Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …

Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …

Data fusion is a prevalent way to deal with imperfect raw data for capturing reliable, valuable
and accurate information. Comparing with a range of classical probabilistic data fusion …

Intrusion detection systems (IDSs) play a pivotal role in computer security by discovering
and repealing malicious activities in computer networks. Anomaly-based IDS, in particular …

Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …

The aim of an intrusion detection systems (IDS) is to detect various types of malicious
network traffic and computer usage, which cannot be detected by a conventional firewall …

Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford
limited processing resources, proposed solutions are rule-based or lightweight machine …

A current focus of intense research in pattern classification is the combination of several
classifier systems, which can be built following either the same or different models and/or …