Aradhya Gupta from TLDAV Model School created a Machine Learning-Based bank Security System (MLSAC) which provides robust security measures to guard against unauthorized access. The system employs a combination of photo sensors, invisible sensors, laser lights, alarms, and password authentication. This creative design provides an automated three-level security system, making it practically impossible for an intruder to bypass these security checks.
At the first level, the photo sensors actively monitor movement to detect any intruder. The presence of an intruder triggers the locking of the main door and alerts all security personnel. At the second level, an intruder must pass an LDR sensor in a dark room to unlock the second door. Once in, the laser lights and alarms notify security and the main door automatically locks to prevent escape. The last level consists of password authentication with an alarm activated if the wrong password is entered.
MLSAC is a highly efficient, automated security system that can provide secure access to critical assets and reduce theft and other security breaches.
