Anomaly detection is a critical issue across several academic fields and real-world
applications. Artificial neural networks have been proposed to detect anomalies from …

Recent semi-supervised anomaly detection methods that are trained using small labeled
anomaly examples and large unlabeled data (mostly normal data) have shown largely …

This study compares four models for industrial condition monitoring including a principal
components analysis (PCA) approach and three deep learning models, one of which is a …

All organizations, be they businesses, governments, infrastructure or utility providers,
depend on the availability and functioning of their computers, computer networks and data …

Anomaly detection, an important task in data analysis, involves identifying observations or
events that deviate in some way from the rest of the data. Machine learning techniques have …

Deep learning models are vulnerable to specifically crafted inputs, called adversarial
examples. In this paper, we present DA3G, a novel method to reliably detect evasion attacks …

Anomaly detection is a challenging task for machine learning methods due to the inherent
class imbalance. It is costly and time-demanding to manually analyse the observed data …

Abstract We discuss Empirical Risk Minimization approach in conjunction with one-class
classification method to learn classifiers for biased Positive Unlabeled (PU) data. For such …

The present PhD thesis delves into the topic of predictive maintenance (PdM), which is a
maintenance strategy for detecting, predicting, and planning the maintenance needs of …

Neural networks follow a gradient-based learning scheme, adapting their mapping
parameters by back-propagating the output loss. Samples unlike the ones seen during …