False Alarm

False alarms, the erroneous detection of events or patterns, pose a significant challenge across diverse fields, from security systems to medical monitoring and image analysis. Current research focuses on improving the accuracy of detection systems by employing various techniques, including statistical learning, deep learning models (like BERT and GPT), and advanced feature extraction methods to better distinguish genuine events from false positives. This work aims to reduce the rate of false alarms, thereby improving the efficiency and reliability of applications ranging from cybersecurity threat detection to medical alert systems and video surveillance. The ultimate goal is to enhance the signal-to-noise ratio, leading to more effective and trustworthy systems.