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 …

Abstract Data-driven Anomaly Detection approaches have received increasing attention in
many application areas in the past few years as a tool to monitor complex systems in …

With the rapid growth of the number of connected devices that exchange personal, sensitive,
and important data through the IoT based global network, attacks that are targeting security …

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 …

Predictive maintenance is a well studied collection of techniques that aims to prolong the life
of a mechanical system by using artificial intelligence and machine learning to predict the …

Anomalous behavior identification is the process of detecting behavior that differs from its
normal. These incidents will vary from violence to war, road crashes to kidnapping, and so …

Recently, there has been a significant advancement in the water quality index (WQI) models
utilizing data-driven approaches, especially those integrating machine learning and artificial …

The “black-box” nature of artificial intelligence (AI) models has been the source of many
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …

Data-driven methods routinely achieve promising results on remaining useful life prediction,
but under a window-manner end-to-end paradigm, they suffer from unsatisfying …

We investigated the application of ensemble learning approaches in geotechnical stability
analysis and proposed a compound explainable artificial intelligence (XAI) fitted to …