Marwa A. Shouman

TL;DR: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework and indicated that clinical studies are the next milestone of this research work.

TL;DR: A new framework of cascaded deep learning classifiers to enhance the performance of these CAD systems for highly suspected COVID-19 and pneumonia diseases in X-ray images and shows that VGG16, ResNet50V2, and Dense Neural Network (DenseNet169) models achieved the best detection accuracy of CO VID-19, viral (Non-COVID- 19) pneumonia, and bacterial pneumonia images, respectively.

TL;DR: In this article, an Internet of Things (IoT) enabled system for monitoring and predicting PM2.5 concentration on both edge devices and the cloud is proposed, which employs a hybrid prediction architecture using several Machine Learning (ML) algorithms hosted by Nonlinear AutoRegression with eXogenous input (NARX).

TL;DR: In this paper , the authors present a review of how digital watermarking technologies are really very helpful in the copyright protection of the DNNs and several optimizers are proposed to improve the accuracy against the fine-tuning attack.

TL;DR: In this article , an enhanced method for PM2.5 prediction within the next hour is introduced using nonlinear autoregression with exogenous input (NARX) model hosting a convolutional neural network (CNN) followed by long short-term memory (LSTM) neural networks.