Practical applications involving big data, such as weather monitoring, identification of
customer preferences, Internet log analysis, and sensors warnings require challenging data …

The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …

Deep learning techniques have become one of the main propellers for solving engineering
problems effectively and efficiently. For instance, Predictive Maintenance methods have …

With the rapid development of data-driven technologies, a massive amount of actual data
emerges from industrial systems, forming data stream. Their data distribution may change …

Abnormal behaviour can be an indicator for a medical condition in older adults. Our novel
unsupervised statistical concept drift detection approach uses variational autoencoders for …

Many applications and fields produce a vast quantity of time-relevant or continuously
changing data which may represent new phenomena. This data stream behavior is known …

Real-time monitoring of human behaviours, especially in e-Health applications, has been an
active area of research in the past decades. On top of IoT-based sensing environments …

Anomaly detection is critically important for enhancing data security across networks,
industrial applications, and fraud detection systems. Traditional methods in anomaly …

The prevalence of streams in practical applications is rapidly increasing, making stream data
mining increasingly important. However, unlike the static datasets used in machine learning …

Abstract Concept drift mainly refers to the change of the current data distribution in the data
streams due to the dynamic evolution of the external environment, which leads to the failure …