Published Paper Details:

Women's safety is a critical concern in modern society, and leveraging machine learning (ML) technologies offers innovative solutions to enhance personal security. This paper explores the application of machine learning for real-time detection of dangerous or threatening situations, aimed at improving the safety of women. The proposed system integrates various techniques such as computer vision, audio analysis, wearable sensors, and natural language processing to detect potential risks, including physical assaults, verbal threats, and abnormal behavior. Machine learning models analyze real-time data, such as video feeds, audio signals, and motion patterns from wearable devices, to identify distress signals and trigger immediate alerts. In addition, geofencing and behavioral prediction models enable proactive monitoring of users' movements, sending notifications if dangerous situations are detected or if a user deviates from safe routines. While promising, the system faces challenges related to data privacy, accuracy, and real-time processing requirements. Despite these challenges, machine learning presents a transformative opportunity to enhance women's safety, offering efficient, scalable, and personalized protection through automated threat detection and immediate emergency response mechanisms.