Diabetes Mellitus (DM) is a frequent condition in which the body's sugar levels are abnormally high for an extended length of time. It is a major cause of death with high mortality rates and the second leading cause of total years lived with disability worldwide. Its seriousness comes from its long-term complications, including nephropathy, retinopathy, and neuropathy leading to kidney failure, poor vision and blindness, and peripheral sensory loss, respectively. Such conditions are life-threatening and affect patients’ quality of life. Therefore, this paper aims to identify the most relevant features in the diagnosis of DM and identify the best classifier that can efficiently diagnose DM based on a set of relevant features. To achieve this, four different feature selection methods have been utilized. Moreover, twelve different classifiers that belong to six learning strategies have been evaluated using two datasets and several evaluation metrics such as Accuracy, Precision, Recall, F1-measure, and ROC area. The obtained results revealed that the correlation attribute evaluation method would be the best choice to handle the task of feature selection and ranking for the considered datasets, especially when considering the Accuracy metric. Furthermore, MultiClassClassifier would be the best classifier to handle Diabetes datasets, especially when considering True Positive, precision, and Recall metrics.

This study looks at how gamifying the classroom might help students retain and apply what they have learned in the Jordan educational system. During the year-long research, 500 participants from a wide range of educational attainment levels served as participants. Immediately after participation in gamified courses, participants retain a significant proportion of their newly acquired skills over a long period of time, demonstrating a notable improvement in retention. Important factors that affect how well one remembers newly acquired abilities include intrinsic motivation and interest. What's more, studies have shown that there's a strong link between keeping knowledge and being able to use it elsewhere, which highlights the need of maintaining competence for maximum efficiency in applying knowledge in the real world. Important implications for the Jordan educational system may be drawn from the findings since they are consistent with the goals of Vision 2030. The goal of this nationwide effort is to train workers who can sustainably advance the nation's economy and culture.

A robust authentication method is needed to protect online user accounts and data from cyber-attacks. Using only passwords is insufficient because they can be easily stolen or cracked. Multi-factor authentication (MFA) increases security by requiring two or more verification factors from the user before granting access to a resource such as an online account or an application. MFA is essential to a strong identity and access management (IAM) policy. This study evaluates and contrasts several MFA methods for online systems, including Microsoft Authenticator, FIDO2 security keys, SMS, voice calls, and biometrics. We assess these methods based on four criteria: security, usability, cost, and compatibility. We discover that only some MFA methods excel across the board. The best MFA method will depend on the organization's and users' specific needs and preferences. Each MFA method has benefits and drawbacks on its own. Based on our analysis, we do, however, make some general observations and recommendations, such as preferring FIDO2 security keys and certificate-based authentication for high-security scenarios, choosing Microsoft Authenticator and biometrics for high-usability scenarios, and avoiding SMS and voice calls for low-security and low-usability scenarios.

The onset of the COVID-19 pandemic has wrought a profound transformation in the higher education landscape, compelling universities worldwide to pivot towards E-learning as the primary mode of instruction. This study, which was performed on a sample of 330 undergraduate students at The University of Jordan, undertook an in-depth exploration of student satisfaction with E-learning at The University of Jordan during this unprecedented shift, examining it through four demographic variables: gender, residence, faculty type, and academic year. The researchers employed rigorous statistical analyses, including calculations of mean and standard deviation, as well as one-way ANOVA tests, to thoroughly examine the data. The findings indicate a strong overall satisfaction level among students regarding E-learning, with discernible variations between items. There is a noteworthy correlation observed between students' academic faculty and their academic year in relation to their satisfaction level, with a p-value below 0.05, indicating statistical significance. This underscores the nuanced influence of these demographic factors in shaping students' perceptions and contentment levels as they navigate the evolving landscape of higher education during this extraordinary period. While the gender and the residence have no effect on the satisfaction level.

This study investigates the impact of sustainability initiatives, choice of content, and engagement with environmental content on environmental awareness. Employing a sample of 435 respondents and utilizing Partial Least Squares (PLS) analysis, the research provides valuable insights into the role of digital marketing strategies in fostering environmental consciousness. The findings of this study highlight the substantial influence that digital marketing techniques can have on shaping individuals' awareness and attitudes towards environmental concerns. The confirmed hypotheses underscore the effectiveness of these strategies in promoting environmental awareness among diverse audiences. As the digital landscape continues to evolve, the research contributes to the growing synergy between digital marketing and environmental advocacy, encouraging further exploration and practical applications for a more sustainable future.

The COVID-19 pandemic has led to the end of in-person classes at universities and schools and the beginning of digital advancements in higher education. Flipped learning is very different from traditional teaching methods and necessitates some shifts in the roles of the teacher and the student. The purpose of this study is to investigate the effect of using flipped learning on students' achievement and measure their attitudes towards learning through it during the Corona pandemic period. A quasi-experimental study design was adopted through the pretest and posttest measurements. Two groups were randomly assigned one to be experimental, and the other a control. The present study showed that there was no statistically significant difference between the mean of the pre-measurement and post-measurement tests of the experimental group’s motivation towards learning using the flipped learning strategy. The findings from the quantitative data revealed that flipped learning contributed to the academic success of students and their attitudes toward learning during the corona pandemic period. Hence, further studies with more extended periods are recommended to examine the effect of Flipped learning on self-directed learning and other related variables.

The present study aims to examine the implementation of Blockchain Technology within the financial sector, with a specific emphasis on its acceptance by Islamic banks operating in the United Arab Emirates (UAE). In the context of a technologically advanced period that necessitates expeditious and safe transactions, blockchain emerges as a viable remedy by obviating the need for intermediary entities and augmenting the velocity and security of transactions. This study uses Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate the important success aspects of technology and its influence on the performance of Islamic banks. The findings of this study are of particular importance, as they demonstrate the considerable impact of Investment Willingness on Financial Resources. Additionally, the study highlights the crucial role played by Organizational Culture and Leadership Support in fostering readiness for adoption. This study serves as an initial step towards the wider use of blockchain technology in the financial industry, with a particular focus on its application within Islamic banking institutions.

The aim of this study was to examine how the implications of the Covid-19 pandemic moderate the impact of Artificial Intelligence (AI) on the effective application of Cyber Governance (CG) in Islamic banks. A total of 93 questionnaires from branch heads of Islamic banks were used in this study, and the data were analyzed using the Statistical Package for Social Sciences (SPSS) through descriptive-analytical methods. The findings indicated that AI has a significant influence on the effective application of CG in Islamic banks. The study also revealed that the Covid-19 Pandemic positively moderates the influence of AI on the effective application of CG in Islamic banks. The results of this study have implications for regulators and decision-makers in proposing new legislation to effectively apply CG in the Islamic banking sector, which can help protect public funds and limit cyber-attacks. This study is the first to investigate the moderating effect of the Covid-19 pandemic on the influence of AI on the effective application of CG in Islamic banks.

The aim of this study is to examine the impact of Big Data on strategic foresight in the presence of open innovation as a (mediating variable) in the companies of the therapeutic industry and the medical supplies sector in Amman the capital city of Jordan. Out of the (121) companies making up the total of such companies, the study focused on (18) industrial companies that had more than (100) employees in total, however only (11) of those have consented to take part in the study. The study population consisted of (271) employees occupying different jobs (general manager, deputy general manager, unit manager, department manager). Due to the limited size of the population, the entire number of employees were included as participants using the comprehensive survey technique, and the number of returned and validated questionnaires for analysis were (259), representing (95.5%) of the total. In order to determine the study problem, pilot structured interviews were used in a sample of the mentioned companies. The questionnaire was employed as the key tool to measure the study variables through data collection, and the descriptive and inferential statistics methods were used to analyze the collected data, through calculations of the arithmetic mean, standard deviation, t-test and half-segmentation, exploratory and confirmatory factor analysis and the structured equation model using SMART PLS3 for hypothesis testing. The study culminated in several results, the most important of which was evidence that open innovation played a partial mediating role in the relationship between big data and strategic foresight in the companies of the therapeutic industries and medical supplies sector. Accordingly, a set of recommendations were put forward, the most important of which is to increase investment in big data in the companies due to its importance in foreseeing the future. As well as applying open innovation practices and strengthening strategic foresight practices due to its important role in avoiding extensive losses and seizing new opportunities. Highlighting the need to pay attention to open innovation practices that generate ideas and help to have strategic foresight.

Educational systems worldwide have witnessed a significant shift towards technological applications, especially after COVID-19, which impacted how the learning contents are delivered in classrooms. Given the increased attention given to the numerous advantages of computer Simulations (CSs) programs, particularly in science education, this study compared the efficacy of employing a lab simulation of Newton's Second Law of Motion to teach physics in the UAE secondary school environment versus the more conventional approach (Face-to-face instruction). The study employed a quasi-experimental design that included 90 UAE 11th-grade students from two public schools in the City of Al Ain. The intervention included student engagement in the PhET interactive simulation of Newton’s second law of motion. The study employed the Test of Science-Related Attitudes (TOSRA) questionnaire to collect data before and after the intervention for the experimental and control groups. The findings demonstrated statistically significant differences between experimental and control groups in students' attitudes toward scientific inquiry, enjoyment of science lessons, and career interest in physics/science. Furthermore, results showed a significant difference in attitudes perceived in these scales, with males having a more significant effect size than female students in all three scales. The study concludes with implications and suggests recommendations for future research and practice.