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Data-Driven Approaches for Accident Analysis in Sociochemical Systems
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References

  • 2020Bias in data‐driven artificial intelligence systems—An introductory survey1,040 cited
  • 2013Big Data Integration564 cited
  • 2019Does big data enhance firm innovation competency? The mediating role of data-driven insights334 cited
  • 2020Overview and Importance of Data Quality for Machine Learning Tasks315 cited
  • 2014Data quality: The other face of Big Data262 cited
  • 2022Uncertainty modeling in risk assessment of digitalized process systems126 cited
  • 2020Analysis of severe industrial accidents caused by hazardous chemicals in South Korea from January 2008 to June 201891 cited
  • 2022Fuzzy Sets Theory and Human Reliability: Review, Applications, and Contributions77 cited
  • 2023How to account artificial intelligence in human factor analysis of complex systems?64 cited
  • 2022Learning from major accidents: A machine learning approach56 cited
  • 2022Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident55 cited
  • 2022An advanced approach to the system safety in sociotechnical systems53 cited
  • 2021Identification of human errors and influencing factors: A machine learning approach50 cited
  • 2023A hybrid model for dynamic analysis of domino effects in chemical process industries49 cited

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  • 2019Contribution of Artificial Intelligence to Risk Assessment of Railway Accidents48 cited
  • 2023An interpretable and augmented machine-learning approach for causation analysis of major accident risk indicators in the offshore petroleum industry30 cited
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    • 2024Influential criteria in domino accident analysis: An evaluation using the logarithm methodology of additive weights10 cited
    • 2025Reframing safety barriers as socio-technical systems: a review of the hydrogen sector10 cited