Unlocking Artificial Intelligence

From Theory to Applications

Unlocking Artificial Intelligence
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Last edited by Tauriel063
January 4, 2025 | History

Unlocking Artificial Intelligence

From Theory to Applications

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.

Publish Date
Publisher
Springer Nature
Pages
380

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Book Details


Table of Contents

Theory
Front Matter
Automated Machine Learning
Sequence-based Learning
Learning from Experience
Learning with Limited Labelled Data
The Role of Uncertainty Quantification for Trustworthy AI
Process-aware Learning
Combinatorial Optimization
Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications
Applications
Front Matter
Assured Resilience in Autonomous Systems – Machine Learning Methods for Reliable Perception
Data-driven Wireless Positioning
Comprehensible AI for Multimodal State Detection
Robust and Adaptive AI for Digital Pathology
Safe and Reliable AI for Autonomous Systems
AI for Stability Optimization in Low Voltage Direct Current Microgrids
Self-Optimization in Adaptive Logistics Networks
Optimization of Underground Train Systems
AI-assisted Condition Monitoring and Failure Analysis for Industrial Wireless Systems
XXL-CT Dataset Segmentation
Energy-Efficient AI on the Edge

Edition Notes

Published in
Switzerland
Copyright Date
2024

Contributors

Editor
Christopher Mutschler
Editor
Christian Münzenmayer
Editor
Norman Uhlmann
Editor
Alexander Martin

The Physical Object

Format
Ebook
Pagination
XVI, 380
Number of pages
380

ID Numbers

Open Library
OL57407359M
ISBN 13
9783031648328

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History

Download catalog record: RDF / JSON / OPDS | Wikipedia citation
January 4, 2025 Edited by Tauriel063 Edited without comment.
January 4, 2025 Created by Tauriel063 Added new book.