Deep-kidney: an effective deep learning framework for chronic kidney disease prediction.

Chronic kidney disease (CKD) is one of today's most serious illnesses. Because this disease usually does not manifest itself until the kidney is severely damaged, early detection saves many people's lives. Therefore, the contribution of the current paper is proposing three predictive models to predict CKD possible occurrence within 6 or 12 months before disease existence namely; convolutional neural network (CNN), long short-term memory (LSTM) model, and deep ensemble model.

Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19.

The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients' mortality risk factors is essential for reducing this risk among infected individuals.

Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation.

The wide prevalence of brain tumors in all age groups necessitates having the ability to make an early and accurate identification of the tumor type and thus select the most appropriate treatment plans. The application of convolutional neural networks (CNNs) has helped radiologists to more accurately classify the type of brain tumor from magnetic resonance images (MRIs). The learning of CNN suffers from overfitting if a suboptimal number of MRIs are introduced to the system.

Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases.

Corona Virus Disease (COVID-19) has been announced as a pandemic and is spreading rapidly throughout the world. Early detection of COVID-19 may protect many infected people. Unfortunately, COVID-19 can be mistakenly diagnosed as pneumonia or lung cancer, which with fast spread in the chest cells, can lead to patient death.

Development of an adaptive radial basis function neural network estimator-based continuous sliding mode control for uncertain nonlinear systems.

In this paper an adaptive neural network (NN)-based nonlinear controller is proposed for trajectory tracking of uncertain nonlinear systems. The adopted control algorithm combines a continuous second-order sliding mode control (CSOSMC), the radial basis function neural network (RBFNN) and the adaptive control methodology. First, a second-order sliding mode control scheme (SOSMC), which is published recently in literature for linear uncertain systems, is extended for nonlinear uncertain systems.