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001454518 020__ $$a9783031223716$$q(electronic bk.)
001454518 020__ $$a3031223713$$q(electronic bk.)
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001454518 020__ $$z9783031223709
001454518 0247_ $$a10.1007/978-3-031-22371-6$$2doi
001454518 035__ $$aSP(OCoLC)1369515603
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001454518 08204 $$a006.3/1$$223/eng/20230214
001454518 24500 $$aFusion of machine learning paradigms :$$btheory and applications /$$cIoannis K. Hatzilygeroudis, George A. Tsihrintzis, Lakhmi C. Jain, editors.
001454518 260__ $$aCham, Switzerland :$$bSpringer,$$c2023.
001454518 300__ $$a1 online resource
001454518 4901_ $$aIntelligent Systems Reference Library ;$$vv.236
001454518 504__ $$aIncludes bibliographical references.
001454518 5050_ $$aIntro -- Foreword -- References -- Preface -- Contents -- 1 Introduction to Fusion of Machine Learning Paradigms -- 1.1 Editorial -- References -- Part I Recent Application Areas of Fusion of Machine Learning Paradigms -- 2 Artificial Intelligence as Dual-Use Technology -- 2.1 Introduction -- 2.2 What Is DUT -- 2.3 AI: Concepts, Models and Technology -- 2.4 Agent-Based AI and Autonomous System -- 2.4.1 Basic Model of Agent-Based AI -- 2.4.2 Conceptual Model of Autonomous Weapon System -- 2.5 Dual-Use Technology and DARPA -- 2.5.1 Historical View and Role of DARPA
001454518 5058_ $$a2.5.2 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
001454518 5058_ $$a3.3.3 Data Augmentation Using Generative Adversarial Networks -- 3.4 Datasets of Eye Fundus Images -- 3.5 Transfer Learning Experiments -- 3.5.1 Dataset -- 3.5.2 Image Preprocessing -- 3.5.3 Image Augmentation -- 3.5.4 Deep Learning Experiments -- 3.5.5 Reinforcement Learning Experiments -- 3.6 Conclusion and Future Work -- References -- 4 A Novel Approach for Non-linear Deep Fuzzy Rule-Based Model and Its Applications in Biomedical Analyses -- 4.1 Introduction -- 4.2 Method -- 4.2.1 Preliminaries -- 4.2.2 Hierarchical Fuzzy Structure -- 4.2.3 Stacked Deep Fuzzy Rule-Based System (SD-FRBS)
001454518 5058_ $$a4.2.4 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
001454518 5058_ $$a5.2.2 Harmony Search -- 5.3 Methodology -- 5.3.1 Datasets -- 5.3.2 Experimental Setup -- 5.4 Experimental Results -- 5.5 Conclusions -- References -- 6 Toward Smart Energy Systems: The Case of Relevance Vector Regression Models in Hourly Solar Power Forecasting -- 6.1 Introduction -- 6.2 Relevance Vector Regression -- 6.3 RVR Based Day Ahead Forecasting -- 6.4 Results -- 6.5 Conclusion -- References -- 7 Domain-Integrated Machine Learning for IC Image Analysis -- 7.1 Introduction -- 7.2 Hierarchical Multi-classifier System -- 7.2.1 Architecture of Hierarchical Multi-classifier System
001454518 506__ $$aAccess limited to authorized users.
001454518 520__ $$aThis book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.
001454518 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 14, 2023).
001454518 650_0 $$aMachine learning.
001454518 655_0 $$aElectronic books.
001454518 7001_ $$aHatzilygeroudis, Ioannis.
001454518 7001_ $$aTsihrintzis, George A.
001454518 7001_ $$aJain, L. C.
001454518 77608 $$iPrint version: $$z3031223705$$z9783031223709$$w(OCoLC)1348922597
001454518 830_0 $$aIntelligent systems reference library ;$$vv. 236.
001454518 852__ $$bebk
001454518 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-22371-6$$zOnline Access$$91397441.1
001454518 909CO $$ooai:library.usi.edu:1454518$$pGLOBAL_SET
001454518 980__ $$aBIB
001454518 980__ $$aEBOOK
001454518 982__ $$aEbook
001454518 983__ $$aOnline
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