Nozad Hussein Mahmood

The study aims to showcase machine learning techniques in the application of medical datasets for improving identification of correlations and relationships between variables, which will lead to more informed decision-making. Unlike other studies, intensive statistical modelling is used to understand and find the effective of variables cause to lead death due to Covid-19. Due to large dataset, not common approaches derive us to ideal conclusion. Furthermore, Bayesian technique is applied to generate predictive posterior distributions of the unknown parameters in the model in neural network as well as logistic regression, which helps us to avoid overfitting in machine learning applications and have additional measurements in assessing fitted model performance. According to the results extracted from the statistical analysis, the Bayesian neural network demonstrated superior performance in terms of classification measurements such as AUC (84.66%), F1 (87.11%), Precision (88.29%), and Recall (85.96%). The Bayesian logistic regression also performed well, but with slightly lower scores, achieving AUC (83.07%), F1 (85.59%), Precision (84.55%), and Recall (85.59%). In contrast, logistic regression (MLE) technique had the worst performance with very low scores (AUC = 52.38%, F1 = 57.55%, Precision = 57.01%, Recall = 58.10%). Regarding the variables' association with mortality, stepwise forward selection helped to identify seven significant variables. Age was found to be the most significant variable in predicting the probability of dying, with patients in the age group of (18–44) having 12 times higher odds, patients in the age group of (45–64) having 123 more odds, and patients above 65 years old having 436 times more chance to die compared to patients below 18 years old. Severe coughing was also significant with 7.26 odds, and patients suffering from diabetes had 2.82 times more chance to die. Moreover, SpO2 contributed to a decrease of 20% in the relative risk of dying from Covid-19 disease. Gender and Smoking did not show a significant association with mortality. Finally, the Bayesian approach showed higher sensitivity and specificity than the classic neural network.

The main purpose of this study was to show the role of management information system application in improving the performance of the public universities in Sulaimani city of Iraq. The study's sample consists of (200) of administrative employees from all the public universities in the city. The most important conclusion of the research is that all predicted variables of (System and Information Quality, Customer Satisfaction, User Training, Organizational Leadership, and Organization Services Quality) are significant and toward positive effect on the response variable which is Organization Performance of public universities. Furthermore, the results show that (System and Information Quality) and (Organization Services Quality) have the most and the least effects on the Organization Performance respectively. Likewise, the correlation coefficient between Organization Performance and each of the predicted variables is positive with a moderate level of power, and the most powerful relationship was between (Organization Performance) and (System and Information Quality). In order to more effectively and successfully implement the management information system of the universities and to increase the organization’s and employee’s performance, it is suggested to the universities management to upgrade the existing management information system continuously and have a training program for all administrative staff of the university.

Abstract-This research aimed to use factor analysis and cluster analysis approaches to evaluate the crucial components influencing the psychological stability of students at Salahaddin University-Erbil. To obtain our data, we selected a sample size of 149 students and surveyed them with twenty-two items about their psychological stability. According to the findings of both methods, six common factors or clusters influence the psychological stability of students labeled (Anxiety, Satisfaction, Relationship, Health, Simplicity, and Participation). Furthermore, according to the results of both methods, the first factor and the first cluster, anxiety, have the most significant effect on the psychological stability of university students compared to the other variables. Consequently, it is suggested that universities should emphasize the psychological stability of students and provide training courses for academic staff by educational and psychological experts so that they can treat the students better and understand their psychological instability situations.

The purpose of this study was to determine faculty members' attitudes toward online learning in Kurdistan region universities. The study examined the biographic and personal characteristics of the lecturer towards e-learning. The data was collected among faculty members at Cihan University-Sulaimaniya, and to analyze the data, a full factorial design with five main factors at two levels and no central points was applied for this specific purpose. The study's findings indicated that there was no significant relationship between gender and lecturer attitude towards e-learning. In comparison to teachers with an MSc degree, those with a PhD have a more negative attitude toward e-learning. Furthermore, full-time faculty members have a greater positive effect on teachers' attitudes than part-time lecturers. Likewise, the results indicate that lecturers who earned their most recent education degrees outside of Iraq have a more favorable attitude. Similarly, lecturers in the sciences are more favorable to e-learning than those in the arts and social sciences. In addition, the findings demonstrated that the interaction factors (Gender) and (Education Degree) have a negative effect on lecturers' attitudes when they are combined. Besides that, the interaction of factors (Country of Last Education Degree) and (Faculty Member Types) improves attitudes toward e-learning. Based on the results, it is suggested that academic staff receive e-learning training to deepen their knowledge and understanding of such a modern teaching system. There is also a need to enhance factors related to positive attitudes towards e-learning among university lecturers. The findings of this study are necessarily significant to both teachers and educational organizations in Kurdistan Region universities.