Learns the normal patterns in network traffic data Automatically removes highly correlated features Detects anomalies based on reconstruction error Provides ...

This project implements advanced AI-driven anomaly detection algorithms to identify atypical patterns in financial transaction data. It uses both Isolation Forest and Autoencoder-based approaches to ...

This paper proposes CAE-AD, a novel convolutional autoencoder anomaly detection method that relies only on normal operation data for training the intelligent classi-fier. The method also accommodates ...

Abstract: Image anomaly detection and localization have received widespread interest in the community, and knowledge distillation (KD) has been widely explored. Recently, the reverse distillation (RD) ...

You will be redirected to our submission process. The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection.