Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …

Anomalies are occurrences in a dataset that are in some way unusual and do not fit the
general patterns. The concept of the anomaly is typically ill defined and perceived as vague …

The diversity and specificities of today's businesses have leveraged a wide range of
prediction techniques. In particular, churn prediction is a major economic concern for many …

With the rapid development of emerging technologies such as self-media, the Internet of
Things, and cloud computing, massive data applications are crossing the threshold of the …

Connected autonomous vehicles (CAVs) are revolutionizing the development of
transportation due to their potential to improve transportation performance in many ways …

Customer churn prediction and profiling are two major economic concerns for many
companies. Different learning approaches have been proposed, however the a priori choice …

Clustering is an unsupervised learning technique that discovers intrinsic groups based on
proximity between data points. Therefore, the performance of clustering techniques mainly …

Abstract Semantics-aware anomaly detection based on log has attracted much attention.
However, the existing methods based on the weighted aggregation of all word vectors might …

The impact of outliers and anomalies on model estimation and data processing is of
paramount importance, as evidenced by the extensive body of research spanning various …