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 …

This article is devoted to the development of a classification method based on an artificial
neural network architecture to solve the problem of recognizing the sources of acoustic …

We introduce a general method for achieving robust group-invariance in group-equivariant
convolutional neural networks ($ G $-CNNs), which we call the $ G $-triple-correlation ($ G …

Abstract Convolutional Neural Network is one of the famous members of the deep learning
family of neural network architectures, which is used for many purposes, including image …

Face recognition techniques have been widely employed in real-world biomimetics
applications. However, traditional approaches have limitations in recognizing faces correctly …

Research on automatically producing syntactically and semantically accurate captions is still
an open challenge. This paper proposes an effective pretrained Augmentation–Ranking (A …

Segmentation of roads in remote sensing (RS) images is a challenging task due to the
inhomogeneous intensity, nonconsistent contrast, and very cluttered background in remote …

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine
learning (ML) architectures for handling multidimensional data by maintaining data locality …

This paper presents the development and implementation of an application that recognizes
American Sign Language signs with the use of deep learning algorithms based on …

On-site wind speed information is paramount in various engineering domains. Conventional
methods, reliant on expensive equipment and skilled operators, present challenges for …