Deep neural networks (DNNs) have evolved as a beneficial machine learning method that
has been successfully used in various applications. Currently, DNN is a superior technique …

Complex networks (CNs) have gained much attention in recent years due to their
importance and popularity. The rapid growth in the size of CNs leads to more difficulties in …

Deep learning algorithms are an important part of disease prediction and diagnosis by
analyzing health data. If not diagnosed and treated early, symptoms of nutritional anemia …

A supervised CNN Deep net classifier is proposed for the detection, classification and
diagnosis of meningioma brain tumor using deep learning approach. This proposed method …

Convolutional neural networks (CNN) are highly effective for image classification and
computer vision activities. The accuracy of CNN architecture depends on the design and …

The detection of brain tumors in brain magnetic resonance imaging (MRI) image is an
important process for preventing earlier death. This article proposes an automated computer …

This paper designs and develops a computational intelligence-based framework using
convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19 cases …

Detecting cracks in solar photovoltaic (PV) modules plays an important role in ensuring their
performance and reliability. The development of convolutional neural networks (CNNs) has …

Deploying Convolutional Neural Network (CNN)-based applications to mobile platforms can
be challenging due to the conflict between the restricted computing capacity of mobile …

Due to powerful data representation ability, deep learning has dramatically improved the
state-of-the-art in many practical applications. However, the utility highly depends on fine …