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Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications
https://orcid.org/0000-0002-2707-0346
https://orcid.org/0000-0002-6411-984X
Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that can automatically learn from data and can perform tasks such as predictions and decision-making. Interdisciplinary studies combining ML/DL with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this Review, commonly used ML/DL tools and concepts as well as popular ML/DL algorithms are introduced and discussed. More than 100 papers have been categorized and summarized to present the current development of ML/DL-based research in the area of chemical health and safety. In addition, the limitation of current studies and prospects of ML/DL-based study are also discussed. This Review can serve as useful guidance for researchers who are interested in implementing ML/DL into chemical health and safety research and for readers who try to learn more information about novel ML/DL techniques and applications.
Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications
https://orcid.org/0000-0002-2707-0346
https://orcid.org/0000-0002-6411-984X
Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that can automatically learn from data and can perform tasks such as predictions and decision-making. Interdisciplinary studies combining ML/DL with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this Review, commonly used ML/DL tools and concepts as well as popular ML/DL algorithms are introduced and discussed. More than 100 papers have been categorized and summarized to present the current development of ML/DL-based research in the area of chemical health and safety. In addition, the limitation of current studies and prospects of ML/DL-based study are also discussed. This Review can serve as useful guidance for researchers who are interested in implementing ML/DL into chemical health and safety research and for readers who try to learn more information about novel ML/DL techniques and applications.
Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications
https://orcid.org/0000-0002-2707-0346
https://orcid.org/0000-0002-6411-984X
Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that can automatically learn from data and can perform tasks such as predictions and decision-making. Interdisciplinary studies combining ML/DL with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this Review, commonly used ML/DL tools and concepts as well as popular ML/DL algorithms are introduced and discussed. More than 100 papers have been categorized and summarized to present the current development of ML/DL-based research in the area of chemical health and safety. In addition, the limitation of current studies and prospects of ML/DL-based study are also discussed. This Review can serve as useful guidance for researchers who are interested in implementing ML/DL into chemical health and safety research and for readers who try to learn more information about novel ML/DL techniques and applications.
Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications
Jiao, Zeren (Autor:in) / Hu, Pingfan (Autor:in) / Xu, Hongfei (Autor:in) / Wang, Qingsheng (Autor:in)
ACS Chemical Health & Safety ; 27 ; 316-334
23.11.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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