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Autor(en): 
  • Robert Stevens
  • Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Oktober 2023  
    Genre:  Schulbücher 
     
    Artificial Intelligence / Artificial Intelligence (AI) / Automatic control engineering / Calculus & mathematical analysis / Calculus and mathematical analysis / COMPUTERS / Data Science / Machine Learning / Engineering# general / Ergonomics
    ISBN:  9781032424385 
    EAN-Code: 
    9781032424385 
    Verlag:  Taylor and Francis 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 216 mm / B 138 mm / D  
    Gewicht:  260 gr 
    Seiten:  88 
    Illustration:  schwarz-weiss Illustrationen, Zeichnungen, schwarz-weiss, Tabellen, schwarz-weiss 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Nearly all our safety data collection and reporting systems are backwardlooking: incident reports; dashboards; compliance monitoring systems; and so on. This book shows how we can use safety data in a forward-looking, predictive sense. Predictive Safety Analytics: Reducing Risk through Modeling and Machine Learning contains real use cases where organizations have reduced incidents by employing predictive analytics to foresee and mitigate future risks. It discusses how Predictive Safety Analytics is an opportunity to break through the plateau problem where safety rate improvements have stagnated in many organizations. The book presents how the use of data, coupled with advanced analytical techniques, including machine learning, has become a proven and successful innovation. Emphasis is placed on how the book can "meet you where you are" by illuminating a path to get there, starting with simple data the organization likely already has. Highlights of the book are the real examples and case studies that will assist in generating thoughts and ideas for what might work for individual readers and how they can adapt the information to their particular situations. This book is written for professionals and researchers in system reliability, risk and safety assessment, quality control, operational managers in selected industries, data scientists, and ML engineers. Students taking courses in these areas will also find this book of interest to them.

      



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