首页|Findings on Machine Learning Discussed by Investigators at Sapienza University of Rome (A Systematic Approach To Develop Safety-related Undesired Event Databases for Machine Learning Analyses: Application To Confined Space Incidents)
Findings on Machine Learning Discussed by Investigators at Sapienza University of Rome (A Systematic Approach To Develop Safety-related Undesired Event Databases for Machine Learning Analyses: Application To Confined Space Incidents)
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原文链接
NETL
NSTL
Elsevier
Investigators publish new report on Machine Learning. According to news reporting originating in Rome, Italy, by NewsRx journalists, research stated, “In Occupational Safety and Health (OSH) and operational safety, confined spaces are high-risk working areas, where frequent serious and fatal incidents occur. However, there is a limited use of data-driven approaches based on Machine Learning (ML) techniques for learning from such incidents.”
RomeItalyEuropeCyborgsEmerging TechnologiesMachine LearningSapienza University of Rome
NETL
NSTL
Elsevier
