Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques. Modern academic institutions operate in a highly competitive and complex environment. Analyzing performance, providing high-quality education, strategies for evaluating the students’ performance, and future actions are among the prevailing challenges universities face. Student intervention plans must be implemented in these universities to overcome problems experienced by the students during their studies. In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various Machine Learning (ML) techniques are used to understand and overcome the underlying challenges; predicting students at risk and students drop out prediction. Moreover, most studies use two types of datasets: data from student colleges/university databases and online learning platforms. ML methods were confirmed to play essential roles in predicting students at risk and dropout rates, thus improving the students’ performance.

Predicting protein complexes from protein-protein interaction data is becoming a fundamental problem in computational biology. The identification and characterization of protein complexes implicated are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. On the other hand, large datasets of experimentally detected protein-protein interactions were determined using High-throughput experimental techniques. However, experimental data is usually liable to contain a large number of spurious interactions. Therefore, it is essential to validate these interactions before exploiting them to predict protein complexes. In this paper, we propose a novel graph mining algorithm (PEWCC) to identify such protein complexes. Firstly, the algorithm assesses the reliability of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient. To demonstrate the effectiveness of the proposed method, the performance of PEWCC was compared to several methods. PEWCC was able to detect more matched complexes than any of the state-of-the-art methods with higher quality scores. The higher accuracy achieved by PEWCC in detecting protein complexes is a valid argument in favor of the proposed method. The datasets and programs are freely available at http://faculty.uaeu.ac.ae/nzaki/Research.htm .

Background: Precision medicine, targeting treatments to individual genetic and clinical profiles, faces challenges in data collection, costs, and privacy. Generative AI offers a promising solution by creating realistic, privacy-preserving patient data, potentially revolutionizing patient-centric healthcare. Objective: This review examines the role of deep generative models (DGMs) in clinical informatics, medical imaging, bioinformatics, and early diagnostics, showcasing their impact on precision medicine. Methods: Adhering to PRISMA guidelines, the review analyzes studies from databases such as Scopus and PubMed, focusing on AI's impact in precision medicine and DGMs' applications in synthetic data generation. Results: DGMs, particularly Generative Adversarial Networks (GANs), have improved synthetic data generation, enhancing accuracy and privacy. However, limitations exist, especially in the accuracy of foundation models like Large Language Models (LLMs) in digital diagnostics. Conclusion: Overcoming data scarcity and ensuring realistic, privacy-safe synthetic data generation are crucial for advancing personalized medicine. Further development of LLMs is essential for improving diagnostic precision. The application of generative AI in personalized medicine is emerging, highlighting the need for more interdisciplinary research to advance this field.

Chest radiography is a significant diagnostic tool used to detect diseases afflicting the chest. The automatic detection techniques associated with computer vision are being adopted in medical imaging research. Over the last decade, several remarkable advancements have been made in the field of medical diagnostics with the application of deep learning techniques. Various automated systems have been proposed for the rapid detection of pneumonia from chest X-rays. Although several algorithms are currently available for pneumonia detection, a detailed review summarizing the literature and offering guidelines for medical practitioners is lacking. This study will help practitioners to select the most effective and efficient methods from a real-time perspective, review the available datasets, and understand the results obtained in this domain. It will also present an overview of the literature on intelligent pneumonia identification from chest X-rays. The usability, goodness factors, and computational complexities of the algorithms employed for intelligent pneumonia identification are analyzed. Additionally, this study discusses the quality, usability, and size of the available chest X-ray datasets and techniques for coping with unbalanced datasets. A detailed comparison of the available studies reveals that the majority of the applied datasets are highly unbalanced and limited, providing unreliable results and rendering methods that are unsuitable for large-scale use. Large-scale balanced datasets can be obtained via smart techniques, such as generative adversarial networks. Current literature has indicated that deep learning-based algorithms achieve the best results for pneumonia classification with an accuracy of 98.7%, a sensitivity of 0.99, and a specificity of 0.98. The higher accuracy offered by deep-learning algorithms in addition to their appropriate class balancing techniques serves as a good reference for further research.

A novel coronavirus (COVID-19) has taken the world by storm. The disease has spread very swiftly worldwide. A timely clue which includes the estimation of the incubation period among COVID-19 patients can allow governments and healthcare authorities to act accordingly. to undertake a review and critical appraisal of all published/preprint reports that offer an estimation of incubation periods for COVID-19. This research looked for all relevant published articles between the dates of December 1, 2019, and April 25, 2020, i.e. those that were related to the COVID-19 incubation period. Papers were included if they were written in English, and involved human participants. Papers were excluded if they were not original (e.g. reviews, editorials, letters, commentaries, or duplications). COVID-19 Open Research Dataset supplied by Georgetown’s Centre for Security and Emerging Technology as well as PubMed and Embase via Arxiv, medRxiv, and bioRxiv. A data-charting form was jointly developed by the two reviewers (NZ and EA), to determine which variables to extract. The two reviewers independently charted the data, discussed the results, and updated the data-charting form. Screening was undertaken 44,000 articles with a final selection of 25 studies referring to 18 different experimental projects related to the estimation of the incubation period of COVID-19. The majority of extant published estimates offer empirical evidence showing that the incubation period for the virus is a mean of 7.8 days, with a median of 5.01 days, which falls into the ranges proposed by the WHO (0–14 days) and the ECDC (2–12 days). Nevertheless, a number of authors proposed that quarantine time should be a minimum of 14 days and that for estimates of mortality risks a median time delay of 13 days between illness and mortality should be under consideration. It is unclear as to whether any correlation exists between the age of patients and the length of time they incubate the virus.