The Reality of Distance Education for Kindergarten From the Point of View of Their Mothers in the Light of Corona Pandemic

Identifying the reality of distance education for kindergarten children from the point of view of their mothers in light of the Corona pandemic, as well as identifying the role of the family in eliminating the distance education plan, the distance education environment, and the distance education environment, where the researchers followed the descriptive approach to the appropriateness of this study and the group was conducted during the semester First academic year 2021/2022. The study population consisted of a sample of mothers from a sample of kindergartens in Jubail Industrial City, and the castle included (200) mothers, and a questionnaire was distributed to them consisting of three fields comprising (15) items. © 2023 NSP.

Machine Learning Model for Predicting Number of COVID-19 Cases in Countries with Low Number of Tests

Background: The COVID-19 pandemic has presented a series of new challenges to governments and healthcare systems. Testing is one important method for monitoring and controlling the spread of COVID-19. Yet with a serious discrepancy in the resources available between rich and poor countries, not every country is able to employ widespread testing. Methods and Objective: Here, we have developed machine learning models for predicting the prevalence of COVID-19 cases in a country based on multilinear regression and neural network models. The models are trained on data from US states and tested against the reported infections in European countries. The model is based on four features: Number of tests, Population Percentage, Urban Population, and Gini index. Result(s): The population and the number of tests have the strongest correlation with the number of infections. The model was then tested on data from European countries for which the correlation coefficient between the actual and predicted cases R2 was found to be 0.88 in the multi-linear regression and 0.91 for the neural network model Conclusion(s): The model predicts that the actual prevalence of COVID-19 infection in countries where the number of tests is less than 10% of their populations is at least 26 times greater than the reported numbers. Copyright © 2022 Hashim et al.

Hyperparameters Optimization of Deep Convolutional Neural Network for Detecting COVID-19 Using Differential Evolution

COVID-19 is one of the most dangerous diseases that appeared during the past 100 years, that caused millions of deaths worldwide. It caused hundreds of billions of losses worldwide as a result of complete business paralysis. This reason has attracted many researchers to attempt to find a suitable treatment for this dreaded virus. The search for a cure is still ongoing, but many researchers around the world have begun to search for the safest ways to detect if a person carries the virus or not. Many researchers resorted to artificial intelligence and machine learning techniques in order to detect whether a person is carrying the virus or not. However, many problems are arising when using these techniques, the most important problem is the optimal selection of the parameter values for these methods, as the choice of these values greatly affects the expected results. In this chapter, Differential Evolution algorithm is used to determine the optimal values for the hyperparameters of Convolutional Neural Networks, as Differential Evolution is one of the most efficient optimization algorithms in the last two decades. The results obtained showed that the use of Differential Evolution in optimizing the hyperparameters of the Convolutional Neural Network was very efficient. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The effect of COVID-19 pandemic on global stock markets: Return, volatility, and bad state probability dynamics.

This study investigates the impact of COVID-19 pandemic on stock returns, conditional volatility, conditional skewness and bad state probability. This study utilizes an asymmetric exponential generalized autoregressive conditional heteroscedasticity model to capture the asymmetric effect of positive and negative shocks (news) on conditional volatility. Using a sample consisting of international stock market indices in Brazil, China, Italy, India, Germany, Russia, Spain, United Kingdom, and United States, over the period from January 1, 2013 to December 31, 2020, we find unprecedented increases in conditional volatilities and bad state probabilities across all the markets. However, this impact is not symmetric across markets. Furthermore, we find that the negative affect of deaths is more pronounced, compared to the positive impact of recovered cases.