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 …

Load demand forecasting is a critical process in the planning of electric utilities. An
ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep …

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater
accuracy are highly desirable. To this end, a number of techniques have been proposed …

In a non-stationary environment, newly received data may have different knowledge patterns
from the data used to train learning models. As time passes, a learning model's performance …

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based
brain-computer interface (BCI) a dynamic system, thus improving its performance is a …

A common assumption in traditional supervised learning is the similar probability distribution
of data between the training phase and the testing/operating phase. When transitioning from …

This paper presents main results of fault detection and diagnosis in a cement manufacturing
plant using a new monitoring scheme. The scheme is based on multivariate statistical …

In recent years, the increasing demand on organic farming necessitates continuous
monitoring of plant health. In order to ensure the quality and quantity this becomes more …

Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-
computer Interfacing (BCI) system requires frequent calibration. This leads to inter session …