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@article{209526,
        author = {Dr. M.K. Jayanthi Kannan and Jayant Ved and Atharva and Jiya Kiritkumar Patel and Pranav Ranjan and Srishti and Atraiu Roy},
        title = {AI-Driven Emergency SOS App: A Multi-Modal Intelligent Emergency Detection and Response System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {2385-2393},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209526},
        abstract = {The rapid evolution of personal safety environments, combined with the increasing reliance on technology-driven solutions, highlights the necessity of intelligent systems to assist users in making rapid distress calls. Traditional emergency SOS systems mainly depend on the user manually pressing an SOS button, which may not be possible during situations such as accidents, physical attacks, unconsciousness, or panic. The "AI-Driven Emergency SOS App" platform is designed as an innovative personal safety solution that leverages advanced machine learning algorithms to analyze physical and acoustic parameters to forecast and detect threats automatically. With features including real-time GPS tracking, predictive incident analytics, multiparameter threat assessment, and a user-friendly interface, this system aims to empower users by providing an automated and seamless emergency response. By integrating AI-based audio detection, NLP voice analysis, camera-based computer vision, and secure backend dispatching, the application enhances user safety and significantly reduces the time between threat detection and emergency assistance.},
        keywords = {Artificial Intelligence, Emergency SOS, Multimodal AI, Audio Detection, NLP, Computer Vision, GPS Tracking, Trading Automation, Risk Assessment, Safety Analytics.},
        month = {October},
        }