ISO 9126 Quality Model For Evaluating of Student Portal: Al-Madinah International University (Case study)

E-learning is a teaching system that involves electronic resources such as computers and the Internet, and the student portal is an essential tool that supports learning in universities. However, there is a limited evaluation model for educational websites. Therefore, a framework is required to guide the creation of such a model. The study conducted evaluates the quality of the student portal at Al-Madinah International University based on ISO 9126 quality model from the student's perspective, and the results show a good quality portal. Nonetheless, students suggest improvements to enhance its effectiveness, ease of use, and learning process. Keywords: ISO 9126; Al-Madinah International University; Quality Model; Student Portal. eISSN: 2398-4287 © 2023. The Authors. Published for AMER and cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under the responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), and cE-Bs (Centre for Environment-Behaviour Studies), College of Built Environment, Universiti Teknologi MARA, Malaysia DOI:  https://doi.org/10.21834/e-bpj.v8iSI15.510

Arabic text summarization using pre-processing methodologies and techniques

Recently, one of the problems that has arisen due to the amount of information and its availability on the web, is the increased need for effective and powerful tools to automatically summarize text. For English and European languages an intensive works has been done with high performance and nowadays they look forward to multi-document and multi-language summarization. However, Arabic language still suffers from the little attention and research done in this field. In our research we propose a model to automatically summarize Arabic text using text extraction. Various steps are involved in the approach: preprocessing text, extract set of features from sentences, classify sentence based on scoring method, ranking sentences and finally generate an extract summary. The main difference between our proposed system and other Arabic summarization systems are the consideration of semantics, entity objects such as names and places, and similarity factors in our proposed system. In recent years, text summarization has seen renewed interest, and has been experiencing an increasing number of research and products especially in English language. However, in Arabic language, little work and limited research have been done in this field. will be adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with state-of-the-art methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE-1 and ROUGE-2 metrics showed promising results in comparison with existing methods

Python Solutions to Address Natural Language Challenges

Arabic is one of six official languages, according to UNESCO. It's spoken by more than 422 million Arabs, and 1.5 billion Muslims around the world use it when they pray five times a day. Arabs spoke classical Arabic more than 1400 years ago. On the other hand, dialectal Arabic is the everyday language that is used informally and varies from region to region. Modern Standard Arabic borrows from and adds to other languages to fit the needs of its speakers. Arabic is harder to learn because there are three different ways to speak it: the classical way, the modern way, and the casual way. Arabic is hard to work with on computers for more than one reason. Because Arabic has a lot of inflection and derivation, one lemma can turn into many different words with different meanings

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population