EMPOWERING WOMEN: A CREATIVE APPROACH TO INTEGRATED SAFETY WITH MACHINE LEARNING ALGORITHMS

Abstract

Ensuring the safety and security of women in public and private spaces remains a critical global challenge. Traditional safety measures, while important, often lack real-time responsiveness and predictive capabilities to effectively prevent incidents before they occur. This project presents a creative and integrated approach to women’s safety by leveraging advanced machine learning algorithms to detect, predict, and respond to potential threats dynamically.The proposed system combines data from multiple sources such as real-time video surveillance, social media signals, location tracking, and emergency alerts to create a comprehensive safety network. Machine learning models analyze these data streams to identify suspicious behavior, abnormal patterns, and early warning signs of danger. By integrating various data inputs, the system aims to provide timely alerts and actionable insights to both users and law enforcement agencies.Key machine learning techniques utilized in this project include supervised learning for threat classification, anomaly detection to highlight unusual activities, and natural language processing to analyze text-based inputs like social media posts or emergency messages. These models are trained on diverse datasets to improve accuracy and reduce false alarms, thereby ensuring the system's reliability and effectiveness.Moreover, the solution incorporates a user-friendly mobile application that empowers women to instantly report incidents, share their location, and receive real-time safety notifications based on predictive analysis.