https://www.um.edu.mt/library/oar/handle/123456789/5581 Sat, 29 Aug 2026 07:42:22 GMT 2026-08-29T07:42:22Z https://www.um.edu.mt/library/oar/handle/123456789/148653 Title: An ensemble of long short-term memory networks with an attention mechanism for upper limb electromyography signal classification Authors: Alotaibi, Naif D.; Jahanshahi, Hadi; Yao, Qijia; Mou, Jun; Bekiros, Stelios Abstract: Advancing cutting-edge techniques to accurately classify electromyography (EMG) signals are of paramount importance given their extensive implications and uses. While recent studies in the literature present promising findings, a significant potential still exists for substantial enhancement. Motivated by this need, our current paper introduces a novel ensemble neural network approach for time series classification, specifically focusing on the classification of upper limb EMG signals. Our proposed technique integrates long short-term memory networks (LSTM) and attention mechanisms, leveraging their capabilities to achieve accurate classification. We provide a thorough explanation of the architecture and methodology, considering the unique characteristics and challenges posed by EMG signals. Furthermore, we outline the preprocessing steps employed to transform raw EMG signals into a suitable format for classification. To evaluate the effectiveness of our proposed technique, we compare its performance with a baseline LSTM classifier. The obtained numerical results demonstrate the superiority of our method. Remarkably, the method we propose attains an average accuracy of 91.5%, with all motion classifications surpassing the 90% threshold. Sun, 01 Jan 2023 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/148653 2023-01-01T00:00:00Z 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