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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://doab-dev.siscern.org/handle/20.500.12854/166950
            Keywords
            machine learning; time-series analysis; artificial intelligence; data annotation; logistics and transportation applications; healthcare informatics; geographical information systems; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning; thema EDItEUR::U Computing and Information Technology::UX Applied computing
            DOI
            10.1007/978-3-031-64832-8
            ISBN
            9783031648328, 9783031648311
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            Cham, 2024
            Imprint
            Springer Nature Switzerland
            Pages
            380
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            Credits


            • logo Investir l'avenirInvestir l'avenir
            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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