In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

Real-world data streams pose a unique challenge to the implementation of machine
learning (ML) models and data analysis. A notable problem that has been introduced by the …

Artificial Intelligence and Machine Learning (AI/ML) can transform mobile communications,
enable new applications and services, and pave the way beyond 5G. The adoption of AI/ML …

Drift detection methods identify changes in data streams. Such changes are called concept
drifts. Existing drift detection methods often assume that the input is a noise-free data stream …

Abstract Concept drift is a prevalent phenomenon in data streams that necessitates
detection and in-depth understanding, as it signifies that the statistical properties of a target …

Twitter spam classification is a tough challenge for social media platforms and cyber security
companies. Twitter spam with illegal links may evolve over time in order to deceive filtering …

Today's social networks continuously generate massive streams of data, which provide a
valuable starting point for the detection of rumours as soon as they start to propagate …

Data shift is a phenomenon present in many real-world applications, and while there are
multiple methods attempting to detect shifts, the task of localizing and correcting the features …

Abstract Concept drift refers to the inevitable phenomenon that influences the statistical
features of the data stream. Detecting concept drift in data streams quickly and precisely …