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@article{195897,
        author = {Umera Rawoot and Vaishnavi Patil and Priya Yadav and Shreya Wavekar},
        title = {Chatbot Android Application for Smart Personal Safety using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7432-7437},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195897},
        abstract = {Personal safety has become a major concern, especially for individuals who may face emergencies in public or isolated areas. This project proposes a Smart Personal Safety Android Application that combines Machine Learning, Chatbot interaction, Google Maps, and an SOS alert system The application is built using Android Studio and Kotlin for the mobile interface, while Python-based Machine Learning algorithms such as Decision Tree and Random Forest analyse behavioural and location data. The chatbot understands message-pattern recognition approach, where it monitors user messages to detect distress or abnormal conversation tone. When risk is detected, the ML model automatically activates the SOS function, sharing the live location through Google Maps API with emergency contacts. This intelligent integration enables real-time assistance, proactive detection, and efficient communication, providing a reliable and user- friendly framework for personal safety.},
        keywords = {Android Application, Chatbot, Decision Tree Algorithm, Emotion Detection, Google Maps Integration, Kotlin, Machine Learning, Natural Language Processing (NLP), Python, Random Forest, SOS System},
        month = {April},
        }