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Herausgeber: 
  • Jürgen Franke
  • Tanja Manuela Kneiske
  • Manja Mai-Ly Pfaff-Kastner
  • Isabel Michel
  • Claudia Kirch
  • Machine Learning in Industry and Science 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Vorankündigung
    Veröffentlichung:  ANGEKÜNDIGT (Februar 2027)  
    Genre:  EDV / Informatik 
     
    Applied science / data analysis / Data Mining / Data Mining and Knowledge Discovery / Deep Learning / Explainable AI / Fraunhofer / Healthcare
    ISBN:  9783032395795 
    EAN-Code: 
    9783032395795 
    Verlag:  Springer International Publishing 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Seiten:  320 
    Illustration:  Approx. 400 p. 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Keine Bewertung vor Veröffentlichung möglich.
    Inhalt:
    Applied Machine Learning is transforming industries and public services across Europe. This book offers a unique insight into the field, bringing together work from researchers within the Fraunhofer network - one of the leading organizations for applied research and innovation.
    Covering cutting-edge approaches to data mining, complex spatial data analysis, and knowledge modeling, the text reflects both theoretical advances and practical implementations. The contributions span a wide range of real-world domains, including healthcare, manufacturing, energy systems, and public administration. A particular focus is also placed on critical cross-cutting issues such as algorithmic transparency and the interpretability of AI systems.
    What sets this volume apart is its emphasis on the strong connection between research excellence and application relevance. Drawing from the Fraunhofer ecosystem, it showcases how Machine Learning is shaping next-generation technologies and decision-making processes in both industry and public services.
    This open access book is an essential resource for researchers, data scientists, AI practitioners, and policy stakeholders seeking insight into the current state and future direction of applied Machine Learning in Europe.

      



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