ABSTRACT
This work is devoted to introduce a reliable, fast and highly accurate reservoir computer machine learning scheme to forecast time evolution of COVID-19 pandemic. In particular, the COVID-19 official data related to susceptible cases, confirmed cases, and recovered cases in Egypt and Saudi Arabia are collected. They employed as the training data for suggested reservoir computer (RC) model. Then, detailed simulation experiments are carried out within specified time periods. The evolution of COVID-19 in Egypt and Saudi Arabia are predicted on the subsequent times intervals and compared with real validation test data. The forecasting accuracy is improved by computing the optimal output matrix which minimizes the normalized root mean square errors (NRMSEs). The performance of RC scheme is evaluated when different-size training data, different-size test data, and different number of internal nodes are used. The comparisons with the robust LSTM deep learning techniques are performed. It is shown that the presented RC-based forecasting technique is more accurate for long-time forecasting, faster, and has lower computational cost. © 2023 THE AUTHORS
ABSTRACT
Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pre-train Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score. © 2022 Little Lion Scientific. All rights reserved.
ABSTRACT
In this paper, a discrete fractional Susceptible-Infected-Treatment-Recovered-Susceptible (SITRS) model for simulating the coronavirus (COVID-19) pandemic is presented. The model is a modification to a recent continuous-time SITR model by taking into account the possibility that people who have been infected before can lose their temporary immunity and get reinfected. Moreover, a modification is suggested in the present model to correct the improper assumption that the infection rates of both normal susceptible and old aged/seriously diseased people are equal. This modification complies with experimental data. The equilibrium points for the proposed model are found and results of thorough stability analysis are discussed. A full numerical simulation is carried out and gives a better analysis of the disease spread, influences of model’s parameters, and how to control the virus. Comparisons with clinical data are also provided. [ABSTRACT FROM AUTHOR] Copyright of Fractals is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)