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        Unlocking Artificial Intelligence

        From Theory to Applications

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        Contributor(s)
        Mutschler, Christopher (editor)
        Münzenmayer, Christian (editor)
        Uhlmann, Norman (editor)
        Martin, Alexander (editor)
        Language
        English
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        Abstract
        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.
        URI
        https://library.oapen.org/handle/20.500.12657/92701
        Keywords
        machine learning; time-series analysis; artificial intelligence; data annotation; logistics and transportation applications; healthcare informatics; geographical information systems
        DOI
        10.1007/978-3-031-64832-8
        ISBN
        9783031648328, 9783031648328, 9783031648311
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2024
        Imprint
        Springer Nature Switzerland
        Classification
        Machine learning
        Applied computing
        Pages
        380
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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