Fire Detection and Prevention System in Residential Kitchens Using IoT for Early Warning

This paper aims to develop a system that detects potential fires in residential kitchens. The system collects real-time data on changes in temperature, CO2, and cooking gas leaks using sensors and transmits this information to residents through an Android application. The machine learning model embedded in the system achieved an accuracy of 99.5\% during training and 99.4\% during testing, demonstrating high reliability in detecting anomalies. The system developed in this study will allow for early intervention and fire prevention, significantly reducing potential losses.