NLU Meghalaya Library

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Artificial intelligence in highway safety [electronic resource] / Subasish Das.

By: Material type: TextPublisher: Boca Raton, FL : CRC Press, 2022Copyright date: ©2022Edition: First editionDescription: 1 online resourceISBN:
  • 9781000726268
  • 1000726266
  • 9781003005599
  • 1003005594
  • 9781000726282
  • 1000726282
Subject(s): DDC classification:
  • 363.12/5 23/eng/20220325
LOC classification:
  • HE5614
Online resources: Summary: Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend. Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.
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Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend. Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.

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