In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

Anomaly detection is critical for network security, fraud detection, and system health
monitoring applications. Traditional methods like statistical approaches and distance-based …

Low back pain is one of the main causes of motor disabilities and psychological stress, with
the painful process encompassing sensory and affective components. Noxious stimuli …

The main aim of the outlying aspect mining algorithm is to automatically detect the subspace
(s)(aka aspect (s)), where a given data point is dramatically different than the rest of the data …

Massively parallel sequencing helps create new knowledge on genes, variants and their
association with disease phenotype. This important technological advancement …

The surge in real-time data collection across various industries has underscored the need
for advanced anomaly detection in both univariate and multivariate time series data. This …

Reconstruction-based anomaly detectors have drawn much attention recently. Existing
methods rely almost universally on the neural network autoencoder and its variants. Their …

In order to prevent and control the increasing number of serious epidemics, the ability to
predict the risk caused by emerging outbreaks is essential. However, most current risk …

Background Tolerance limit is defined on pre‐treatment patient specific quality assurance
results to identify “out of the norm” dose discrepancy in plan. An out‐of‐tolerance plan …

Background Anomalies in healthcare refer to deviation from the norm of unusual or
unexpected patterns or activities related to patients, diseases or medical centres. Detecting …