Enhancing Airport Security: Integrating PSO-TAM with Deep Learning for Real-Time Threat Assessment

Background: To increase their threat detecting ability in face of mounting security issues, airports are using creative algorithms. Objective: This work merges deep learning approaches with the Passenger Security Optimization with Threat Assessment Model (PSO-TAM) to improve real-time threat assessment. Contribution: This paper addresses the basic problem of the need of a more efficient and effective security model merging new analytics with present airport security technologies. The challenge is especially designing a system that reduces false alarms, increases threat detection accuracy, and provides security workers with fast and valuable information. Results and Findings: PSO-TAM routinely delivers superior accuracy, precision, recall, and F-measure even if it also shows lowered loss values. This implies that PSO-TAM not only raises the accuracy of threat predictions but also reduces false alarms, therefore enabling more consistent and strong security responses. Combining PSO with the TAM has demonstrated to be very beneficial in optimizing the classification parameters and changing to varied data volumes.