Anomaly detection is a crucial means to detect unbalanced and multiclass abnormal data in
industrial products. The existing anomaly detection model is usually suitable for products …

Anomaly detection is a crucial task in the engineering systems field. However, there is
usually little or no information about all possible abnormal modes in systems. Hence, a …

Dimension reduction is one of the key data transformation techniques in machine learning
and knowledge discovery. It can be realized by using linear and nonlinear transformation …

An example of anomaly detection for industrial production processes is shown, based on a
variational autoencoder. Starting with a simple probability corridor, the concept of …

Machine learning models have demonstrated vulnerability to adversarial attacks, more
specifically misclassification of adversarial examples. In this article, we propose a one-off …

A lo largo de la última década, los algoritmos de aprendizaje profundo o deep learning se
han convertido en un motor de innovación y transformación, con impacto en una amplia …

KPI anomaly detection plays an important role in operation and maintenance. Due to
incomplete or missing labels are common, methods based on VAE (ie, Variational Auto …

The implementation of artificial intelligence faces different challenges of infrastructural, data
related, security related and social scope. These aspects are discussed, reflecting on the …

Machine learning models with adversarial examples must be trained to differentiate between
genuine and invading data. Cyber computing technology and tools face huge intrusion …

In recent years, deep learning has been widely used in the field of anomaly detection.
Existing deep anomaly detection methods mostly focus on extracting feature representations …