https://www.um.edu.mt/library/oar/handle/123456789/692 2026-06-24T20:51:25Z 2026-06-24T20:51:25Z https://www.um.edu.mt/library/oar/handle/123456789/147574 2026-06-22T07:46:23Z 2026-05-01T00:00:00Z

Title: Early changes everything : the cost of inaction Abstract: Delivered at the Strengthening Youth Mental Health Conference (2026) by Prof. Marie Briguglio, this presentation outlines the key findings, metrics, and societal implications of the Malta Wellbeing INDEX project. Utilizing a robust framework of data curated across four distinct dashboards, the project tracks both objective and subjective dimensions of quality of life in Malta.

2026-05-01T00:00:00Z Mercieca, Joana Castillo, Daniela https://www.um.edu.mt/library/oar/handle/123456789/147487 2026-06-17T06:25:30Z 2026-06-01T00:00:00Z

Title: Who is to blame when AI fails? Attribution processes in anthropomorphic chatbot interactions Authors: Mercieca, Joana; Castillo, Daniela Abstract: The increasing integration of artificial intelligence (AI)–powered chatbots into digital customer service has fundamentally changed customer–brand interactions. While chatbots enable efficiency, accessibility and 24/7 service provision, their growing human-like design features also raise customer expectations regarding competence, responsibility and accountability. When chatbot failures occur, these heightened expectations may amplify customers’ emotional and behavioural reactions. Despite expanding research on chatbot performance and effectiveness, limited attention has been given to how customers cognitively evaluate and behaviourally respond to chatbot failures, particularly in relation to perceived anthropomorphism. Addressing this gap is essential for advancing service theory in AI-mediated contexts. [excerpt]

2026-06-01T00:00:00Z Alsaadi, Fawaz E. Yasami, Amirreza Volos, Christos Bekiros, Stelios Jahanshahi, Hadi https://www.um.edu.mt/library/oar/handle/123456789/147375 2026-06-12T13:03:18Z 2023-01-01T00:00:00Z

Title: A new fuzzy reinforcement learning method for effective chemotherapy Authors: Alsaadi, Fawaz E.; Yasami, Amirreza; Volos, Christos; Bekiros, Stelios; Jahanshahi, Hadi Abstract: A key challenge for drug dosing schedules is the ability to learn an optimal control policy even when there is a paucity of accurate information about the systems. Artificial intelligence has great potential for shaping a smart control policy for the dosage of drugs for any treatment. Motivated by this issue, in the present research paper a Caputo–Fabrizio fractional-order model of cancer chemotherapy treatment was elaborated and analyzed. A fix-point theorem and an iterative method were implemented to prove the existence and uniqueness of the solutions of the proposed model. Afterward, in order to control cancer through chemotherapy treatment, a fuzzy-reinforcement learning-based control method that uses the State-Action-Reward-State-Action (SARSA) algorithm was proposed. Finally, so as to assess the performance of the proposed control method, the simulations were conducted for young and elderly patients and for ten simulated patients with different parameters. Then, the results of the proposed control method were compared with Watkins’s Q-learning control method for cancer chemotherapy drug dosing. The results of the simulations demonstrate the superiority of the proposed control method in terms of mean squared error, mean variance of the error, and the mean squared of the control action—in other words, in terms of the eradication of tumor cells, keeping normal cells, and the amount of usage of the drug during chemotherapy treatment.

2023-01-01T00:00:00Z Alharbi, Njud S. Jahanshahi, Hadi Yao, Qijia Bekiros, Stelios Moroz, Irene https://www.um.edu.mt/library/oar/handle/123456789/147363 2026-06-11T14:32:17Z 2023-01-01T00:00:00Z

Title: Enhanced classification of heartbeat electrocardiogram signals using a long short-term memory–convolutional neural network ensemble : paving the way for preventive healthcare Authors: Alharbi, Njud S.; Jahanshahi, Hadi; Yao, Qijia; Bekiros, Stelios; Moroz, Irene Abstract: In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes on LSTM’s exceptional sequential data learning capability and CNN’s intricate pattern recognition strength. Advanced signal processing methods are integrated to enhance the quality of raw ECG signals before feeding them into the deep learning model. Experimental evaluations on benchmark ECG datasets demonstrate that our proposed ensemble model surpasses other state-of-the-art deep learning models. It achieves a sensitivity of 94.52%, a specificity of 96.42%, and an accuracy of 95.45%, highlighting its superior performance metrics. This study introduces a promising tool for bolstering cardiovascular disease diagnosis, showcasing the potential of such techniques to advance preventive healthcare.

2023-01-01T00:00:00Z