https://www.um.edu.mt/library/oar/handle/123456789/692 Fri, 12 Jun 2026 19:43:23 GMT 2026-06-12T19:43:23Z https://www.um.edu.mt/library/oar/handle/123456789/147375 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. Sun, 01 Jan 2023 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/147375 2023-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147363 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. Sun, 01 Jan 2023 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/147363 2023-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147284 Title: Logistics cooperation in the far-east : prioritizing supply chain requirements to strengthen intra regional integration of maritime transport networks Authors: D’agostini, Enrico; Deselnicu, Dana; Bezzina, Frank; Rosiello, Antonietta Abstract: Logistics cooperation among countries is an important policy tool with potential for strengthening the international competitiveness and economic growth of regions. In the Far-East region, joint efforts to increase the coordination of maritime logistics activities have undergone since 2006 between the governments of China, South Korea and Japan. This paper aims at finding a potential gap in past and current logistics cooperation requirements for the Far-east region to provide suitable logistics policies to be implemented in the region. In the first step of the study, past joint statements of China, Republic of Korea and Japan’s ministerial conference on transport and logistics were examined. Thereafter, a text mining methodology was applied to highlight key areas of logistics and derive related network patterns by analyzing the degree centrality and the community betweenness of the most frequent terms. Secondly, a q-methodology was utilized to analyse whether new priorities areas should be discussed among governments to increase logistics cooperation in the region. A gap analysis between logistics trends derived from text mining and current trends derived from the Q-method led to suggest that there is a gap between past and current logistics requirements and particularly in the areas of environment, technology, standardization and adoption of best practices. Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/147284 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/147281 Title: The role of artificial intelligence in the competitiveness of Maltese small & medium businesses Authors: Rosiello, Antonietta; Castillo, Daniela; Sciberras, Gerald Abstract: This pilot study investigates the role of Artificial Intelligence (AI) in enhancing the competitiveness of Maltese small and medium-sized businesses (SMBs), focusing on its potential to mitigate the liability of smallness and the factors influencing its adoption across sectors. A sequential mixed-methods approach was employed. The first stage involved qualitative semi-structured interviews with 15 decision-makers from Maltese SMBs across different sectors, generating insights into AI adoption drivers and barriers. These findings informed a second-stage quantitative survey, which yielded 127 valid responses which provided broader empirical support for the identified patterns. The analysis was guided by the Resource-Based View (RBV), Dynamic Capabilities (DC), and Situated AI theoretical lenses. The findings highlight a clear sectoral divide, with more digitally mature service-oriented firms demonstrating higher levels of AI adoption than firms in production and distribution sectors. A central paradox emerges: while AI can act as a resource amplifier capable of mitigating structural constraints, its adoption is often hindered by the same limitations it seeks to address. Leadership mindset and foundational digital maturity are identified as critical enablers. This study provides context-specific insights into AI adoption within a small-state economy and offers practical implications for SMB leaders, policymakers, and technology providers. Thu, 01 Jan 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/147281 2026-01-01T00:00:00Z