Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …

This paper proposes FeSAD, a framework that will allow a machine learning classifier to
detect evolutionary ransomware. Ransomware is a critical player in the malware space that …

This paper investigates how different genetic and nature-inspired feature selection
algorithms operate in systems where the prediction model changes over time in unforeseen …

Last decade demonstrate the massive growth in organizational data which keeps on
increasing multi‐fold as millions of records get updated every second. Handling such vast …

Deployment of an air quality low-cost sensor network (AQLCSN), with proper calibration of
low-cost sensors (LCS), offers the potential to substantially increase the ability to monitor air …

The goal of continuous learning is to acquire and fine-tune knowledge incrementally without
erasing already existing knowledge. How to mitigate this erasure, known as catastrophic …

Abstract Machine learning (ML) based time series forecasting models often require and
assume certain degrees of stationarity in the data when producing forecasts. However, in …

Abstract Concept evolution detection is an important but difficult task in streaming data
analysis, and further the noise may seriously limit the detection performance gains. This …

Data streams applications generate a continuous stream of data in a high rate that it is not
possible to store all data in available memory. Hence, it is important to apply techniques that …

Homogeneous ensembles are very effective in concept-drift adaptation. However, choosing
an appropriate base learner and its hyperparameters suitable for a stream is critical for their …