Improving real-time security screening

The Transportation Security Administration (TSA) is responsible for air travel safety within the United States but faces a significant challenge. Recent studies show an alarming 80% failure rate in threat detection at most screening locations, primarily due to heavy reliance on human judgment. With more than 50,000 TSA officers screening over 2 million passengers daily, it is essential to address this issue promptly, as evidenced by a 42% increase in complaints related to TSA screening over the past year, according to the US Department of Transportation's monthly air travel consumer report. These complaints underscore the pressing need for improved threat detection procedures in airport security. In response to these critical concerns, we present a novel and efficient neural network classification algorithm as a potential solution, specifically designed to mitigate the identified shortcomings in the TSA's threat detection capabilities. By reducing the overall complexity of larger models, through the application of advanced layers and an artfully configured structure, we achieve a solution that maximizes efficiency without compromising accuracy. This research bridges the gap between the demands of contemporary threat detection and the practical limitations of airport security procedures. By introducing a tailored solution, we aim to significantly enhance the efficacy of threat detection, thereby contributing to the overall safety and security of air travel. This work represents a pivotal step in addressing the critical issues associated with the TSA's current screening methods and underscores the potential of advanced technology to bolster the reliability of threat detection systems.