Authors :
Shwetha D S; Preethi K P
Volume/Issue :
Volume 10 - 2025, Issue 8 - August
Google Scholar :
https://tinyurl.com/5dtf2tdd
Scribd :
https://tinyurl.com/328xhfkd
DOI :
https://doi.org/10.38124/ijisrt/25aug524
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
Real-time emergency response solutions are necessary because women's safety is still a major worry in today's
culture. The SOS Alert System, a comprehensive safety program created to identify, notify, and react in emergency
circumstances, is presented in this paper. The system, which was created in Python and has a Tkinter-based graphical user
interface, combines contact management, automated SOS notifications, real-time location sharing, and safety zone
evaluation into a single platform. By examining variables including crime rate, population density, lighting, historical events,
time of day, and present location, a Random Forest Classifier is used to assess site safety and produce a safety score with
associated risk categories. The system's multi-channel emergency communication features, which include automated
emergency notifications, SMS warnings, and location sharing via WhatsApp, guarantee prompt aid from pre-registered,
reliable contacts. Furthermore, the platform offers interactive safety analytics via graphs such as scatter plots, pie charts,
bar charts, and histograms, which make it possible to identify high-risk locations and track safety trends. According to
experimental data, the SOS Alert System is a low-cost, user-friendly, and scalable solution that works well in urban and
semi-urban settings by improving situational awareness and guaranteeing quicker emergency response.
Keywords :
SOS Alert System, Women’s Safety, Emergency Communication, Machine Learning, Random Forest Classifier, Location Tracking, Predictive Analytics.
References :
- Sharma and Singh, "An Android mobile application for women's safety," International Journal of Computer Applications, vol. 179, no. 18, pp. 1–4, February 2018.
- In the International Journal of Emerging Technologies in Engineering Research, volume 7, issue 5, pages 65–70, 2019, P. Choudhary, V. Sharma, and K. Chouhan, "Smart SOS system for women safety using GPS and GSM." [Online]. Available: https://www.ijeter.com/download/smart-sos-system.pdf
- In May 2019, the International Journal of Innovative Research in Science, Engineering, and Technology published a paper titled "IoT-based women safety device using GPS and GSM," written by S. A. Shaikh, M. R. Shaikh, and S. Shaikh.
- A. Mahalakshmi, S. Mohanapriya, and R. S. Rekha, “Design of women safety mobile application using machine learning,” International Journal for Research in Applied Science and Engineering Technology (IJRASET), vol. 8, no. 5, May 2021. https://doi.org/10.22214/ijraset.2021.34542
- "IoT and cloud-based emergency response system for women's safety," International Conference on Communication and Electronics Systems (ICCES), pp. 1152–1157, 2020, S. A. Kumar, R. Krishnan, and N. Balasubramanian.
- “Real-time women safety system using wearable IoT devices,” International Conference on Intelligent Sustainable Systems (ICISS), pp. 820–825, 2019, M. B. Dhanalakshmi and V. Vijayalakshmi.
- In Proceedings of ICCIET 2024, R. R. Rout, S. S. Panda, and B. Panda published "AI-driven smart alerting for women safety using mobile devices.” https://doi.org/10.2991/978-94-6463-471-6_140
- "Random forest-based crime prediction and analysis system," International Conference on Computing, Communication and Automation (ICCCA), pp. 412–417, 2019; H. Gupta, A. Jain, and A. Dubey
- In July 2020, S. K. Prasad and M. R. Patil published a paper titled "Women safety mobile app with GPS tracking and SMS alerting system" in the International Journal of Emerging Trends in Engineering Research, volume 8, issue 7, pages 3269–3273.
- A. Joshi and K. Jain, "Using Android to integrate location tracking and an SOS alert system for women's safety," International Journal of Engineering Research and Technology, vol. 8, no. 10, pp. 1–5, 2020.
- "Real-time urban safety monitoring using IoT and cloud services," by M. M. Rathore, A. Paul, and W. H. Hong https://doi.org/10.1109/ACCESS.2018.2808934, IEEE Access, vol. 6, pp. 10600–10610, 2018.
- A. K. Singh and N. K. Verma, "Machine learning-based smart surveillance and emergency alert system for urban safety," International Conference on Advances in Computing, Communication, and Control (ICAC3), pp. 445–451, 2021.
Real-time emergency response solutions are necessary because women's safety is still a major worry in today's
culture. The SOS Alert System, a comprehensive safety program created to identify, notify, and react in emergency
circumstances, is presented in this paper. The system, which was created in Python and has a Tkinter-based graphical user
interface, combines contact management, automated SOS notifications, real-time location sharing, and safety zone
evaluation into a single platform. By examining variables including crime rate, population density, lighting, historical events,
time of day, and present location, a Random Forest Classifier is used to assess site safety and produce a safety score with
associated risk categories. The system's multi-channel emergency communication features, which include automated
emergency notifications, SMS warnings, and location sharing via WhatsApp, guarantee prompt aid from pre-registered,
reliable contacts. Furthermore, the platform offers interactive safety analytics via graphs such as scatter plots, pie charts,
bar charts, and histograms, which make it possible to identify high-risk locations and track safety trends. According to
experimental data, the SOS Alert System is a low-cost, user-friendly, and scalable solution that works well in urban and
semi-urban settings by improving situational awareness and guaranteeing quicker emergency response.
Keywords :
SOS Alert System, Women’s Safety, Emergency Communication, Machine Learning, Random Forest Classifier, Location Tracking, Predictive Analytics.