This research paper explores the potential of deep learning, specifically Convolutional Neural Networks (CNNs), in advancing the verification and validation of railway signaling systems. By leveraging deep learning algorithms, the aim is to improve the accuracy, efficiency, and reliability of train operations. Case studies and experiments demonstrate the effectiveness of CNNs in anomaly detection and signal classification, surpassing traditional methods. The integration of deep learning techniques enhances safety, reliability, and operational efficiency in railway signaling. Ethical considerations, data acquisition challenges, real-time integration, and interpretability of deep learning models are ad- dressed as important factors in the application of deep learning to railway signaling systems. This research highlights the significant potential of deep learning, specifically CNNs, in advancing the verification and validation of railway signaling systems, ultimately contributing to enhanced safety and efficiency....
Authors: Bheema Shanker Neyigapula.