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 …

This article proposes a deep generative model for anomaly detection in unsupervised power
grid data. One-class classifier-based methods suffer from performance degradation when …

In data analysis, recognizing unusual patterns (outliers’ analysis or anomaly detection)
plays a crucial role in identifying critical events. Because of its widespread use in many …

Multivariate extreme value distributions are a common choice for modeling multivariate
extremes. In high dimensions, however, the construction of flexible and parsimonious …

A widespread methodology for modeling modern day information, which consists of high-
dimensional digital measurements, is to use vine copulas; they can flexibly encode the …

Wafer fabrication is a complex manufacturing system. Timely identification of the key
influencing factors associated with the cause of defects is critical to wafer yield …

The Vehicle-to-Grid (V2G) networks are a part of the Smart Grid networks. Their primary goal
is to recharge electric vehicles. These networks, as with any computer system, are facing …

The increased demand for and use of autonomous driving and advanced driver assistance
systems has highlighted the issue of abnormalities occurring within the perception layers …

We study the problem nonparametric classification with repeated observations. Let $\bX $
be the $ d $ dimensional feature vector and let $ Y $ denote the label taking values in …

A heterogeneous structure of social networks induces various intriguing phenomena. One of
them is the friendship paradox, which states that on average, your friends have more friends …