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 …

When put into practice in the real world, predictive maintenance presents a set of challenges
for fault detection and prognosis that are often overlooked in studies validated with data from …

River is a machine learning library for dynamic data streams and continual learning. It
provides multiple state-of-the-art learning methods, data generators/transformers …

Abstract Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems
that exploit the unique physical properties of nanostructured materials. In addition to their …

Data streams are potentially unbounded sequences of instances arriving over time to a
classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving …

The significant growth of interconnected Internet‐of‐Things (IoT) devices, the use of social
networks, along with the evolution of technology in different domains, lead to a rise in the …

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

Rapid expansion of smart metering technologies has enabled large-scale collection of
electricity consumption data and created the foundation for sensor-based load forecasting …

The discipline of seismology is based on observations of ground motion that are inherently
undersampled in space and time. Our basic understanding of earthquake processes and our …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …