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
Parents
15
Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media
Adam Sadilek, Henry Kautz, Lauren DiPrete et al. · AI Magazine · 2017 · 57 citations · Full text
Saer Samanipour, Sarit Kaserzon, Soumini Vijayasarathy et al. · Talanta · 2018 · 49 citations · Full text
1–5 of 15
62
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2
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2
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2
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2
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1–5 of 62
34

University of Rochester
Rochester, United States
2 h-index
9 publications
413 citations
Southern Nevada Health District
Las Vegas, United States
2 h-index
4 publications
188 citations

University of Genoa
Genoa, Italy
1 h-index
5 publications
220 citations
University of Northern British Columbia
Prince George, Canada
1 h-index
1 publications
29 citations
Institute for Conflict Research
Belfast, United Kingdom
1 h-index
3 publications
144 citations

South Dakota School of Mines and Technology
Rapid City, United States
1 h-index
3 publications
72 citations
National Research Council
Rome, Italy
1 h-index
2 publications
42 citations

Manisa Celal Bayar University
Manisa, Türkiye
1 h-index
1 publications
39 citations
1–8 of 34
1 publications · 24 citations
1
h-index
1 publications · 29 citations
1
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1 publications · 120 citations
1
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Conference · 1 publications · 37 citations
1
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1 publications · 46 citations
1
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1–5