Utilizing E-administration to Improve the Quality of Educational Services at Saudi Universities During COVID-19

The present paper aims to identify the reality of utilizing e-administration to improve the quality of educational services at Saudi universities during COVID-19. It explores the most significant technical, human, economic, social, and administrative obstacles and the requirements of utilizing e-administration to make suggestions for utilizing e-administration to improve the quality of educational services at Saudi universities during COVID-19. The author developed and applied an interview form to (10) staff and faculty members. The paper results showed obstacles to utilizing e-administration at Saudi universities. The most significant technical issues were falsifying documents, corrupting programs and data, and cybercrimes. The human obstacles included the lack of training programs qualifying to handle the requirements of electronic work, sticking to regulations and inflexibility, and lack of laws and legislation regarding the privacy of information security. The social obstacles were the inadequate awareness of the importance of applying e-administration, and questioning the credibility of information via the e-administration means. The most significant economic obstacles were the ill funding of the e-administration training programs for staff members and the lack of financial incentives to distinguished staff members of e-administration. Furthermore, the most significant administrative obstacles included the lack of enthusiasm of the current administration for e-administration applications and the lack of assigning a department of e-administration at each college. The paper suggested linking the achievement of work to financial incentives using e-administration means and applications and enforcing the staff of Saudi universities to use e-administration applications. It recommends recruiting external experts to train the officials of the e-administration department and increasing the funds and incentives to distinguished staff in the departments of e-administration. © 2023 NSP Natural Sciences Publishing Cor.

DEVELOPMENT OF ENSEMBLE MACHINE LEARNING MODEL TO IMPROVE COVID-19 OUTBREAK FORECASTING

The world is currently facing the coronavirus disease 2019 (COVID-19 pandemic). Forecasting the progression of that pandemic is integral to planning the necessary next steps by governments and organizations. Recent studies have examined the factors that may impact COVID-19 forecasting and others have built models for predicting the numbers of active cases, recovered cases and deaths. The aim of this study was to improve the forecasting predictions by developing an ensemble machine-learning model that can be utilized in addition to the Naïve Bayes classifier, which is one of the simplest and fastest probabilistic classifiers. The first ensemble model combined gradient boosting and random forest classifiers and the second combined support vector machine and random-forest classifiers. The numbers of confirmed, recovered and death cases will be predicted for a period of 10 days. The results will be compared to the findings of previous studies. The results showed that the ensemble algorithm that combined gradient boosting and random-forest classifiers achieved the best performance, with 99% accuracy in all cases. © 2022, Scientific Research Support Fund of Jordan. All rights reserved.