The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …

Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

Nowadays, video sensors are used on a large scale for various applications, including
security monitoring and smart transportation. However, the limited communication …

CCTV cameras installed for continuous surveillance generate enormous amounts of data
daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent …

IoT-based automated systems require efficient online time-series analysis and forecasting
and there is a growing requirement to enable such processing at the low-cost constrained …

Learning efficient predictive models in dynamic environments requires taking into account
the continuous changing nature of phenomena generating the data streams, known in …

Artificial intelligence (AI) model services offer remarkable efficiency and automation,
engaging customers across various tasks. However, not all AI consumers possess sufficient …

In recent years, there has been an increasing amount of streaming information coming from
time series. Learning from data appearing in real time is quite a call, due in part to the speed …

Although the precise symmetric forward and feedback connections between neurons are
thought to be impossible in the brain, most existing deep spiking neural networks (SNNs) …