Although Deep Learning (DL) is revolutionising practices across fields, it requires a large
amount of data and computing resources, requires considerable training time, and is thus …

Transfer learning (TL) reduces the training overheads by transferring knowledge across
domains/tasks. However, the advantages of TL come with computation and communication …

Deep learning (DL) has become increasingly popular in recent years, with researchers and
businesses alike successfully applying it to a wide range of tasks. However, one challenge …

Deep Convolutional Neural Networks (CNN) have evolved as popular machine learning
models for image classification during the past few years, due to their ability to learn the …

Meteorological applications such as precipitation nowcasting, synthetic radar generation,
statistical downscaling and others have benefited from deep learning (DL) approaches …

Building accurate machine learning models for mapping crops using remote sensing
imagery is a challenging task. Traditional solutions include per-pixel based and object …

Deep transfer learning recently has acquired significant research interest. It makes use of
pre-trained models that are learned from a source domain, and utilizes these models for the …

Considering the increasing number of communicable disease cases such as COVID-19
worldwide, the early detection of the disease can prevent and limit the outbreak. Besides …

With the gradual advancement of technology in the production of consumer goods, the
Internet-of-Things (IoT) systems have experienced rapid development, resulting in a …

Nowadays, image classification is a core task for many high impact applications such as
object recognition, self-driving cars, national security (border monitoring, assault detection) …