Power Predictive Modeling for Fire Hazards Using Machine Learning

Abstract: This paper presents a fire prediction protocol that integrates deep learning techniques with Internet of Things (IoT) infrastructure for rapid fire detection and enhanced safety measures. The proposed system utilizes an optimized YOLO model for real-time flame detection by processing video and images based on color, temperature, and shape analysis, achieving an accuracy of up to 81% in fire hazard detection. It incorporates flame and smoke sensors for hazard detection, triggering local alerts via a buzzer and LED, and providing remote warnings to users through a Telegram bot. The system architecture encompasses sensor selection, design, and hardware / software implementation using the Arduino IDE. Test results demonstrate improved response time and notification reliability, validating the system's efficiency in enhancing fire safety.