The D-CNN-LSTM Autoencoder method optimizes the anomaly detection rate for all of the anomalies, specifically in the case of low magnitude anomalies, enhancing F1-score up to 18.12% in single types of ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...

The integration of Network Functions Virtualization (NFV) systems into mobile edge and core networks has heightened the need for effective anomaly detection and localization methods. The complexity of ...