Published Paper Details:

This paper introduces a GSM-based border security system that integrates Raspberry Pi, Arduino Uno, and machine learning algorithms to enhance surveillance and threat detection along border areas. The system relies on cameras as primary input devices, with video feeds processed in real time by the Raspberry Pi. Advanced machine learning algorithms analyze the visual data, accurately detecting and classifying unauthorized activities or intrusions. The Arduino Uno manages hardware-level operations and facilitates communication between system components. Upon detecting a potential security breach, the system activates an alert mechanism. A GSM module transmits instant notifications--including images, video evidence, and location details--to border security personnel, ensuring a swift response and reducing the need for human intervention. This proposed solution is cost-effective, scalable, and fully automated, improving situational awareness and reinforcing national security infrastructure by integrating machine learning with GSM communication and embedded hardware.