Abstract :

Ensuring Womens Safety Has Become A Major Societal Concern Due To The Increasing Number Of Crimes Reported Across Different Regions. Although Several Digital Safety Solutions Are Available, Many Of Them Provide Only Isolated Services Such As Emergency Alerts Or Location Tracking, Limiting Their Effectiveness During Critical Situations. This Paper Presents An Integrated Womens Safety Framework That Combines Crime Prediction, Emergency Response, Location-based Navigation, And Safety Visualization Within A Single Intelligent Platform. The Proposed System Employs An Artificial Neural Network (ANN) Trained On A Publicly Available Women Crime Dataset To Estimate The Expected Number Of Crime Incidents Across Different States Based On Historical Records. Data Preprocessing Techniques, Including Cleaning, Normalization, And Feature Preparation, Are Applied To Improve Prediction Quality Before Model Training. In Addition To Predictive Analytics, The Framework Incorporates A Panic Alert Mechanism That Instantly Delivers Emergency Notifications Containing The Users Location To Registered Contacts, Enabling Rapid Assistance During Emergencies. A Route Navigation Module Assists Users In Identifying Nearby Police Stations, While A Heatmap Visualization Highlights Crime-prone Regions To Improve Situational Awareness And Support Safer Travel Decisions. The System Also Provides Secure User Authentication And Incident Reporting Functionalities To Enhance Usability And Encourage Timely Reporting Of Safetyrelated Events. Experimental Evaluation Demonstrates That The Proposed ANN Model Effectively Captures Crime Patterns And Generates Reliable Predictions While Seamlessly Integrating Multiple Safety Services Into A Unified Application. By Combining Machine Learning With Real-time Emergency Support And Location-aware Services, The Proposed Framework Offers A Practical, Scalable, And User-centric Solution For Improving Womens Personal Safety And Assisting Informed Decisionmaking In Everyday Travel.

Published:

30-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1335-1341

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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