technology
2.1 Introduction
2.2 What Is DUT
2.3 AI: Concepts, Models and Technology
2.4 Agent-Based AI and Autonomous System
2.4.1 Basic Model of Agent-Based AI
2.4.2 Conceptual Model of Autonomous Weapon System
2.5 Dual-Use Technology and DARPA
2.5.1 Historical View and Role of DARPA
2.5.2 DARPA’s Contribution to DUT R&D on AI
2.6 DARPA-Like Organizations in Major Countries
2.7 Dual-Use Dilemma
2.8 Concluding Remarks
References
3 Diabetic Retinopathy Detection Using Transfer and Reinforcement Learning with Effective Image Preprocessing and Data Augmentation Techniques
3.1 Introduction
3.2 Background
3.2.1 Deep Learning for Diabetic Retinopathy
3.2.2 Image Preprocessing Techniques
3.2.3 Reinforcement Learning and Deep Learning
3.3 Data Augmentation Techniques
3.3.1 Traditional Data Augmentation
3.3.2 SMOTE-Based Data Augmentation
3.3.3 Data Augmentation Using Generative Adversarial Networks
3.4 Datasets of Eye Fundus Images
3.5 Transfer Learning Experiments
3.5.1 Dataset
3.5.2 Image Preprocessing
3.5.3 Image Augmentation
3.5.4 Deep Learning Experiments
3.5.5 Reinforcement Learning Experiments
3.6 Conclusion and Future Work
References
4 A Novel Approach for Non-linear Deep Fuzzy Rule-Based Model and Its Applications in Biomedical Analyses
4.1 Introduction
4.2 Method
4.2.1 Preliminaries
4.2.2 Hierarchical Fuzzy Structure
4.2.3 Stacked Deep Fuzzy Rule-Based System (SD-FRBS)
4.2.4 Adaptation of the First-Order TSK Structure in SD-FRBS
4.2.5 Concatenated Deep Fuzzy Rule-Based System (CD-FRBS)
4.3 Data Description and Results
4.3.1 MIMIC-III Dataset
4.3.2 SD-FRBS as a Multivariate Regressor for Granger Causality Estimation—In EEG Connectivity Index Extraction
4.3.3 CD-FRBS in Staging Depression Severity
4.4 Discussion and Conclusion
4.4.1 Suggested Future Works
References
5 Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks
5.1 Introduction
5.2 Theoretical Background
5.2.1 Deep Belief Networks
5.2.2 Harmony Search
5.3 Methodology
5.3.1 Datasets
5.3.2 Experimental Setup
5.4 Experimental Results
5.5 Conclusions
References
6 Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting
6.1 Introduction
6.2 Relevance Vector Regression
6.3 RVR Based Day Ahead Forecasting
6.4 Results
6.5 Conclusion
References
7 Domain-Integrated Machine Learning for IC Image Analysis
7.1 Introduction
7.2 Hierarchical Multi-classifier System
7.2.1 Architecture of Hierarchical Multi-classifier System
7.2.2 Result and Discussion on Case Study
7.3 Deep Learning with Pseudo Labels
7.3.1 Methodology
7.3.2 Application to IC Image Analysis
7.4 Conclusions and Future Works
References
Part II Applications that Can Clearly Benefit from Fusion of Machine Learning Paradigms
8 Fleshing Out Learning Analytics and Educational Data Mining with Data and ML Pipelines
8.1 Introduction
8.2 Data and ML Pipelines
8.3 Related Work
8.4 An Automated EDM and LA Methodology
8.4.1 A Data Pipeline Scenario
8.4.2 An ML Pipeline Scenario
8.5 Experiments and Results
8.6 Conclusions and Future Work
References
9 Neural Networks Based Throughput Estimation of Short Production Lines Without Intermediate Buffers
9.1 Introduction
9.2 Data Sets of i-Stage Production Line Problems
9.3 Deep Learning and Multilayer Perceptron
9.4 Experimental Process of Deep Learning Approach
9.5 Results of Deep Learning Approach
9.6 Conclusions
References