Malware is a harmful programme, which infects computer systems, deletes data files and steals valuable information. Malware can attack personal and organization computer systems. In this paper, the most recent and dangerous types of malware, including CovidLock Ransomware, have been analysed and the most suitable countermeasures of malware have been provided. The purpose of this paper is to suggest manually removing malware through a range of tools. It investigates whether the University of Halabja employees are protected against malware or not and it hypothesizes that the university of Halabja employees are not protected in a great level against malware attacks. A questionnaire has been conducted and analysed. The results of the questionnaire confirmed that the university of Halabja employees are not crucially protected. Therefore, it works to propose a sufficient way to make the whole organization protected. This research can be extended to include public and private universities across Kurdistan region in order to identify the most secure university in this region against malware attacks.

Recently, the rate of chronic diabetes disease has increased extensively. Diabetes increases blood sugar and other problems like blurred vision, kidney failure, nerve problems, and stroke. Researchers for predicting diabetes have constructed various models. In this paper, gradient boosting classifier, AdaBoost classifier, decision tree classifier, and extra trees classifier machine learning models have been utilized for identifying chronic diabetes disease. The models analyze the PIMA Indian Diabetes dataset (PIMA) and Behavioral Risk Factor Surveillance System (BRFSS) diabetes datasets to classify patients with positive or negative diagnoses. 80% of the datasets are used as training data and 20% as testing data. The extra trees classifier with an area under curve of 0.96% for PIMA and 0.99% for BRFSS datasets outperformed other models. Therefore, it is suggested that healthcare providers can use the ETC model to predict chronic disease.

Background: Smoking is considered to be one of the main risk factors that may affect the severity of coronavirus disease 2019 (COVID-19). Previously, several meta-analyses with a limited or small sample size and insufficient methodology have been conducted investigating the impact of smoking on disease severity. Here, we use a more accurate method to identify the effect of smoking on COVID-19 disease severity. Methods: BMC, PubMed, Science Direct, Wiley, Springer, and Google Scholar websites were used to search for and select reliable articles to be included in the current analysis. Research articles that mentioned the relationship between smoking and COVID-19 severity were included. Results: Twenty-six research articles detailing 15,713 confirmed COVID-19 cases comprising patients who smoke were selected to be included in this analysis. The analysis showed a relationship between smoking, severe COVID-19, and non-severe COVID-19 (OR=0:11; 95%CI: 0.10–0.11; p<0.00001). Only 15% (2407) of the smokers suffered severe COVID-19, with the other 85% (13306) of smokers experiencing non-severe COVID-19. Conclusion: The current analysis found that only 15% of severe COVID-19 cases were smokers. Therefore, smoking is not significantly correlated with severe covid19.

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The aim of this article is to investigate the role of web 2.0 tools and social media applications in the relationship between undergraduate students and university lecturers. The researchers used the quantitative approach to design the methodology of the research. The sample of the study was 85 students from the departments of (Social Sciences, Arabic Language, and English Language) in the second year, third year, and fourth year at the college of basic education at the University of Halabja for the academic year 2020-2021. The questionnaire was used to collect the data from the participants. The result of the study demonstrated that there was a significant influence on the relationship between the students and the lecturers in using social media applications. Students showed that they use social media every day and it has a positive impact on their interaction with the lecturers.