Due to the surge of spatio-temporal data volume, the popularity of location-based services
and applications, and the importance of extracted knowledge from spatio-temporal data to …

The world surrounding us is subject to constant change. These changes, frequently
described as concept drift, influence many industrial and technical processes. As they can …

The spread of the Internet of Things (IoT) involving an uncountable number of applications,
combined with the rise of Machine Learning (ML), has enabled the rapid growth of pervasive …

Tiny machine learning (TinyML) is a fast-growing research area committed to democratizing
deep learning for all-pervasive microcontrollers (MCUs). Challenged by the constraints on …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

The widespread usage of machine learning in different mainstream contexts has made deep
learning the technique of choice in various domains, including finance. This systematic …

With the wide spread of sensors and smart devices in recent years, the data generation
speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems …

With the advancement in intelligent devices, social media, and the Internet of Things,
staggering amounts of new data are being generated, and the pace is continuously …

Data production has followed an increased growth in the last years, to the point that
traditional or batch machine-learning (ML) algorithms cannot cope with the sheer volume of …

Stream Learning (SL) attempts to learn from a data stream efficiently. A data stream learning
algorithm should adapt to input data distribution shifts without sacrificing accuracy. These …