To obtain high performance, generalization, and accuracy in machine learning applications,
such as prediction or anomaly detection, large datasets are a necessary prerequisite …

As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary
signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway …

The recent success of machine learning has accelerated the development of data-driven
lithium-ion battery state estimation and prediction. The lack of accessible battery operation …

For the past several years, there has been an increasing focus on deep learning methods
applied into computational pulse diagnosis. However, one factor restraining its development …

The convergence of data mining and deep learning has become an invaluable tool for
gaining insights into evolving events and trends. However, a persistent challenge in utilizing …

Deep Generative Models (DGMs), as a state-of-the-art technology in the field of artificial
intelligence, find extensive applications across various domains. However, their security …

Data augmentation is needed to use Deep Learning methods for the typically small time
series datasets. There is limited literature on the evaluation of the performance of the use of …

Collection of biosignals data from wearable devices for machine learning tasks can
sometimes be expensive and time-consuming and may violate privacy policies and …