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 …

The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …

Abstract Convolutional Neural Networks (CNNs) use pooling to decrease the size of
activation maps. This process is crucial to increase the receptive fields and to reduce …

Deep neural networks (DNNs) have recently achieved great success in many visual
recognition tasks. However, existing deep neural network models are computationally …

Facial Expression Recognition (FER) in the wild poses significant challenges due to realistic
occlusions, illumination, scale, and head pose variations of the facial images. In this article …

To research the problems of the rolling bearing fault diagnosis under different noises and
loads, a dual-input model based on a convolutional neural network (CNN) and long-short …

Fabric defect detection is a challenging task in the fabric industry because of the complex
shapes and large variety of fabric defects. Many methods have been proposed to solve this …

The convolutional neural network architecture has different components like convolution and
pooling. The pooling is crucial component placed after the convolution layer. It plays a vital …

Pooling layers are essential building blocks of convolutional neural networks (CNNs), to
reduce computational overhead and increase the receptive fields of proceeding …