Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

We investigate anomaly detection in an unsupervised framework and introduce long short-
term memory (LSTM) neural network-based algorithms. In particular, given variable length …

How to build a generic deep one-class (DeepOC) model to solve one-class classification
problems for anomaly detection, such as anomalous event detection in complex scenes …

Shrimp quality evaluations fulfill an essential role in producing high-value shrimp products.
The presence of soft-shell shrimp deteriorates the quality of shrimp products. The biggest …

Considering that weighted possibilistic fuzzy clustering does not obtain significant
performance compared with possibilistic fuzzy clustering, so this paper proposes an …

This paper presents a Semi-supervised Learning approach for anomaly detection in the
Automatic Generation Control loop of the power systems. The proposed technique is an …

Anomaly detection on multivariate key performance indicators (KPIs) is a key procedure for
the quality and reliability of large-scale cyber-physical systems (CPSs). Although extensive …

Fuzzy C‐means (FCM) is an important clustering algorithm with broad applications such as
retail market data analysis, network monitoring, web usage mining, and stock market …

This paper focuses on detecting cyber-attacks targeting the Automatic Generation Control
(AGC) loop and market operation. To achieve this, a new data-driven learning algorithm is …