Retired electric-vehicle lithium-ion battery (EV-LIB) packs pose severe environmental
hazards. Efficient recovery of these spent batteries is a significant way to achieve closed …

The recent wave of digitalization is characterized by the widespread deployment of sensors
in many different environments, eg, multi-sensor systems represent a critical enabling …

Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …

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

Historically, anomalies detection was an important issue for industrial applications such as
the detection of a manufacturing failure or defect. It is still a current topic that tries to meet the …

Anomaly detection is an important field in data science that has been widely researched and
applied, generating many methods. Among these methods, the isolation forest algorithm is …

This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …

Anomaly detection in time series data is essential for fraud detection and intrusion
monitoring applications. However, it poses challenges due to data complexity and high …

Modern data mining techniques have been gained importance in recent years. In particular,
anomaly detection algorithms, applied in key sectors of information technology, have been …

In the real world, a large number of multivariate time series data are generated by Internet of
Things systems, which are composed of many connected sensing devices. Therefore, it is …