Because of the harsh working environment, there is usually a lack of effective data from the
gearboxes of wind turbines for fault classification. In this paper, a fault-diagnosis model …

In the context of few-shot classification, the goal is to train a classifier using a limited number
of samples while maintaining satisfactory performance. However, traditional metric-based …

Multiple sclerosis (MS) is a chronic neurological disorder that targets the central nervous
system, causing demyelination and neural disruption, which can include retinal nerve …

Change detection (CD) is a process of extracting changes on the Earth's surface from
bitemporal images. Current CD methods that use high-resolution remote sensing images …

This study proposes an improved few-shot learning model of the Siamese network residual
Visual Geometry Group (VGG). This model combined with time–frequency domain …

Climatic and atmospheric conditions impact mental health, including increased incidents of
depression associated with air pollution. A growing body of research considers time-bound …

The convolutional neural network-based semantic segmentation is emerging few-shot
learning, which is quite popular in the field of image processing. Semantic segmentation can …

Deep learning-based bearing fault diagnosis methods have achieved good results in recent
years. However, most of them depend heavily on massive labeled data and are not …

В этой статье исследуются компромиссы между тремя популярными архитектурами
нейронных сетей для распознавания лиц в контексте систем мониторинга …