Deep convolutional neural networks (CNNs) achieve state-of-the-art performance in
wearable human activity recognition (HAR), which has become a new research trend in …

At present, there have been many studies on the methods of using the deep learning
regression model to assess depression level based on behavioral signals (facial …

Optical coherence tomography (OCT) is an imaging modality used to obtain a cross-
sectional image of the retina for retinal disease diagnosis. Modern diagnosis systems use …

Deep learning is thought of as a promising mean to identify maize diseases. However, the
drawback of deep learning is the huge sample data and low accuracy. In this paper, we …

Generative adversarial networks (GAN) have shown great potential for image quality
improvement in low-dose CT (LDCT). In general, the shallow features of generator include …

Abstract Type 2 diabetes (T2DM) is a polygenic metabolic disorder characterized by
hyperglycemia occurring as a result of impaired insulin secretion or insulin resistance …

Convolutional neural networks (CNNs) have demonstrated excellent capability in various
visual recognition tasks but impose an excessive computational burden. The latter problem …

In sign language video, the hand region is small, the resolution is low, the motion speed is
fast, and there are cross occlusion and blur phenomena, which have a great impact on sign …

Camouflage object detection (COD) aims to detect camouflaged objects hidden in the
background region in an image. The difficulty of COD lies in the fact that camouflaged …

This article considers the problem of human activity classification behind the walls using
ultrawideband (UWB) radar. The complex-valued multiscale feature fusion capsule network …