Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Computerized methods provide analyses of skin lesions from dermoscopy images
automatically. However, the images acquired from dermoscopy devices are noisy and cause …

Deep convolutional neural networks (CNNs) are used for image denoising via automatically
mining accurate structure information. However, most of existing CNNs depend on enlarging …

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard
and important tasks for several applications in the field of medical analysis. As each brain …

To overcome the shortcomings in current study of oil well production prediction, we propose
a combined model (CNN-LSTM-SA) with the convolutional neural network (CNN), the long …

Due to strong learning ability, convolutional neural networks (CNNs) have been developed
in image denoising. However, convolutional operations may change original distributions of …

As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to
obtain good performance in image denoising. However, how to obtain effective structural …

Recently, deep learned enabled end-to-end communication systems have been developed
to merge all physical layer blocks in the traditional communication systems, which make joint …

Existing deep learning-enabled semantic communication systems often rely on shared
background knowledge between the transmitter and receiver that includes empirical data …