Hepatitis C is a significant public health concern, resulting in substantial morbidity and mortality worldwide. Early diagnosis and effective treatment are essential to prevent the disease’s progression to chronic liver disease. Machine learning algorithms have been increasingly used to develop predictive models for various diseases, including hepatitis C. This study aims to evaluate the performance of several machine learning algorithms in diagnosing chronic liver disease, with a specific focus on hepatitis C, to improve the cost-effectiveness and efficiency of the diagnostic process. We collected a comprehensive dataset of 1801 patient records, each with 12 distinct features, from Jordan University Hospital. To assess the robustness and dependability of our proposed framework, we conducted two research scenarios, one with feature selection and one without. We also employed the Sequential Forward Selection (SFS) method to identify the most relevant features that can enhance the model’s accuracy. Moreover, we investigated the effect of the synthetic minority oversampling technique (SMOTE) on the accuracy of the model’s predictions. Our findings indicate that all machine learning models achieved an average accuracy of 83% when applied to the dataset. Furthermore, the use of SMOTE did not significantly affect the accuracy of the model’s predictions. Despite the increasing use of machine learning models in medical diagnosis, there is a growing concern about their interpretability. As such, we addressed this issue by utilizing the Shapley Additive Explanations (SHAP) method to explain the predictions of our machine learning model, which was specifically developed for hepatitis C prediction in Jordan. This work provides a comprehensive evaluation of various machine learning algorithms in diagnosing chronic liver disease, with a particular emphasis on hepatitis C. The results provide valuable insights into the cost-effectiveness and efficiency of the diagnostic process and highlight the importance of interpretability in medical diagnosis.

In Jordanian higher education institutions, a competency exam was developed to ensure that students had the ability to attain particular competence levels. The results of the competency examination are one of the measures used as key performance indicators (KPIs) evaluating the quality of academic programs and universities. There are numerous evaluation methods for students’ performances based on the academic achievement of the pupils, including the application of conventional statistical approaches and machine learning. The objective of this paper is to develop a framework to help decision-makers and universities evaluate academic programs using ML by identifying programs and learning outcomes that need to be established by analyzing competency exam data. The developed framework can also reduce exam costs by substituting machine learning algorithms for the actual execution of the exam. We have created a dataset that can assist academics with their study; the dataset includes demographic and academic data about students, such as their gender, average university degree, type of university, and outcomes on the competency exam based on their level and competencies. Experiments supported the claim that models trained using samples from the student sub-dataset outperform models constructed using samples from the entire dataset. In addition, the experiments demonstrated that ML algorithms are an effective tool for recognizing patterns in student performance. Experiments demonstrated that no single ML model outperforms other ML models. However, the MLP model generates more accurate models, making them more beneficial for developing robust frameworks.

Cloud computing offers many benefits to governments, including increased efficiency, flexibility, and cost savings. However, there are also significant challenges to adopting cloud computing services. In the case of the Jordanian government, some of these challenges include concerns about data security and privacy, lack of technical expertise, limited funding and resources, and cultural resistance to change. This paper examines the challenges faced by the Jordanian government in adopting cloud computing services and evaluates their impact on government institutions. The study collected data from three local telecommunications companies in Jordan to identify potential challenges and assess their significance through a questionnaire. The results indicated challenges that negatively affected cloud adoption, including performance, usability, and cost, as well as challenges that positively impacted adoption. Maintenance and information security challenges were rated as the most significant challenges. The study recommends promoting awareness, offering training programs, and conducting feasibility studies to overcome these challenges and improve cloud adoption. Future research should expand the study sample and investigate additional challenges impacting government organizations’ adoption of cloud computing services.

The goal of this study is to ascertain how Enterprise Resource Planning (ERP) adoption software is affected in small and medium-sized businesses in Jordan by the problems and barriers represented by (low knowledge of ERP, misperception, lack of resources, financial worries, and employee resistance). The following titles of employees in Jordanian small and medium-sized businesses' departments of information technology made up the study population: (Department Manager, Assistant Department Manager, Head of Department, and Software Officer). To accomplish the goals of the study, the investigator employed a basic random sample of the study population, wherein (160) questionnaires were distributed and (152) were retrieved. The descriptiveanalytic approach was employed in conjunction with various statistical techniques, the most notable of which was the "T" test for one sample (One SampleT–test), and the statistical package program was utilized for statistical analysis and testing of hypotheses. Use SPSS. The study's conclusions include the existence of a statistically significant relationship between Jordanian SMEs' software implementation and their lack of resources, financial concerns, and inadequate ERP understanding. On the other hand, misperception and employee resistance had no statistically significant effect on the implementation of software in Jordanian SMEs. In light of this, the study made several recommendations, the most significant of which is the establishment of an independent committee of senior management experts in information systems and information technology to monitor a variety of data regarding the readiness and effectiveness of the software. As well as involving the user in the ERP implementation from the beginning, because operations management may clearly and effectively benefit from the end-user's assistance in formulating operations that accurately and firmly fulfil daily work needs