Paper Title
Gunshot Detection & Alert System using IOT & ML

Abstract
Gun-related incidents pose a significant threat to public safety worldwide. Traditional surveillance systems are reactive, leading to delayed emergency responses. This project introduces a real-time gunshot detection and alert system using IoT and machine learning. The system captures acoustic signals and video feeds to identify gunfire events accurately. Audio features are analyzed using CNN-based models, while object detection algorithms verify firearms in CCTV footage. A dual-confirmation approach minimizes false positives from non-threatening sounds or visual anomalies. Keywords - Gunshot detection, Real-time surveillance, Directional alert system, Deep learning, IoT-based security, Firearm recognition, Smart cities