Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

The last decade has witnessed the explosion of machine learning research studies with the
inception of several algorithms proposed and successfully adopted in different application …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Mangroves are a globally important ecosystem that provides a wide range of ecosystem
system services, such as carbon capture and storage, coastal protection and fisheries …

Outlier detection refers to the identification of rare items that are deviant from the general
data distribution. Existing approaches suffer from high computational complexity, low …

Many real-world cyber-physical systems (CPSs) are engineered for mission-critical tasks
and usually are prime targets for cyber-attacks. The rich sensor data in CPSs can be …

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector
in recent years due to its general effectiveness across different benchmarks and strong …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

The monitoring of rotating machinery is an essential task in today's production processes.
Currently, several machine learning and deep learning-based modules have achieved …

Several techniques for multivariate time series anomaly detection have been proposed
recently, but a systematic comparison on a common set of datasets and metrics is lacking …