Unlocking Artificial Intelligence : From Theory to Applications / edited by Christopher Mutschler, Christian Münzenmayer, Norman Uhlmann, Alexander Martin.

Format:

Book

Author/Creator:

Mutschler, Christopher.

Contributor:

Münzenmayer, Christian.

Uhlmann, Norman.

Martin, Alexander.

Series:

Computer Science Series

Language:

English

Subjects (All):

Machine learning.

Application software.

Machine Learning.

Computer and Information Systems Applications.

Local Subjects:

Machine Learning.

Computer and Information Systems Applications.

Physical Description:

1 online resource (382 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

ISBN:

3-031-64832-3

OCLC:

1450832775