Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm.

Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.

Effects of Platelet-Rich Plasma on the Oxymetholone-Induced Testicular Toxicity.

Oxymetholone is one of the anabolic steroids that has widely been used among teenagers and athletes to increase their muscle bulk. It has undesirable effects on male health and fertility. In this study, the therapeutic effects of platelet-rich plasma (PRP) on oxymetholone-induced testicular toxicity were investigated in adult albino rats. During the experiments, 49 adult male albino rats were divided into 4 main groups: Group 0 (donor group) included 10 rats for the donation of PRP, Group I (control group) included 15 rats, Group II included 8 rats that received 10 mg/kg of oxymetholone orally, once daily, for 30 days, and Group III included 16 rats and was subdivided into 2 subgroups (IIIa and IIIb) that received oxymetholone the same as group II and then received PRP once and twice, respectively. Testicular tissues of all examined rats were obtained for processing and histological examination and sperm smears were stained and examined for sperm morphology. Oxymetholone-treated rats revealed wide spaces in between the tubules, vacuolated cytoplasm, and dark pyknotic nuclei of most cells, as well as deposition of homogenous acidophilic material between the tubules. Electron microscopic examination showed vacuolated cytoplasm of most cells, swollen mitochondria, and perinuclear dilatation. Concerning subgroup IIIa (PRP once), there was a partial improvement in the form of decreased vacuolations and regeneration of spermatogenic cells, as well as a reasonable improvement in sperm morphology. Regarding subgroup IIIb (PRP twice), histological sections revealed restoration of the normal testicular structure to a great extent, regeneration of the spermatogenic cells, and most sperms had normal morphology. Thus, it is recommended to use PRP to minimize structural changes in the testis of adult albino rats caused by oxymetholone.

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview.

Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia.

COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities.

Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil.

COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.

Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks.

SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200,000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 10 May 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic.