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Unlocking artificial intelligence = ...

Mutschler, Christopher.

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Unlocking artificial intelligence = from theory to applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Unlocking artificial intelligence/ edited by Christopher Mutschler ... [et al.].
其他題名:	from theory to applications /
其他作者:	Mutschler, Christopher.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xvi, 380 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-031-64832-8
ISBN:	9783031648328

Unlocking artificial intelligence = from theory to applications /
Unlocking artificial intelligencefrom theory to applications /[electronic resource] :edited by Christopher Mutschler ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xvi, 380 p. :ill., digital ;24 cm.

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.

Open access.

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.

ISBN: 9783031648328

Standard No.: 10.1007/978-3-031-64832-8doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .U55 2024

Dewey Class. No.: 006.31

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506 $a Open access.
520 $a 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.
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