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Predictive safety analytics : = reducing risk through modeling and machine learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Predictive safety analytics :/ Robert Stevens.
其他題名:	reducing risk through modeling and machine learning /
作者:	Stevens, Robert.
出版者:	Boca Raton :CRC Press, : c2024.,
面頁冊數:	ix, 88 p. :ill. ; : 23 cm.;
附註:	"CRC focus"--Cover.
標題:	Predictive analytics. -
ISBN:	9781032424385 :

Predictive safety analytics : = reducing risk through modeling and machine learning /
Stevens, Robert.

Predictive safety analytics :reducing risk through modeling and machine learning /Robert Stevens. - 1st ed. - Boca Raton :CRC Press,c2024. - ix, 88 p. :ill. ;23 cm. - Reliability, maintenance, and safety engineering: a practical field view on getting work done effectively.

"CRC focus"--Cover.

Includes bibliographical references (p. 85) and index.

Safety in numbers : a data-driven approach -- Analytics defined -- The safety data repository -- Use cases -- Where to go from here.

"Nearly all our safety data collection and reporting systems are backward-looking: 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. A highlight of the book is 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. The 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"--

ISBN: 9781032424385 :NT1954

LCCN: 2023027701Subjects--Topical Terms:

1420732
Predictive analytics.

LC Class. No.: T55.3.S72 / S74 2024

Dewey Class. No.: 363.11

Predictive safety analytics : = reducing risk through modeling and machine learning /
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490 0 $a Reliability, maintenance, and safety engineering: a practical field view on getting work done effectively
500 $a "CRC focus"--Cover.
504 $a Includes bibliographical references (p. 85) and index.
505 0 # $a Safety in numbers : a data-driven approach -- Analytics defined -- The safety data repository -- Use cases -- Where to go from here.
520 # $a "Nearly all our safety data collection and reporting systems are backward-looking: 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. A highlight of the book is 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. The 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"-- $c Provided by publisher.
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