Towards safer steel operations with a multi model framework for accident prediction and risk assessment simulation.

ABSTRACT: This research concentrates on an introduction of a multi-model approach integrating Bayesian Networks (BN), Machine Learning (ML) models, Natural Language Processing (NLP) with Sentiment Analysis, Agent-Based Modeling (ABM), and Survival Analysis to improve predictive modelling of accident causation in high-risk steel industries. The significance of the artificial intelligence (AI) based models is that every approach complements other substantiating the hypothesis. Also, the augmentation of prediction accuracy could be achieved through AI approaches contrary to conventional methods. Results reveal that the application of AI model improves the prediction accuracy compared to conventional approaches. BN application uncovers the machine conditions and human errors responsible for causing acci

SUBMITTER: Pandey S 

PROVIDER: S-EPMC12006498 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature