57 citations · 65 across the 12 of their papers we have counts for
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cs.LG2026
Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey
Ammar Bouketta, Smail Niar, Hamza Ouarnoughi
Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep learning has emerged as an effect…
cs.LG2026
Cycle-Aware Autoencoder with Cross-SignalConsistency for Railway Door Anomaly Detection
Ammar Bouketta, Smail Niar, Hamza Ouarnoughi +1
Passenger access doors are safety-critical subsystems in railway vehicles, yet detecting abnormal door behavior in real operation is challenging because faults are rare, diverse, a…