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Your search keyword '"Elghamrawy, Sally"' showing total 14 results
Start Over Author "Elghamrawy, Sally" Topic artificial intelligence
14 results on '"Elghamrawy, Sally"'

4. Genetic‐based adaptive momentum estimation for predicting mortality risk factors for COVID‐19 patients using deep learning.

Author

Elghamrawy, Sally M., Hassanien, Aboul Ella, and Vasilakos, Athanasios V.

Subjects
*DISEASE risk factors, *COVID-19, *DEEP learning, *CONVOLUTIONAL neural networks, *FEATURE extraction, MORTALITY risk factors
Abstract

The mortality risk factors for coronavirus disease (COVID‐19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID‐19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic‐based adaptive momentum estimation (GB‐ADAM) algorithm. The GB‐ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8+ T Lymphocyte (Count), D‐dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C‐reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID‐19 signs in CT scans included ground‐glass opacity (GGO), followed by crazy‐paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models. [ABSTRACT FROM AUTHOR]

Published
2022
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5. Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach.

Author

Dey, Nilanjan, Elghamrawy, Sally, and Hassanien, Aboul Ella

Subjects
Artificial intelligence, Biomedical engineering, Computational intelligence, Engineering-Data processing
Abstract

Summary: This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19. .

Published
2020

6. Measuring Global Warming Effect by the Prediction of Climate Change on the Different Countries Using Machine Learning Approaches

Author

Abdelhady, Aya Salama, Darwish, Ashraf, Hassanien, Aboul Ella, Kacprzyk, Janusz, Series Editor, Novikov, Dmitry A., Editorial Board Member, Shi, Peng, Editorial Board Member, Cao, Jinde, Editorial Board Member, Polycarpou, Marios, Editorial Board Member, Pedrycz, Witold, Editorial Board Member, Hassanien, Aboul Ella, editor, Darwish, Ashraf, editor, and Elghamrawy, Sally M., editor

Published
2024
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7. Using Artificial Intelligence Techniques in Water Quality Analysis and Prediction: Towards Sustainability

Author

Abdo, Asmaa S., Darwish, Ashraf, Hassanein, Aboul Ella, Kacprzyk, Janusz, Series Editor, Novikov, Dmitry A., Editorial Board Member, Shi, Peng, Editorial Board Member, Cao, Jinde, Editorial Board Member, Polycarpou, Marios, Editorial Board Member, Pedrycz, Witold, Editorial Board Member, Hassanien, Aboul Ella, editor, Darwish, Ashraf, editor, and Elghamrawy, Sally M., editor

Published
2024
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