MACHINE LEARNING WITHOUT HUMAN SUPERVISION FOR HANDLING SAFETY INCIDENTS IN TRAIN STATIONS

Authors

  • M.Kavitha , Badige Bhagyasree , Bandari Veneela , Paluchuri Siri

Keywords:

Machine learning, Unsupervised learning, Principal Component Analysis, K-Means, Unlabeled data, Labeled data, Text mining.

Abstract

Railroad operations must be reliable, easily accessible, well-maintained, and safe (RAMS) in order to move both passengers and freight. The potential for accidents at railway stations is a major issue with everyday operations in many cities. Damage to the market reputation, injuries, public fear, and expenses are further outcomes of accidents. Stations like this are feeling the heat from increased demand,
which is putting a strain on infrastructure and making safety a top administrative priority.

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References

A. Ahadh et al. ”Text mining of accident reports using semi-supervised keyword extraction and topic modeling” Process Safety and Environmental Protection (2021)

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Published

2023-12-24

How to Cite

M.Kavitha , Badige Bhagyasree , Bandari Veneela , Paluchuri Siri. (2023). MACHINE LEARNING WITHOUT HUMAN SUPERVISION FOR HANDLING SAFETY INCIDENTS IN TRAIN STATIONS. Pegem Journal of Education and Instruction, 13(4), 800–811. Retrieved from https://pegegog.net/index.php/pegegog/article/view/4038

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

License

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