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patterns, ensuring that decision-making reflects the current state of the patient's health rather than relying on outdated historical data.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.
Authors - Jorge Lituma, Anthony Moya, Remigio Hurtado Abstract - Dementia, a critical global health challenge recognized by the World Health Organization (WHO), affects millions of lives, with more than 50 million cases reported in 2019, a figure projected to double by 2050. Among its forms, Alzheimer’s disease is the most prevalent, underscoring the urgent need for early detection to improve patient outcomes and mitigate societal impact. Leveraging recent advancements in artificial intelligence, this study introduces an innovative deep learning framework aimed at revolutionizing the diagnostic process, providing valuable insights for the scientific community and practical tools for medical professionals. The proposed approach is structured into five key phases: data collection, preprocessing, model training using transfer learning, quality metrics validation including Accuracy, Precision, Recall, and F1-Score—and result interpretation through integrated gradients. A robust dataset of over 40,000 MRI images was utilized, achieving an exceptional accuracy of 99.86% in classifying the stages of Alzheimer’s disease. To ensure interpretability, integrated gradients were employed to highlight critical neuroanatomical markers, such as cortical atrophy and enlarged ventricles, distinguishing patients with dementia from healthy individuals. These findings validate the model’s reliability and demonstrate its potential as an innovative tool for advancing Alzheimer’s diagnosis and care.
Authors - Douglas Amobi Amoke, Yichun Li, Syed Mohsen Naqvi Abstract - Adopting machine learning solutions for monitoring vessel behaviour and surveillance in the maritime domain shows excellent promise. However, significant challenges arise due to the lack of publicly available vessel trajectory data labelled with Automatic Identification System (AIS) information. A new automated system has been proposed to preprocess and label vessel trajectory data collected from AIS at the Port of New York (NY), Blyth Port in Newcastle (NCL), United Kingdom, and a combined dataset called NYCL to address the labelling problem. This automated labelling system functions in three key stages. The first stage involves data collection and processing. The second stage transforms raw AIS data into meaningful vessel trajectory information. The third stage annotates and labels these trajectories, concluding with classification. The processed AIS data create vessel trajectories, with labels automatically generated. Finally, this work explores the classification models to demonstrate the effectiveness of labelled vessel trajectories in various maritime tasks.
Authors - MS Hasibuan, R Rizal Isnanto, Suryatiningsih, Chae Min A, Lee Kyung Min, Park So Hyeong Abstract - This study aims to design and implement a waste bank application to improve waste management efficiency through digital solutions. The application provides a dashboard to track waste collection activities in real-time, displaying data on waste amounts, schedules, and user contributions, enhancing transparency and efficiency. Test results show the system improves waste bank operations by 25% and simplifies waste management reporting.
Authors - Levyta Farah, Nurul Sukma Lestari, Dendy Rosman, Dewi Andriani Abstract - MSMEs (Micro, Small, and Medium Enterprises) and tourism have a very close relationship and support each other. The collaboration between the two has great potential in improving the economy and regional development. Therefore, active collaboration is needed between tourist destinations and MSMEs in the regions to support each other and enhance the quality of tourism in Indonesia. This research investigates the influence of digital innovation and sustainable strategies on MSME performance with the Penta helix as a moderating variable. The population of this research is MSMEs in Tangerang City, with a sample size of 303 respondents. The results of this research are that digital innovation does not affect MSME performance, while sustainability strategy and Penta Helix have a positive effect on MSME performance. This research also shows that Penta Helix can moderate digital innovation and sustainability strategies on performance. This research clarifies the contribution of variables to the growth and sustainability of MSMEs, strengthens their position in the global market, and enables the development of more robust policies and business practices, potentially significantly contributing to overall economic growth and supporting tourism in the Tangerang area.
Authors - Anamika Dhawan, Pankaj Mudholkar Abstract - Precision Agriculture has put in a lot of enhancement in improving agriculture in the last two decades. Plant monitoring is one of the essential applications of Precision Agriculture. In this study, an IoT-based system for rice leaf disease detection that runs on solar power and makes use of integrated machine learning on a Raspberry Pi 4 Model B is presented. In the classification of two important rice diseases, bacterial leaf blight and rice blast, the built custom Convolutional Neural Network (CNN) model, which was translated to TensorFlow Lite (TFLite) format for edge deployment, obtained a remarkable 94.28% accuracy. For scalable, effective disease detection in rice farming, this solar-powered, cost-effective device integrates edge AI and IoT.
Authors - Luka Jovanovic, Aleksandar Petrovic, Milan Tuba, Miodrag Zivkovic, Eva Tuba, Nebojsa Bacanin Abstract - Strong security measures are required due to the growing use of IoT devices and constantly growing network sizes. In order to tackle some of the most important issues in IoT security, this paper investigates the use of optimization metaheuristics in XGBoost hyperparameter tuning. In particular, we suggest a brand-new modified metaheuristic algorithm that is intended to improve diversity throughout the search process and is modeled after the firefly algorithm (FA). Experiments with simulations on a newly released IoT security dataset show how well the proposed optimizer works to enhance model performance. While tackling important issues related to hyperparameter optimization, such as striking a balance between exploration and exploitation, the method achieves a noteworthy accuracy of 0.996853. These findings demonstrate how the suggested approach may strengthen network security by using more accurate predictive modeling, opening the door for scalable and effective IoT systems in progressively complex settings.
