COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

University web portals are considered one of the main access gateways for universities. Accessibility of university online services is a major issue for undergraduate and graduate students with disabilities. Online registration makes people with disabilities more independent to register courses, add, drop courses, or attend courses independently. Yet, many people with disabilities in Palestine face major challenges when using university websites. In order to understand the issues that face people with disabilities when they use websites of Palestinian universities, this study evaluates the accessibility of the home pages of these universities during COVID-19. In order to evaluate partially the accessibility of Palestinian universities during COVID-19, we apply automatic evaluation on all homepages of eighteen Palestinian universities. The most violated guideline is empty link which is related to success criterion 2.4.4 Link Purpose. The second highest violated error is linked image missing alternative text which is related to the success criterion 1.1.1 Non-text Content. The obtained results show that all the universities websites are not conforming to Web Content Accessibility Guidelines (WCAG) 2.0 level A. Copyright © 2020 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.