Impact of Technostress on Academic Achievement of University Saudi Students During the Corona Pandemic

This study aimed to ascertain the impact of technostress on the academic achievement of Saudi university students during the Corona pandemic. Adopting the descriptive correlative approach, 387 university students were sampled to investigate the research problem. Two instruments were used in the study: A techno-stress questionnaire and an academic achievement measurement. Data were analyzed statistically, and significant results were obtained from statistical tests - means, standard deviations and Mann-Whitney test. Results showed that the dimension of technology dominance over personal life) has influenced university students, which could be attributable to rapid development connected to technology, which interferes with private life. Other dimensions, including the technical burden and technology invasion of students' personal lives and their psychological instability factor and the technology used at the university, had a positive impact on academic achievement. The technical burden has more effect on the females than the males on the technological pressure scale. Besides, the complexities of using technology reduce academic achievement. Moreover, the measurement of technological pressure has no tangible effect based on the gender variable, which may be due to homogeneity between the male and female participants. They receive the same training and education and practice the same required tasks required. Furtheunore, the impact on academic achievement due to gender, academic stage, and major stresses educating students about the effective use of technology and employing it appropriately to promote their educational achievement positively. Apart from rationalizing these findings, the study suggested that less sophisticated technology is used to help learners increase their educational attainment.

Level and Associated Factors Predicting Happiness Among Princess Nourah University Students in Saudi Arabia During Covid-19 Pandemic

Background: Happiness is considered as an important part of people's lives, and it has become part of Saudi Arabia's 2030 vision. Objective: Measure the levels and identify the demographic, family status, and academic factors associated with happiness among students in Princess Nourah Bint Abdulrahman University (PNU). Methods: A cross-sectional study with 771 participants selected from health, humanities, and science colleges by quota-sampling techniques. The data were collected by an online questionnaire consisting of four sections including the Oxford happiness questionnaire. Inferential analysis was done using Chi-square, ANOVA test, and logistic regression. Results: The average mean of happiness between the three colleges were found to be 3.97 using the Oxford questionnaire. In the demographic factors, only household income and mother employment were found to be significantly associated with students' happiness. Regarding family status, only family type and the number of family members were found to be statistically significant. However, in the academic factors, all variables were found to be significant, except the field of study and academic level. Multivariable analysis found that household income, mother employment, family number, satisfaction with specialty, environment and classmates can successfully predict the level of happiness. Conclusion: The mean level of happiness among all colleges is nearly similar, but slightly higher among health colleges. The most associated domain with happiness was found to be academic. Six variables were found to be predictive of students’ happiness. This study recommends conducting regular screening to identify any variations of students’ happiness levels.

Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System

The undergoing research aims to address the problem of COVID-19 which has turned out to be a global pandemic. Despite developing some successful vaccines, the pace has not overcome so far. Several studies have been proposed in the literature in this regard, the present study is unique in terms of its dynamic nature to adapt the rules by reconfigurable fuzzy membership function. Based on patient’s symptoms (fever, dry cough etc.) and history related to travelling, diseases/medications and interactions with confirmed patients, the proposed dynamic fuzzy rule-based system (FRBS) identifies the presence/absence of the disease. This can greatly help the healthcare professionals as well as laymen in terms of disease identification. The main motivation of this paper is to reduce the pressure on the health services due to frequent test assessment requests, in which patients can do the test anytime without the need to make reservations. The main findings are that there is a relationship between the disease and the symptoms in which some symptoms can indicate the probability of the presence of the disease such as high difficulty of breathing, cough, sore throat, and so many more. By knowing the common symptoms, we developed membership functions for these symptoms, and a model generated to distinguish between infected and non-infected people with the help of survey data collected. The model gave an accuracy of 88.78%, precision of 72.22%, sensitivity of 68.42%, specificity of 93.67%, and an f1-score of 69.28%. © 2021. All Rights Reserved.

The level of awareness among the local community in Najran Region, Saudi Arabia about emerging of coronavirus Covid-19

This study aimed to identify the level of awareness among the local community in the Najran region, Saudi Arabia about emerging of Coronavirus Covid-19, To achieve the goal of the study, the descriptive survey method was used. The study sample consisted of (930) individuals and the questionnaire were used as a tool to collect data. The results of the study showed that there is awareness among the study sample members of the most common symptoms that are difficulty breathing and a rise in temperature and coughing, and that the most preventive way to maintain personal hygiene and avoid direct contact with an infected person and the use of tissues when sneezing or coughing, and that the methods of transmission appear in the spray Flying from an infected patient while coughing, sneezing, touching contaminated surfaces and direct contact with an infected patient, and that the most common sources of information for respondents on the emerging coronavirus are social media and Internet sites.

Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects. Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives. In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus. The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict confirmed numbers and death cases. The real time data used has been collected from the World Health Organization (WHO). In the proposed research, we have considered three countries to test the proposed model, namely Saudi Arabia, Spain and Italy. The results suggest that the LSTM models show better performance in predicting the cases of coronavirus patients. Standard measure performance Mean squared Error (MSE), Root Mean Squared Error (RMSE), Mean error and correlation are employed to estimate the results of the proposed models. The empirical results of the LSTM, using the correlation metrics, are 99.94%, 99.94% and 99.91% in predicting the number of confirmed cases in the three countries. As far as the results of the LSTM model in predicting the number of death of Covid-19, they are 99.86%, 98.876% and 99.16% with respect to Saudi Arabia, Italy and Spain respectively. Similarly, the experiment's results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19, using the correlation metrics, are 99.06%, 99.96% and 99.94%, whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%, 99.96% and 99.94% with respect to the Saudi Arabia, Italy and Spain respectively. The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these countries. Such findings provide better insights regarding the future of Covid-19 this pandemic in general. The results were obtained by applying time series models, which need to be considered for the sake of saving the lives of many people.