One of the most promising techniques used in various sciences is deep neural networks
(DNNs). A special type of DNN called a convolutional neural network (CNN) consists of …

Convolutional neural networks (CNN) are widely used in computer vision and medical
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …

Global context information is essential for the semantic segmentation of remote sensing (RS)
images. However, most existing methods rely on a convolutional neural network (CNN) …

Edge intelligence (EI) migrates data and artificial intelligence (AI) to the “edge” of a network,
enhancing the high-bandwidth and low-latency of wireless data transmission with the …

Abstract Computer-Aided Diagnosis (CAD) for dermatological diseases offers one of the
most notable showcases where deep learning technologies display their impressive …

Forest fires are a huge ecological hazard, and smoke is an early characteristic of forest fires.
Smoke is present only in a tiny region in images that are captured in the early stages of …

Breast cancer is considered the most commonly diagnosed cancer globally and falls second
to lung cancer. For the early detection of breast tumors in women, breast cancer analysis …

The detection and location of dead trees are extremely important for the management and
estimating naturalness of the forests, and timely replanting of dead trees can effectively …

Spatial downsampling layers are favored in convolutional neural networks (CNNs) to
downscale feature maps for larger receptive fields and less memory consumption. However …

The occurrence of forest fires can lead to ecological damage, property loss, and human
casualties. Current forest fire smoke detection methods do not sufficiently consider the …