/library/oar/handle/123456789/11493 Tue, 04 Nov 2025 02:00:15 GMT 2025-11-04T02:00:15Z /library/oar/handle/123456789/138747 Title: The design, implementation and evaluation of a web-based student teachers' ePortfolio (STeP) Authors: Farrugia, Anthony; Al-Jumeily, Dhiya Abstract: This paper presents the development process of a web-based student teachers' ePortfolio system for the Faculty of Education at the University of Malta. Literature shows that at a higher educational level, a student ePortfolio is becoming an important tool as it is being used to enhance the learning process through constant tutor and peer feedback, self-regulation and reflection. Many ePortfolio applications exist that may help university faculties to collaborate with their students. However, these existing applications concentrate on general ePortfolio content and allow limited flexibility to be tailored to specific structured ePortfolios that is actually needed by the demanding faculty. In our opinion a new tailor-made structured ePortfolio is needed specifically to replace the manual professional development portfolio system. The proposed system will be the official ePortfolio for the Faculty of Education to be used compulsory by students that are reading a bachelors degree in Education with a secondary track at the University of Malta. Therefore we proposed the full lifecycle development of a new web-based student teachers' ePortfolio which we call STeP. A sample of fifteen selected participants, which include the chairperson of the Professional Development Portfolio, an administrator, tutors and students have taken part in different stages of the software development and played an important role in its success. We show all the stages involved that led to the successful implementation of the proposed tailor-made ePortfolio system. We evaluate our system and present a qualitative outcome for its implementation. Sun, 01 Jan 2012 00:00:00 GMT /library/oar/handle/123456789/138747 2012-01-01T00:00:00Z /library/oar/handle/123456789/138746 Title: Medical diagnosis : are artificial intelligence systems able to diagnose the underlying causes of specific headaches? Authors: Farrugia, Anthony; Al-Jumeily, Dhiya; Al-Jumaily, Mohammed; Hussain, Abir; Lamb, David Abstract: Artificial intelligence is the capability of computing machines to perform at par with humans in some cognitive tasks. Since its conception in the 1940s, AI has ambitiously evolved to naturally and comfortably immerse in extraordinary and multidisciplinary fields including computer science, education, engineering and medicine. This survey aims to provide and highlight the importance of AI work in the field of medical informatics and biomedicine. We have reviewed latest AI research in this immense field of medical science with special attention given to medical diagnosis. Various intelligent computing tools from rule-based expert systems and fuzzy logic to neural networks and genetic algorithms used in medical diagnosis were considered. We have explored hydrocephalus, a medical condition causing headaches. We also analysed a prototype of what is known as NeuroDiary Web application that is currently being tested as a software mobile application for collecting data of patients with hydrocephalus. We finally propose the development of an expert mobile application system to assist clinicians in the diagnosis, analysis and treatment of hydrocephalus. Tue, 01 Jan 2013 00:00:00 GMT /library/oar/handle/123456789/138746 2013-01-01T00:00:00Z /library/oar/handle/123456789/137732 Title: Predicting emergency severity index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning Authors: Agius, Stephen; Cassar, Vincent; Magri, Caroline; Khan, Wasiq; Obe, Dhiya Al-Jumeily; Caruana, Godwin; Topham, Luke Abstract: Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward iIntroduction Emergency departments (EDs) are critical for ensuring timely patient care, especially in triage, where accurate prioritisation is essential for patient safety and resource utilisation. Building on previous research, this study leverages a comprehensive dataset of 653,546 ED visits spanning six years from Mater Dei Hospital, Malta. This dataset enables detailed trend analysis, demographic variation exploration, and predictive modelling of patient prioritisation, admission likelihood, and admitting ward. Methods Two predictive models (Stage 1 and Stage 2) were developed using the Extreme Gradient Boosting (XGBoost) algorithm. In Stage 1, predictions were made at the triage level using basic demographic and presenting symptom data. Stage 2 incorporated critical blood test results (e.g., Haemoglobin, C-Reactive Protein, Troponin T, and White Blood Cell Count) alongside the demographic and symptom data from Stage 1 to refine and enhance predictions. Key steps in data preprocessing, such as handling missing values, balancing class distributions with SMOTE, and feature encoding, are discussed. Model evaluation employed comprehensive metrics, including AUC-ROC and calibration curves, to assess both performance and reliability. This enhanced description provides a clear roadmap of the model development process, reinforcing the study’s rigor and contribution to advancing machine learning applications in emergency care. Results The models demonstrated significant predictive capabilities. Key metrics showed improvement between Stage 1 and Stage 2. For example, patient prioritisation accuracy improved from 0.75 to 0.76, admission prediction accuracy rose from 0.80 to 0.82, and admitting ward prediction accuracy increased from 0.80 to 0.86. These enhancements underscore the value of incorporating clinical data to optimise predictions. Discussion The integration of early predictions into ED workflows has the potential to improve patient flow, reduce wait times, and enhance resource allocation. By leveraging XGBoost’s capabilities and integrating both demographic and clinical data, this study provides a robust framework for advancing decision-making processes in triage environments. Conclusions This research demonstrates the efficacy of machine learning models in predicting key ED outcomes, highlighting their potential to transform emergency care through data-driven insights. an emergency department using data-driven machine learning. Wed, 01 Jan 2025 00:00:00 GMT /library/oar/handle/123456789/137732 2025-01-01T00:00:00Z /library/oar/handle/123456789/136530 Title: gSched : a resource aware Hadoop scheduler for heterogeneous cloud computing environments Authors: Caruana, Godwin; Li, Maozhen; Qi, Man; Khan, Mukhtaj; Rana, Omer Abstract: MapReduce has become a major programming model for data-intensive applications in cloud computing environments. Hadoop, an open source implementation of MapReduce, has been adopted by an increasingly wideuser community. However, Hadoop suffers from task scheduling performance degradation in heterogeneouscontexts because of its homogeneous design focus. This paper presents gSched, a resource-aware Hadoopscheduler that takes into account both the heterogeneity of computing resources and provisioning charges in taskallocation in cloud computing environments. gSched is initially evaluated in an experimental Hadoop clusterand demonstrates enhanced performance compared with the default Hadoop scheduler. Further evaluationsare conducted on the Amazon EC2 cloud that demonstrates the effectiveness of gSched in task allocation in het-erogeneous cloud computing environments. Sun, 01 Jan 2017 00:00:00 GMT /library/oar/handle/123456789/136530 2017-01-01T00:00:00Z