Unlocking Artificial Intelligence
Subtitle: From Theory to Applications
Publisher: Springer Nature
Published: 2024
Place of publication: Switzerland
Language: eng
Translated from: swe
Format: Ebook
Number of pages: 380
ISBN-13: 9783031648328
Copyright date: 2024
Description: 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.
Table of contents: title: Theory; title: Front Matter; title: Automated Machine Learning; title: Sequence-based Learning; title: Learning from Experience; title: Learning with Limited Labelled Data; title: The Role of Uncertainty Quantification for Trustworthy AI; title: Process-aware Learning; title: Combinatorial Optimization; title: Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications; title: Applications; title: Front Matter; title: Assured Resilience in Autonomous Systems – Machine Learning Methods for Reliable Perception; title: Data-driven Wireless Positioning; title: Comprehensible AI for Multimodal State Detection; title: Robust and Adaptive AI for Digital Pathology; title: Safe and Reliable AI for Autonomous Systems; title: AI for Stability Optimization in Low Voltage Direct Current Microgrids; title: Self-Optimization in Adaptive Logistics Networks; title: Optimization of Underground Train Systems; title: AI-assisted Condition Monitoring and Failure Analysis for Industrial Wireless Systems; title: XXL-CT Dataset Segmentation; title: Energy-Efficient AI on the Edge