Generalisation across multiple tasks is a major challenge in deep learning for medical
imaging applications, as it can cause a catastrophic forgetting problem. One commonly …

In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP)
problem is considered, with the use of various types of Deep Neural Networks (DNNs). The …

Cross-modal representation learning allows to integrate information from different modalities
into one representation. At the same time, research on generative models tends to focus on …

Simple Summary In this paper, we introduce a new technique for enhancing medical image
diagnosis through transfer learning (TL). The approach addresses the issue of limited …

Anomaly detection (AD) plays a crucial role in time series applications, primarily because
time series data is employed across real-world scenarios. Detecting anomalies poses …

SARS-COV-2 is a new strain of virus that was first detected in China. It quickly spread across
the world affecting millions of people. For this reason, early detection of the virus is …

Medical image classification poses significant challenges in real-world scenarios. One major
obstacle is the scarcity of labelled training data, which hampers the performance of …

One of the major challenges in modern artificial neural network training methods is reducing
the learning time. To address this issue, a promising approach involves the continuous …

The rise of deep learning techniques has produced significantly better predictions in several
fields which lead to a widespread applicability in healthcare, finance, and autonomous …

Tremendous media attention accompanied the public launch of generative artificial
intelligence (AI) models 1, and novel applications of these technologies have continued to …