Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource

ISBN: 9783031648328

Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-64831-1

Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-64834-2

Language: English

DOI: 10.1007/978-3-031-64832-8

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

Author information: Münzenmayer, Christian 1976-

UID:

Format: 1 online resource (382 pages)

Edition: 1st ed. 2024.

ISBN: 3-031-64832-3

Content: This open access book provides a state-of-the-art overview of current machine learning research and its exploitation in various application areas. It has become apparent that the deep integration of artificial intelligence (AI) methods in products and services is essential for companies to stay competitive. The use of AI allows large volumes of data to be analyzed, patterns and trends to be identified, and well-founded decisions to be made on an informative basis. It also enables the optimization of workflows, the automation of processes and the development of new services, thus creating potential for new business models and significant competitive advantages. The book is divided in two main parts: First, in a theoretically oriented part, various AI/ML-related approaches like automated machine learning, sequence-based learning, deep learning, learning from experience and data, and process-aware learning are explained. In a second part, various applications are presented that benefit from the exploitation of recent research results. These include autonomous systems, indoor localization, medical applications, energy supply and networks, logistics networks, traffic control, image processing, and IoT applications. Overall, the book offers professionals and applied researchers an excellent overview of current exploitations, approaches, and challenges of AI/ML-related research.

Note: Part I: Theory -- 1. Automated Machine Learning -- 2. Sequence-Based Learning -- 3. Learning from Experience -- 4. Learning with Limited Labelled Data -- 5. The Role of Uncertainty Quantification -- 6. Process-Aware Learning -- 7. Combinatorial Optimization -- 8. Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications -- Part II: Applications -- 9. Assured Resilience in Autonomous Systems -- 10. Data-driven Wireless Positioning -- 11. Comprehensible AI for Multimodal State Detection -- 12. Robust and Adaptive AI for Digital Pathology -- 13. Safe and Reliable AI for Autonomous Systems -- 14. AI for Stability Optimization in Low Voltage Direct Current Microgrids -- 15. Self-optimization in Adaptive Logistics Networks -- 16. Opitmization of Undergroud Train Systems -- 17. AI-assisted Condition Monitoring and Failure Analysis for Industrial Wireless Systems -- 18. XXL-CT Dataset Segmentation -- 19. Energy-efficient AI on the Edge.

Additional Edition: ISBN 3-031-64831-5

Language: English

DOI: 10.1007/978-3-031-64832-8

UID:

Format: 1 online resource (382 pages)

Edition: 1st ed.

ISBN: 9783031648328

Note: Intro -- Preface -- Acknowledgements -- Contents -- Part I Theory -- Chapter 1 Automated Machine Learning -- 1.1 Introduction -- 1.2 Components of AutoML Systems -- 1.2.1 Search Space -- 1.2.2 Optimization -- 1.2.3 Ensembling -- 1.2.4 Feature Selection and Engineering -- 1.2.5 Meta-Learning -- 1.2.6 A Brief Note on AutoML in the Wild -- 1.3 Selected Topics in AutoML -- 1.3.1 AutoML for Time Series Data -- 1.3.2 Unsupervised AutoML -- 1.3.3 AutoML Beyond a Single Objective -- 1.3.4 Human-In-The-Loop AutoML -- 1.4 Neural Architecture Search -- 1.4.1 A Brief Overview of the Current State of NAS -- 1.4.2 Hardware-aware NAS -- 1.5 Conclusion and Outlook -- References -- Chapter 2 Sequence-based Learning -- 2.1 Introduction -- 2.2 Time Series Processing -- 2.2.1 Time Series Data Streams -- 2.2.2 Pre-Processing -- 2.2.3 Predictive Modelling -- 2.2.4 Post-Processing -- 2.3 Methods -- 2.3.1 Temporal Convolutional Networks -- 2.3.2 Recurrent Neural Networks -- 2.3.3 Transformer -- 2.4 Perspectives -- 2.4.1 Time Series Similarity -- 2.4.1.1 Deep Metric Learning -- 2.4.2 Transfer Learning & -- Domain Adaptation -- 2.4.3 Model Interpretability -- 2.4.3.1 Interpretability for Time Series -- 2.4.3.2 Trusting Interpretations -- 2.5 Conclusion and Outlook -- Acknowledgments -- References -- Chapter 3 Learning from Experience -- 3.1 Introduction -- 3.2 Concepts of Reinforcement Learning -- 3.2.1 Markov Decision Processes (MDPs) -- 3.2.2 Dynamic Programming -- 3.2.3 Model-free Reinforcement Learning -- 3.2.4 General Remarks -- 3.3 Learning purely through Interaction -- 3.3.1 Exploration-Exploitation -- 3.3.1.1 Exploration Strategies -- 3.3.1.2 Exploration in Deep RL -- 3.4 Learning with Data or Knowledge -- 3.4.1 Model-based RL with continuous Actions -- 3.4.2 MBRL with Discrete Actions: Monte Carlo Tree Search -- 3.4.3 Offline Reinforcement Learning. , 3.4.4 Hierarchical RL -- 3.5 Challenges for Agent Deployment -- 3.5.1 Safety through Policy Constraints -- 3.5.2 Generalizability of Policies -- 3.5.3 Lack of a Reward Function -- 3.6 Conclusion and Outlook -- References -- Chapter 4 Learning with Limited Labelled Data -- 4.1 Introduction -- 4.2 Semi-Supervised Learning -- 4.2.1 Classical Semi-Supervised Learning -- 4.2.2 Deep Semi-Supervised Learning -- 4.2.2.1 Self-training -- 4.2.2.2 Unsupervised Regularization -- 4.2.3 Self-Training and Consistency Regularization -- 4.3 Active Learning -- 4.3.1 Deep Active Learning (DAL) -- 4.3.2 Uncertainty Sampling -- 4.3.3 Diversity Sampling -- 4.3.4 Balanced Criteria -- 4.4 Active Semi-Supervised Learning -- 4.4.1 How can SSL and ALWork Together? -- 4.4.2 Are SSL and AL Always Mutually Beneficial? -- 4.5 Conclusion and Outlook -- References -- Chapter 5 The Role of Uncertainty Quantification for Trustworthy AI -- 5.1 Introduction -- 5.2 Towards Trustworthy AI -- 5.2.1 The EU AI Act -- 5.2.2 From Uncertainty to Trustworthy AI -- 5.3 Uncertainty Quantification -- 5.3.1 Sources of Uncertainty -- 5.3.1.1 Aleatoric Uncertainty -- 5.3.1.2 Epistemic Uncertainty -- 5.3.2 Methods for Quantification of Uncertainty and Calibration -- 5.3.2.1 Data-based Methods -- 5.3.2.2 Architecture-Modifying Methods -- 5.3.2.3 Post-Hoc Methods -- 5.3.3 Evaluation Metrics for Uncertainty Estimation -- 5.3.3.1 Negative Log-Likelihood -- x -- 5.3.3.2 Expected Calibration Error -- 5.3.3.3 Rejection-based Measures -- 5.4 Conclusion and Outlook -- References -- Chapter 6 Process-aware Learning -- 6.1 Introduction -- 6.2 Overview of Process Mining -- 6.2.1 Process Mining Basic Concept -- 6.2.2 Process Mining Types -- 6.2.2.1 Process Discovery -- 6.2.2.2 Conformance Checking -- 6.2.2.3 Model Enhancement -- 6.2.3 Event Log -- 6.2.4 Four Quality Criteria -- 6.2.5 Types of Processes. , 6.2.5.1 Lasagna Processes -- 6.2.5.2 Spaghetti Processes -- 6.3 Process-Awareness from Theory to Practice -- 6.3.1 Predictive Analysis in Process Mining -- 6.3.2 Predictive Process Mining with Bayesian Statistics -- 6.3.2.1 Preliminaries for Bayesian Modeling -- 6.3.2.2 Quality Criteria for Bayesian Modeling -- 6.3.2.3 Context-Aware Structure Learning for Probabilistic Process Prediction -- 6.3.3 Process AI -- 6.4 Conclusion and Outlook -- References -- Chapter 7 Combinatorial Optimization -- 7.1 Introduction -- 7.2 Solving Methods -- 7.2.1 Heuristics -- 7.2.2 Exact Methods -- 7.3 Modeling Techniques -- 7.3.1 Graph Theory -- 7.3.1.1 Clique Problems -- 7.3.1.2 Flow Models -- 7.3.2 Mixed Integer Programs and Connections to Machine Learning -- 7.3.2.1 Modeling Logic -- 7.3.2.2 Binary Decision Trees -- 7.3.3 Pooling -- 7.4 Conclusion and Outlook -- References -- Chapter 8 Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications -- 8.1 Introduction -- 8.2 Approaches to Acquire Semantics -- 8.2.1 Manual Annotation and Labeling -- 8.2.2 Data Augmentation Techniques -- 8.2.3 Simulation and Generation -- 8.2.3.1 Physical Modeling -- 8.2.3.2 Generative Adversarial Networks -- 8.2.4 High-End Reference Sensors -- 8.2.5 Active Learning -- 8.2.6 Knowledge Modeling Using Semantic Networks -- 8.2.7 Discussion -- 8.3 Conclusion and Outlook -- References -- Part II Applications -- Chapter 9 Assured Resilience in Autonomous Systems - Machine Learning Methods for Reliable Perception -- 9.1 Introduction -- 9.1.1 The Perception Challenge -- 9.2 Approaches to reliable perception -- 9.2.1 Choice of Dataset -- 9.2.2 Unexpected Behavior of ML Methods -- 9.2.3 Reliable Object Detection for Autonomous Driving -- 9.2.4 Uncertainty Quantification for Image Classification -- 9.2.5 Ensemble Distribution Distillation for 2D Object Detection. , 9.2.6 Robust Object Detection in Simulated Driving Environments -- 9.2.6.1 Scenarios Setup -- 9.2.6.2 Methods and Metrics -- 9.2.6.3 Results -- 9.2.7 Out-of-Distribution Detection -- 9.3 Conclusion and Outlook -- References -- Chapter 10 Data-driven Wireless Positioning -- 10.1 Introduction -- 10.2 AI-Assisted Localization -- 10.3 Direct Positioning -- 10.3.1 Model -- 10.3.2 Experimental Setup -- 10.3.2.1 Measurement Campaign -- 10.3.2.2 Environments -- 10.3.3 Evaluation -- 10.3.4 Hybrid Localization -- 10.3.5 Zone Identification -- 10.3.6 Experimental Setup -- 10.3.7 Environments -- 10.3.8 Evaluation -- 10.4 Conclusion and Outlook -- Acknowledgements -- References -- Chapter 11 Comprehensible AI for Multimodal State Detection -- 11.1 Introduction -- 11.1.1 Cognitive Load Estimation -- 11.1.2 Challenges in Affective Computing -- 11.2 Data Collection -- 11.2.1 Annotation -- 11.2.2 Data Preprocessing -- 11.3 Modeling -- 11.3.1 In-Domain Evaluation -- 11.3.2 Cross-Domain Evaluation -- 11.3.3 Interpretability -- 11.3.4 Improving ECG Representation Learning -- 11.3.5 Deployment and Application -- 11.4 Conclusion and Outlook -- References -- Chapter 12 Robust and Adaptive AI for Digital Pathology -- 12.1 Introduction -- 12.2 Applications: Tumor Detection and Tumor-Stroma Assessment -- 12.2.1 Generation of Labeled Data Sets -- 12.2.2 Data Sets for Tumor Detection -- 12.2.2.1 Primary Data Set -- 12.2.2.2 Multi-Scanner Dataset -- 12.2.2.3 Multi-Center Dataset -- 12.2.2.4 Out-of-Distribution Data Set -- 12.2.2.5 Urothelial Data Sets -- 12.2.3 Data Set for Tumor-Stroma Assessment -- 12.3 Prototypical Few-Shot Classification -- 12.3.1 Robustness through Data Augmentation -- 12.3.1.1 Evaluation on the Multi-Scanner Data Set -- 12.3.1.2 Evaluation on the Multi-Center Data Set -- 12.3.2 Out-of-Distribution Detection. , 12.3.3 Adaptation to Urothelial Tumor Detection -- 12.3.4 Interactive AI Authoring with MIKAIA® -- 12.4 Prototypical Few-Shot Segmentation -- 12.4.1 Tumor-Stroma Assessment -- 12.5 Conclusion and Outlook -- Acknowledgements -- References -- Chapter 13 Safe and Reliable AI for Autonomous Systems -- 13.1 Introduction -- 13.1.1 Reinforcement Learning -- 13.1.2 Reinforcement Learning for Autonomous Driving -- 13.2 Generating Environments with Driver Dojo -- 13.2.1 Method -- 13.3 Training safe Policies with SafeDQN -- 13.3.1 Method -- 13.3.2 Evaluation -- 13.4 Extracting tree policies with SafeVIPER -- 13.4.1 Training the Policy -- 13.4.2 Verification of Decision Trees -- 13.4.3 Evaluation -- 13.5 Conclusion and Outlook -- References -- Chapter 14 AI for Stability Optimization in Low Voltage Direct Current Microgrids -- 14.1 Introduction -- 14.2 Low Voltage DC Microgrids -- 14.2.1 Control of Low Voltage DC Microgrids -- 14.2.2 Stability of Low Voltage DC Microgrids -- 14.3 AI-based Stability Optimization for Low Voltage DC Microgrids -- 14.3.1 Overview -- 14.3.2 Digital Network Twin and Generation of Labels to Describe the Stability State -- 14.3.3 LVDC Microgrid Surrogate Model Applying Random Forests -- 14.3.4 Stability Optimization Applying Decision Trees -- 14.4 Implementation and Assessment -- 14.4.1 Measurement of Grid Stability -- 14.4.2 Experimental Validation -- 14.5 Conclusion and Outlook -- References -- Chapter 15 Self-Optimization in Adaptive Logistics Networks -- 15.1 Introduction -- 15.2 A Brief Overview of Relevant Literature on Predicting the All-Time Buy Quantity -- 15.3 Predicting the All-Time Buy -- 15.4 A Probabilistic Hierarchical Growth Curve model -- 15.5 Determining the Optimal Order Policy -- 15.5.1 Modeling Non-Linear Costs -- 15.5.2 Robust Optimization -- 15.6 Pooling -- 15.7 Conclusion and Outlook -- References. , Chapter 16 Optimization of Underground Train Systems.

Additional Edition: Print version: Mutschler, Christopher Unlocking Artificial Intelligence Cham : Springer International Publishing AG,c2024 ISBN 9783031648311

Language: English

Keywords: Electronic books. ; Electronic books.

URL: Click to View

URL: SpringerLink

URL: Click here to view book

UID:

Format: XVI, 380 p. , online resource.

Edition: 1st ed. 2024.

ISBN: 9783031648328

Content: This open access book provides a state-of-the-art overview of current machine learning research and its exploitation in various application areas. It has become apparent that the deep integration of artificial intelligence (AI) methods in products and services is essential for companies to stay competitive. The use of AI allows large volumes of data to be analyzed, patterns and trends to be identified, and well-founded decisions to be made on an informative basis. It also enables the optimization of workflows, the automation of processes and the development of new services, thus creating potential for new business models and significant competitive advantages. The book is divided in two main parts: First, in a theoretically oriented part, various AI/ML-related approaches like automated machine learning, sequence-based learning, deep learning, learning from experience and data, and process-aware learning are explained. In a second part, various applications are presented that benefit from the exploitation of recent research results. These include autonomous systems, indoor localization, medical applications, energy supply and networks, logistics networks, traffic control, image processing, and IoT applications. Overall, the book offers professionals and applied researchers an excellent overview of current exploitations, approaches, and challenges of AI/ML-related research.

Note: Part I: Theory -- 1. Automated Machine Learning -- 2. Sequence-Based Learning -- 3. Learning from Experience -- 4. Learning with Limited Labelled Data -- 5. The Role of Uncertainty Quantification -- 6. Process-Aware Learning -- 7. Combinatorial Optimization -- 8. Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications -- Part II: Applications -- 9. Assured Resilience in Autonomous Systems -- 10. Data-driven Wireless Positioning -- 11. Comprehensible AI for Multimodal State Detection -- 12. Robust and Adaptive AI for Digital Pathology -- 13. Safe and Reliable AI for Autonomous Systems -- 14. AI for Stability Optimization in Low Voltage Direct Current Microgrids -- 15. Self-optimization in Adaptive Logistics Networks -- 16. Opitmization of Undergroud Train Systems -- 17. AI-assisted Condition Monitoring and Failure Analysis for Industrial Wireless Systems -- 18. XXL-CT Dataset Segmentation -- 19. Energy-efficient AI on the Edge.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031648311

Additional Edition: Printed edition: ISBN 9783031648335

Additional Edition: Printed edition: ISBN 9783031648342

Language: English

DOI: 10.1007/978-3-031-64832-8

URL: https://doi.org/10.1007/978-3-031-64832-8