Anomaly detection aims to detect instances that deviate significantly from the majority. Due
to the difficulties of collecting a large amount of anomalies in practice, existing methods …

Real-time safety assessment (RTSA) of dynamic systems holds substantial implications
across diverse fields, including industrial and electronic applications. However, the …

Anomaly Detection (AD) focuses on identifying unusual patterns in complex datasets and
systems. While Machine Learning and Decision Support Systems (DSS) are effective for this …

One-class classification methods are often used for anomaly detection in healthcare, quality
control in manufacturing, and fraud detection in financial services. Particularly in medical …

The existing one-class classification (OCC) methods typically presume the existence of a
pure target training set and generally face difficulties when the training set is contaminated …

Many existing anomaly detection methods assume the availability of a large-scale normal
dataset. But for many applications, limited by resources, removing all anomalous samples …