Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis Article Swipe
Ze Wei
,
Hui Liu
,
Xuewen Tao
,
Kai Pan
,
Rui Huang
,
Wenjing Ji
,
Jianhai Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su15086965
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su15086965
Risk assessment is of great significance in industrial production and sustainable development. Great potential is attributed to machine learning in industrial risk assessment as a promising technology in the fields of computer science and the internet. To better understand the role of machine learning in this field and to investigate the current research status, we selected 3116 papers from the SCIE and SSCI databases of the WOS retrieval platform between 1991 and 2022 as our data sample. The VOSviewer, Bibliometrix R, and CiteSpace software were used to perform co-occurrence analysis, clustering analysis, and dual-map overlay analysis of keywords. The results indicate that the development trend of machine learning in industrial risk assessment can be divided into three stages: initial exploration, stable development, and high-speed development. Machine learning algorithm design, applications in biomedicine, risk monitoring in construction and machinery, and environmental protection are the knowledge base of this study. There are three research hotspots in the application of machine learning to industrial risk assessment: the study of machine learning algorithms, the risk assessment of machine learning in the Industry 4.0 system, and the application of machine learning in autonomous driving. At present, the basic theories and structural systems related to this research have been established, and there are numerous research directions and extensive frontier branches. “Random Forest”, “Industry 4.0”, “supply chain risk assessment”, and “Internet of Things” are at the forefront of the research.
Related Topics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su15086965
- https://www.mdpi.com/2071-1050/15/8/6965/pdf?version=1682001289
- OA Status
- gold
- Cited By
- 21
- References
- 89
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366691591
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4366691591Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su15086965Digital Object Identifier
- Title
-
Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping AnalysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-20Full publication date if available
- Authors
-
Ze Wei, Hui Liu, Xuewen Tao, Kai Pan, Rui Huang, Wenjing Ji, Jianhai WangList of authors in order
- Landing page
-
https://doi.org/10.3390/su15086965Publisher landing page
- PDF URL
-
https://www.mdpi.com/2071-1050/15/8/6965/pdf?version=1682001289Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2071-1050/15/8/6965/pdf?version=1682001289Direct OA link when available
- Concepts
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Machine learning, Computer science, Artificial intelligence, Cluster analysis, Data scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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21Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 9, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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89Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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