The condition monitoring (CM) of rotating machinery (RM) is an essential operation for
improving the reliability of mechanical systems. For this purpose, an efficient CM method that …

This paper provides a comprehensive review of recent challenges and results in the field of
generative AI with application to mobile telecommunications networks. The objective is to …

Time-series anomaly detection is a task of detecting data that do not follow normal data
distribution among continuously collected data. It is used for system maintenance in various …

Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …

Quality of data services is crucial for operational large-scale internet-of-things (IoT) research
data infrastructure, in particular when serving large amounts of distributed users. Effectively …

Existing anomaly detection models for time series are primarily trained with normal-point-
dominant data and would become ineffective when anomalous points intensively occur in …

The primary objective of multivariate time-series anomaly detection is to spot deviations from
regular patterns in time-series data compiled concurrently from various sensors and …

Time series anomaly detection (TSAD) is an essential problem faced in several fields, eg,
fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial …

Many real-world Internet of Things (IoT) systems contain various sensor devices. Operating
the devices generates a large amount of multivariate time series data, which reflects the …

With the increased use of automated systems, the Internet of Things (IoT), and sensors for
real-time water quality monitoring, there is a greater requirement for the timely detection of …