Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

With the explosive growth of mathematical optimization and computing hardware, deep
neural networks (DNN) have become tremendously powerful tools to solve many …

The integration of communication networks and the Internet of Things (IoT) in Industrial
Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing …

To address the challenges of long-tailed classification, researchers have proposed several
approaches to reduce model bias, most of which assume that classes with few samples are …

To leverage the enormous amount of unlabeled data on distributed edge devices, we
formulate a new problem in federated learning called federated unsupervised …

Construction machinery and robots are essential equipment for major infrastructure. The
application of deep learning technology can improve the construction quality and alleviate …

Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The
delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to …

This study reviews the technique of convolutional neural network (CNN) applied in a specific
field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on …

In observational studies, confounder separation and balancing are the fundamental
problems of treatment effect estimation. Most of the previous methods focused on …

Neural network-based approaches can achieve high accuracy in various medical image
segmentation tasks. However, they generally require large labelled datasets for supervised …