https://www.um.edu.mt/library/oar/handle/123456789/11493 Wed, 15 Apr 2026 20:23:48 GMT 2026-04-15T20:23:48Z https://www.um.edu.mt/library/oar/handle/123456789/144778 Title: How social imbalance and governance quality shape policy directives for energy transition in the OECD countries? Authors: Sinha, Avik; Bekiros, Stelios; Hussain, Nazim; Nguyen, Duc Khuong; Khan, Sana Akbar Abstract: In line with the COP26 Summit objectives, this paper develops a policy framework to achieve energy transition by considering the social imbalances and regulatory effectiveness. A new energy transition index is proposed. It is an output-side indicator based on the energy ladder hypothesis. This index enables to apprehend the energy transition scenario in any country by capturing (a) the transition to a cleaner energy source, and (b) the transition to more energy efficient sources. Using the two-step System GMM approach and data for 37 OECD countries over the 2000–2019 period, the dynamic and extreme marginal impacts of energy transition drivers with respect to estimates of the model parameters are analyzed. The results show that the social imbalance dampens the positive impacts of energy transition drivers, whereas governance quality helps in augmenting those impacts. The outcomes, drawn from a scenario-based policy design approach, are particularly helpful in advancing potential policy discourse. They have important practical implications for the development of the SDG-oriented policy framework, with special focus on the attainment of the SDG 7 and 13. Sun, 01 Jan 2023 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/144778 2023-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/144573 Title: A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization Authors: Lahmiri, Salim; Bekiros, Stelios; Avdoulas, Christos Abstract: The valuation of house prices is drawing noteworthy attention due to worldwide financial and real estate crises in the last decade. Therefore, there is an immediate need to design more effective predictive systems of house prices. Indeed, investors, creditors, and governments are all interested in such predictive systems to improve their buying and lending decisions and activities. This study explores the application of artificial intelligence, machine learning, and nonlinear statistical models to house price prediction problems. In that order, we use boosting ensemble regression trees, support vector regression, and Gaussian process regression. Bayesian optimization is implemented in a ten-fold cross-validation framework to determine their respective optimal kernels and parameter values. Four performance metrics are used to evaluate the prediction ability of each predictive system. The experimental results showed that boosting ensemble regression trees performed the best, followed by Gaussian process regression and support vector regression. In addition, all three aforementioned predictive systems outperformed artificial neural networks and multi-variate regression employed in recent work on the same data set. Under this perspective, it is concluded that boosting ensemble regression trees are clear candidates to be considered for operational house price prediction in Taiwan. Sun, 01 Jan 2023 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/144573 2023-01-01T00:00:00Z https://www.um.edu.mt/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 https://www.um.edu.mt/library/oar/handle/123456789/138747 2012-01-01T00:00:00Z https://www.um.edu.mt/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 https://www.um.edu.mt/library/oar/handle/123456789/138746 2013-01-01T00:00:00Z