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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
    Show full item record
    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, 9783031648311, 9783031648328
    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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