Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …

This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …

The purpose is to explore the feature recognition, diagnosis, and forecasting performances
of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins …

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

Accurate load forecasting can create both economic and reliability benefits for power system
operators. However, the cyberattack on load forecasting may mislead operators to make …

One-class classification (OCC) poses as an essential component in many machine learning
and computer vision applications, including novelty, anomaly, and outlier detection systems …

Novelty detection is a classification problem to identify abnormal patterns; therefore, it is an
important task for applications such as fraud detection, fault diagnosis and disease …

This paper proposes a novel anomaly detection methodology for industrial systems based
on Digital Twin (DT) ecosystems. In addition to DTs, conceived as a digital representation of …

Pavement management systems (PMSs) have a primary role in determining pavement
condition monitoring and maintenance strategies. Moreover, many researchers have …

Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse
domains. However, given a new detection task, it is unclear how to choose an algorithm to …