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.

The Web of Things (WoT) is a concept that aims to create a network of intelligent devices capable of remote monitoring, service provisioning, and control. Virtual and Physical Internet of Things (IoT) gateways facilitate communication, processing, and storage among social nodes that form the social Web of Things (SWoT). Peripheral IoT services commonly use device data. However, due to the limited bandwidth and processing power of edge devices in the IoT, they must dynamically alter the quality of service provided to their connected clients to meet each user's needs while also meeting the service quality requirements of other devices that may access the same data. Consequently, deciding which transactions get access to which Internet of Things data is a scheduling problem. Edge-cloud computing requires transaction management because several Internet of Things transactions may access shared data simultaneously. However, cloud transaction management methods cannot be employed in edge-cloud computing settings. Transaction management models must be consistent and consider ACIDity of transactions, especially consistency. This study compares three implementation strategies, Edge Host Strategy (EHS), Cloud Host Strategy (CHS), and Hybrid BHS (BHS), which execute all IoT transactions on the Edge host, the cloud, and both hosts, respectively. These transactions affect the Edge hosts as well. An IoTT framework is provided, viewing an Internet of Things transaction as a collection of fundamental and additional subtransactions that loosen atomicity. Execution strategy controls essential and additional subtransactions. The integration of edge and cloud computing demonstrates that the execution approach significantly affects system performance. EHS and CHS can waste wireless bandwidth, while BHS outperforms CHS and EHS in many scenarios. These solutions enable edge transactions to complete without restarting due to outdated IoT data or other edge or cloud transactions. The properties of these approaches have been detailed, showing that they often outperform concurrent protocols and can improve edge-cloud computing.

Software component selection is critical in software engineering due to its vital role in reducing software development cost and time. This study analyzes software component selection research studies on methodologies, criteria, and multi-criteria decision-making (MCDM) techniques. The key study findings are: first, comprehensive standardized criteria for software component selection are lacking, with ambiguous terminology used in research. Second, current ad hoc selection processes need streamlining to reduce time, cost, and effort. Thus, an integrated approach is required to aid decision-makers. The review suggests developing automated tools or decision support systems combining multiple criteria decision methods to improve selection accuracy and efficiency. Standardized criteria catalogs can also assist software developers in the evaluation. The findings highlight that despite extensive academic research, component selection in practice remains sub-optimal. By informing future research and tool development, this review can benefit practitioners to systematically select the most appropriate software components meeting software requirements.