The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Deep learning (DL), as one of the most active machine learning research fields, has
achieved great success in numerous scientific and technological disciplines, including …

Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …

This paper investigates the prediction of geological conditions ahead of tunnel boring
machines (TBM) using a hybrid deep learning approach. By integrating graph convolutional …

Abstract Network Intrusion Detection System (NIDS) is a key component in securing the
computer network from various cyber security threats and network attacks. However …

Deep learning (DL), a branch of machine learning (ML), is the core technology in today's
technological advancements and innovations. Deep learning-based approaches are the …

Developing effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

With the development of deep neural networks (DNNs), classifying streaming data with
concept drifts based on DNNs is becoming more and more effective. However, the …

Newly deployed trains have massive normal data and scarce faulty data for training, which
limits the diagnosis accuracy with class imbalance problem of small samples. Considering …