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Herausgeber: 
  • Saikat Gochhait
  • Ashish Khanna
  • Green AI-Powered Intelligent Systems for Disease Prognosis 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  August 2024  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Artificial Intelligence (AI) / Bio-Neuro Informatics / Biomedical Computing / Clinical decision support / computational modeling / Data Science / Disease prognosis / Eco-Conscious Technology
    ISBN:  9798369312438 
    EAN-Code: 
    9798369312438 
    Verlag:  IGI Global 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 260 mm / B 183 mm / D 27 mm 
    Gewicht:  988 gr 
    Seiten:  424 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Experts in Medicine are under new pressures of advancing their studies while also reducing the impact they leave on the environment. Researchers within the fields of bio-neuro informatics, healthcare, engineering, and medical sciences require a dynamic platform that bridges the realms of academia, science, industry, and innovation. Green AI-Powered Intelligent Systems for Disease Prognosis facilitates a crossroads for a diverse audience interested in these two seldom coalesced concepts. Academicians, scientists, researchers, professionals, decision-makers, and even aspiring scholars all find a space to contribute, collaborate, and learn within the platform that this book provides. The book's thematic coverage is unequivocally compelling; by exploring the intersections of bio-neuro informatics, healthcare, engineering, and medical sciences, it captures the spirit of interdisciplinary research. It delves into well-established domains while also casting a spotlight on emerging trends that have the potential to reshape our understanding of these fields. Two prominent tracks form the backbone of the book's content. The first covers the Bioinformatics and Data Mining of Biological Data (BiDMBD), and unravels the intricacies of biomedical computation, signal analysis, clinical decision support, and health data mining. This approach holds a treasure trove of insights into the mechanisms of health data acquisition, clinical informatics, and the representation of healthcare knowledge. The second covers Biomedical Informatics and is a symposium of computational modeling, genomics, and proteomics. Here, the fusion of data science with medical sciences takes center stage.

      



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