10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
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Authors - Jiahui Yu, Simon Fong, Jinan Fiaidhi, Sabah Mohammed, Richard Millham, Alexandre Lobo, Seon-Phil Jeong, Liansheng Liu Abstract - . Clinical pathways play a vital role in managing diabetes treatment, incorporating medical strategies customized to individual patient needs. In the case of insulin-dependent diabetes mellitus (IDDM), accurately determining insulin dosage and administration timing is essential for regulating blood glucose levels effectively. While general health guidelines exist, they lack personalization. The interactions between medication, lifestyle, and patient conditions are complex, with therapy patterns differing among patients. This paper introduces a combined data stream mining method with fuzzy rule generation to create realtime decision guidelines as clinical pathways for managing IDDM. These guidelines are derived from daily medication records and personal blood glucose trends, generated from continuously updated health data instead of relying on past records. Fuzzy rules are preferred for their ability to personalize treatment, handle complex interactions, adapt in real-time, and provide accurate, timely decisions. They allow for personalization by adapting to individual patient conditions, providing tailored insulin dosages and timings based on real-time data. This method effectively manages the complex interactions between medication, lifestyle, and patient conditions, which can vary greatly among patients. Additionally, fuzzy rules are generated from continuously updated health patterns, ensuring that decision-making reflects the current state of the patient's health rather than relying on outdated historical data. This flexibility accommodates fluctuations in glucose levels due to various factors, making the approach more responsive to both short-term and long-term medical effects. Ultimately, using fresh data leads to more accurate and timely decisions, crucial for maintaining appropriate blood glucose levels in IDDM patients. A computer-based simulation is provided to assess the most appropriate data stream algorithms for this task.
Authors - Assia Boukhamla, Tamer Abderrahmane Lafia, Nabiha Azizi, Samir Brahim Belhaouari Abstract - The high prevalence of cardiovascular diseases (CVDs) worldwide requires accurate diagnostic imaging, particularly through magnetic resonance imaging (MRI). The framework includes preprocessing for region-of-interest segmentation via ViTs, followed by PTQ to reduce model size while maintaining segmentation accuracy Using a small calibration dataset, we apply PTQ to compress the ViT, significantly reducing storage requirements and latency without compromising precision. Experimental results indicate that Float16 quantization achieves an optimal balance between compression rate and segmentation accuracy, demonstrating the feasibility of ViTs for real-time applications.
Authors - Cristinel Gabriel RUSU, Simona MOLDOVANU, Nilanjan DEY, Luminita MORARU Abstract - We are interested in exploring different visual patterns by training ma-chine learning (ML) classifiers on raw and foreground images for MI detection, which has been less studied. In this work, we train machine learning classifiers on raw images (containing background lines) and clear images (containing just foreground/object with background lines removed). Two ECG record datasets containing normal (N) and myocardial infarction (MI) data are analysed via high-level features provided by standard 12-lead ECG signals. Only the limb lead I was cropped from the 12-lead signals to generate the input data. Data augmenta-tion was used for a balanced dataset to prevent overfitting while maintaining the required spatiotemporal invariances for a correct diagnosis. The newly generated ‘clear’ dataset results show that the proposed model achieves high classification performance for the AD, KNN, and RF models, with accuracies that are 32.1%, 27.3%, and 18.5% higher than those of their ‘raw’ counterparts, respectively. These results prove the robustness of the model.
Authors - Volodymyr Kulivnuk, Oleksandr Hladkyi, Alexander Gertsiy, Tetiana Tkachenko, Tetiana Shparaga, Tetiana Mykhailenko, Ihor Vynnychenko, Kateryna Postovitenko, Rostislav Semeniuk Abstract - The development of natural and artificial information systems (IS) in medicine and health tourism is explored. The essence of information resources (IR) and information processes (IP) and their role in medical treatment and health tourism services is investigated. The structural model of the body information resources is substantiated. The information processes occurring in the human body are described. The informaciology model of the information accumulation in the human memory is proposed. The structural model of building principles of human body functional systems (FS) as well as the informaciology model of the human body FS are systemized. The informaciology model of formation of adap-tive results of the human activity is proposed. The natural and artificial infor-mation systems usage in medicine and health tourism is substantiated. The struc-tural models of the natural and artificial information systems are observed. The informaciology resources of artificial IS are explored. The structure of informaci-ology technologies in artificial IS is defined. The structural models of cybernetic systems and artificial (preformed) therapeutic ones are determined.
Authors - Niyantra Mohan Babu, K.Vijayan, Alekhya Devi Malepati Abstract - Autism spectrum disorder (ASD) is a developmental condition that affects social communication and behavioral intelligence. Many live out their lives in ignorance due to misdiagnosis or a lack of awareness. Individuals with high-functioning autism or Asperger’s Syndrome are hard to detect using a single method of detection because not every child will show the same symptoms. For example, a child with Asperger’s Syndrome with good eye contact but without social communication skills would be hard to detect using only their gaze points. This paper explores a method for the diagnosis of ASD through the integration of three models: gaze point tracking, quantitative behavioral checklist, and image processing. The eye-tracking technology pinpoints the coordinates of the gaze points and connects them to the training to detect whether the subject has ASD. Studies on this subject have shown discrepancies between detected and actual individuals with autism, as not all autistic children have an irregular gaze. (Yaneva et al. 2020) Insights into the child’s behavioral patterns are offered through Q-CHAT, which quantitatively categorizes the actions of the child. However, the Q-CHAT checklist is one-dimensional and differs in result as the child grows. (Howard et al. 2022) A pre-trained CNN VGG 16 model identifies traits in the children’s facial features as for the image processing model. But, not all individuals with autism have a facial structure that belies their disorder. (Anitha et al. 2024) This paper addresses all the problems through the novel integrated approach of ASD detection using multiple methods.
Authors - Vasileios E. Papageorgiou, Dimitrios-Panagiotis Papageorgiou, Georgios Petmezas, Pan-telis Dogoulis, Nicos Maglaveras, George Tsaklidis Abstract - This study presents a computationally efficient Convolutional Neural Network (CNN) enhanced with transfer learning for medical image classifica-tion. The method was rigorously tested on 3 tumor datasets: brain MRI, and lung and kidney CT scans. It leverages a pre-trained CNN on brain MRI images, fine-tuned with minimal re-training for the CT scans, achieving high classification accuracy. Transfer learning allows the model to adapt to cancer-specific features by utilizing insights from large datasets. Re-training on each tumor type using only 20 epochs, can deliver significant classification performance, demonstrating the method's efficiency. The CNN's computational efficiency ensures it is both accurate and scalable, making it suitable for use in resource-constrained environ-ments. This research highlights the potential of low-complexity deep learning (DL) to accelerate cancer diagnosis while balancing accuracy and efficiency. It shows that complex deep learning models are not always necessary, and optimal performance can be achieved with lower computational costs.
