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 …

Data streams may encounter data distribution changes, which can significantly impair the
accuracy of models. Concept drift detection tracks data distribution changes and signals …

The proliferation of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G,
and progress towards 6G technology have led to the accumulation of massive amounts of …

In data stream mining, the emergence of new patterns or a pattern ceasing to exist is called
concept drift. Concept drift makes the learning process complicated because of the …

In data stream mining, one of the biggest challenges is to develop algorithms that deal with
the changing data. As data evolve over time, static models become outdated. This …

With the rapid increase in communication technologies and smart devices, an enormous
surge in data traffic has been observed. A huge amount of data gets generated every …

Predicting online on data streams, with data flowing continuously, quickly, and in large
quantities, is becoming increasingly more important in tackling real-world problems. In such …

Data stream mining has become an important research area over the past decade due to the
increasing amount of data available today. Sources from various domains generate a near …

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

When using statistical models (such as a classifier) in a streaming environment, there is
often a need to detect and adapt to concept drifts to mitigate any deterioration in the model's …