Machine Learning-based Hazard Screening

Machine Learning-Based Hazard Screening is a systematic method within hazard screening that employs machine learning algorithms to automatically identify, classify, or prioritize potential hazards by analyzing patterns and anomalies in data, distinguishing it from traditional rule-based or manual screening approaches through its adaptive, data-driven modeling capabilities.

15

Publications

675

Citations

62

Authors

34

Institutions

Publications per year

2017–2026

12

Authors

62

  1. JT
    Jack Teitel

    University of Rochester, United States

    2 publications · 94 citations

    2

    h-index

  2. BL
    Brian Labus

    University of Nevada, Las Vegas, United States

    2 publications · 94 citations

    2

    h-index

  3. AS
    Adam Sadilek

    Google (United States), United States

    2 publications · 94 citations

    2

    h-index

  4. HK
    Henry Kautz

    University of Rochester, United States

    2 publications · 94 citations

    2

    h-index

  5. VS
    Vincent Silenzio

    University of Rochester, United States

    2 publications · 94 citations

    2

    h-index

1–5 of 62

Venues

  1. The Science of The Total Environment

    1 publications · 24 citations

    1

    h-index

  2. Journal of Hazardous Materials

    1 publications · 29 citations

    1

    h-index

  3. Journal of Cleaner Production

    1 publications · 120 citations

    1

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 37 citations

    1

    h-index

  5. Eos

    1 publications · 46 citations

    1

    h-index

1–5