Deep Learning Frame Work for Easily Detection and Prediction of Breast Cancer

Abstract

Vehicle theft and unsafe driving conditions have become major concerns in modern transportation systems due to the increasing number of vehicles and the limitations of conventional security methods. Traditional systems such as mechanical locks and basic alarm systems provide limited protection and are unable to offer real-time monitoring and automated response mechanisms. This project presents a Real-Time Vehicle Theft Control System using Engine Safety Technique, designed to enhance vehicle security and improve safety through the integration of embedded systems and Internet of Things (IoT) technologies. The proposed system utilizes a Raspberry Pi Zero W as the central processing unit along with IR sensors, flame sensors, MQ135 gas sensors, GPS modules, and IoT communication systems for continuous monitoring and intelligent control. The IR sensor is used to detect authorized access, while the flame sensor identifies fire hazards and the MQ135 sensor detects harmful gases and fuel leakage conditions. The GPS module enables real-time vehicle location tracking during theft situations. Based on sensor inputs, the Raspberry Pi processes information and automatically performs safety actions such as engine shutdown and alert generation