Faculty of Computer Science and Engineering
Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5
The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest
faculties in that field in the region.
The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.
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Item type:Publication, Small Prompts, Big Energy and CO2 Impact: Benchmarking Ollama LLMs on CPU and GPU(IEEE, 2025-11-25) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Overview of Legal Artificial Intelligence Assistants Landscape(IEEE, 2025-11-25); ; ; This survey presents the current landscape of AI legal tools, serving both legal professionals and the general public. It compares existing solutions, while also addressing technological and business challenges that shape their development and use. The findings contribute to a clearer understanding of the role and potential of AI assistants in the legal domain, offering insights relevant to both practitioners and researchers. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards Generating Synthetic EHR Knowledge Graphs — a Probabilistic Approach(GOBLIN COST Action, 2025-06-12); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data(Wiley, 2025-05-27); ; ; ; The integration of big data into nephrology research will open new avenues for analyzing and understanding complex biological datasets, driving advances in personalized management of kidney diseases. This paper describes the multifaceted challenges and opportunities by incorporating big data in nephrology, emphasizing the importance of data standardization, advanced storage solutions, and advanced analytical methods. We discuss the role of data science workflows, including data collection, preprocessing, integration, and analysis, in facilitating comprehensive insights into disease mechanisms and patient outcomes. Furthermore, we highlight predictive and prescriptive analytics, as well as the application of large language models (LLMs) in improving clinical decision‐making and enhancing the accuracy of disease predictions. The use of high‐performance computing (HPC) is also examined, showcasing its role in processing large‐scale datasets and accelerating machine learning algorithms. Through this exploration, we aim to provide a comprehensive overview of the current state and future directions of big data analytics in nephrology, with a focus on enhancing patient care and advancing medical research. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Temperature induced self-healing properties of alginate gelatin hydrogels(Elsevier, 2024-05-15) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sensor-based systems for the measurement of Functional Reach Test results: a systematic review(PeerJ, 2024); Albuquerque, CarlosThe measurement of Functional Reach Test (FRT) is a widely used assessment tool in various fields, including physical therapy, rehabilitation, and geriatrics. This test evaluates a person's balance, mobility, and functional ability to reach forward while maintaining stability. Recently, there has been a growing interest in utilizing sensor-based systems to objectively and accurately measure FRT results. This systematic review was performed in various scientific databases or publishers, including PubMed Central, IEEE Explore, Elsevier, Springer, the Multidisciplinary Digital Publishing Institute (MDPI), and the Association for Computing Machinery (ACM), and considered studies published between January 2017 and October 2022, related to methods for the automation of the measurement of the Functional Reach Test variables and results with sensors. Camera-based devices and motion-based sensors are used for Functional Reach Tests, with statistical models extracting meaningful information. Sensor-based systems offer several advantages over traditional manual measurement techniques, as they can provide objective and precise measurements of the reach distance, quantify postural sway, and capture additional parameters related to the movement. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, CRITICAL OXYGEN SATURATION-LEVEL ESTIMATION FROM PHOTOPLETHYSMOGRAM (PPG): A PRISMA-COMPLIANT SYSTEMATIC REVIEW AND META-ANALYSIS(World Scientific, 2024-10-04); ; ; Sidorenko, MarijaObjectives: Photoplethysmogram (PPG) signals have become a crucial tool in the non-invasive monitoring of oxygen saturation levels (SpO2). The main purpose of the present review is to perform a meta-analysis of the involvement and consideration of critical SpO2 levels (<90%) in the research papers where SpO2 levels are calculated/predicted from PPG and to elaborate on the impact of the critical levels when presenting the evaluation results. Data sources: PubMed, Science Direct, and Scopus were searched for papers published between January 1, 2016, and September 10, 2022. Results: This study produced several results, concerning the main objective as well as other important issues for improving the SpO2 estimation/calculation. We discovered that only 21 out of 75 papers considered SpO2 values that are in the critical domain. Many papers do not provide access to their databases or disclose the software/models used. Additionally, some studies lack sufficient testing subjects and fail to make their results reproducible. The findings reveal a preference for SpO2 calculation over prediction, limited data availability, undisclosed methodologies, and diverse evaluation metrics hinder replication and direct comparisons between studies. Also, a scoring table is offered that scores higher the papers that are more valuable for SpO2 calculation/prediction.Conclusion: Employing PRISMA guidelines, a comprehensive search across PubMed, Science Direct, and Scopus databases initially extracted 6173 potential papers. Following rigorous screening, 75 papers were selected for detailed analysis, of which only 21 included data from critical SpO2 levels. Furthermore, this research provided information for the filtered 21 paper about the sample size of the study participants, the models utilized to derive the results, the availability of databases, the specific devices employed in the research, the methodologies employed for PPG signal measurement, and the collaborative efforts among authors from different institutions. This information is sublimed in the scoring table which gives higher scoring to those papers that are more valuable for SpO2 calculation/prediction. This study offers references to all these findings that can be used as concrete guidelines for prospective researchers and developers of new sensors for SpO2 estimation/calculation utilizing PPG signals. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of Companies using Graph Neural Networks(IEEE, 2024-05-20); Classification of companies into GICS categories can be addressed using Graph Neural Networks (GNN), by utilizing the different types of relationship between companies such as customer, supplier, partner, competitor, and investor. We use the Relato business graph data and compare the performances of several GNNs and a large language model like BERT that is trained only on the descriptions of the companies. Our goal is company classification into its corresponding category within the four tiers of the GICS hierarchy. Several architectures of GNNs are explored such as GCN, GraphSAGE and GAT, but also RGCN and RGAT that consider the edge type, or relationship between the companies. The main purpose is to reveal what kind of relationship between the companies is most valuable when determining the category of the company. The findings indicate that Graph Neural Networks (GNNs) enhance both classification performance and the understanding of collaboration patterns among companies, providing valuable insights for determining the industry in which these companies operate. This contrasts with the classification based solely on company descriptions using BERT. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, On the (WGb,φ; α)− diaphony of the nets of Halton- Zaremba constructed over finite groups(Italian Journal of Pure and Applied Mathematics, 2023-10) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, RDFGraphGen: A Synthetic RDF Graph Generator based on SHACL Constraints(2024-07-25)