Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Human activity recognition is a key to a lot of applications such as healthcare and smart
home. In this study, we provide a comprehensive survey on recent advances and challenges …

Designed to learn long-range interactions on sequential data, transformers continue to show
state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no …

In the past decade, deep neural networks, particularly convolutional neural networks, have
revolutionised computer vision. However, all deep learning models may require a large …

Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …

This urgent and eye-opening book makes the case that protecting humanity's future is the
central challenge of our time. If all goes well, human history is just beginning. Our species …

Fast and accurate plant disease detection is critical to increasing agricultural productivity in
a sustainable way. Traditionally, human experts have been relied upon to diagnose …

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The
recent expansion of deep learning methods has found its application in plant disease …

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it
has gradually become the leading approach in many fields. It is currently playing a vital role …

This paper presents a novel method to deal with the challenging task of generating
photographic images conditioned on semantic image descriptions. Our method introduces …