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@article{177887,
        author = {Dr.K.Raghavendar and P.Archana and P.Kanteshwar and MD.Irfan and M.Pavan Kumar},
        title = {Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7543-7547},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177887},
        abstract = {Safety management in railway stations is a critical concern due to the high volume of daily commuters and the potential risk of accidents in crowded, dynamic environments. Traditional monitoring systems often rely on manual reporting or supervised learning techniques that require extensive labeled datasets, making them inefficient, time-consuming, and less adaptable to unforeseen incidents. This project introduces an advanced approach to accident prevention and safety enhancement in railway stations through the application of unsupervised machine learning techniques. By leveraging clustering and anomaly detection algorithms, the system can autonomously analyze large volumes of unstructured data—including surveillance inputs, sensor data, and historical incident records—to identify hidden patterns, classify accident types, and predict potential safety hazards. The proposed system supports real-time data integration from IoT devices, enabling continuous monitoring and early warning mechanisms. This AI-driven solution not only minimizes manual intervention but also improves accuracy, scalability, and responsiveness in safety management processes. Ultimately, this project aims to empower railway authorities with a smart, automated tool to detect, assess, and mitigate safety risks proactively.},
        keywords = {About four(minimum) key words or phrases in alphabetical order, separated by commas.},
        month = {May},
        }