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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Start date: 2020Language: search.filters.language.English

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    Comparing the Use of Simu5G and 5G‐Sim‐V2I/N Modules When Analysing the Edge Computing Resource Management Efficiency
    (Wiley, 2025-04-29)
    Bernad, Cristina
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    Gilly, Katja
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    Thomas, Nigel
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    Roig, Pedro Juan
    Performance analysis of smart edge computing orchestration algorithms should be done using a realistic urban simulation environment wherein mobile users are accessing their edge services using a readily available 5G network. In this paper, we investigate the influence of using two different 5G simulation frameworks, which are provided as readily available possibilities to model the access network used to deliver edge computing services. The results show that although both frameworks aim to implement the 5G specifications and are deemed suitable choices for simulating a 5G smart city vehicular environment, there can be significant differences in the obtained macro results. The analysis of the simulation results from two identical studies where the only change is the choice of a 5G simulation framework shows that the obtained average end-to-end edge service latency as perceived by edge users can differ up to more than 21 times. The choice of 5G simulation framework is also reflected in the overall generated workload for the edge computing orchestration leading to over 25% more migrations when using 5G-Sim-V2I/N compared to Simu5G.
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    Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
    (MDPI AG, 2025-05-30)
    Jamil, Mahnoor
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    Creutzburg, Reiner
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    RDFGraphGen: An RDF Graph Generator Based on SHACL Shapes
    (Springer Nature (Singapore), 2026-04-01)
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    Vecovska, Marija
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    Jakubowski, Maxime
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    Hose, Katja
    Developing and testing modern RDF-based applications often requires access to RDF datasets with certain characteristics. Unfortunately, it is very difficult to publicly find domain-specific knowledge graphs that conform to a particular set of characteristics. Hence, in this paper we propose RDFGraphGen, an open-source RDF graph generator that uses characteristics provided in the form of SHACL (Shapes Constraint Language) shapes to generate synthetic RDF graphs. RDFGraphGen is domain-agnostic, with configurable graph structure, value constraints, and distributions. It also comes with a number of predefined values for popular schema.org classes and properties, for more realistic graphs. Our results show that RDFGraphGen is scalable and can generate small, medium, and large RDF graphs in any domain.
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    Small Prompts, Big Energy and CO2 Impact: Benchmarking Ollama LLMs on CPU and GPU
    (IEEE, 2025-11-25)
    Kolovska, Ana
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    Gusev, Marjan
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    Mileski, Dimitar
    Energy efficiency is a crucial challenge when deploying Large Language Models (LLMs). Electricity usage and related CO2 emissions can differ greatly depending on model architecture, parameter size, prompt length, and inference hardware. In this study, we evaluate 31 popular Ollama models across CPU and GPU inference, resulting in 60 testing scenarios. Energy and carbon metrics were gathered using the NVML and CodeCarbon libraries, providing insights into the environmental impact of LLM inference in data center settings.
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    An Overview of Legal Artificial Intelligence Assistants Landscape
    (IEEE, 2025-11-25)
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    Kostov, Alen
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    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.
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    Towards Generating Synthetic EHR Knowledge Graphs — a Probabilistic Approach
    (GOBLIN COST Action, 2025-06-12)
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    Milenkova, Eva
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    Jakubowski, Maxime
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    Hose, Katja
    Advances in medical AI and data analytics require large amounts of patient data. Due to privacy concerns, such data is not always available. Synthetic data generation promises a solution to provide the required data despite privacy restrictions. In this paper, we therefore introduce SynMedRDF, an open-source tool to generate synthetic Electronic Health Records. It ensures clinical accuracy by using real-world probabilities and correlations. The data is output as an RDF knowledge graph, enabling structure- and semantics-aware sharing, linking, and analysis.
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    Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data
    (Wiley, 2025-05-27)
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    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.
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    Temperature induced self-healing properties of alginate gelatin hydrogels
    (Elsevier, 2024-05-15)
    Greco, Immacolata
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    Vadakken Gigimon, Anet
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    Varon, Carolin
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    Madevska Bogdanova, Ana
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    Iorio, S.Carlo
    One of the main challenges in tissue engineering is developing constructs that restore damaged tissues. This has led to the growth of classes of materials with tuneable mechanical and absorption properties. Among others, hydrogels are particularly fascinating because they can be functionalized to self-repairdamage like the native living tissues. This work proposes an improved thermo-responsive alginate-gelatine (SA-Gel) hydrogel capable of self-repairing, whose mechanical properties are enhanced by the addition of optimal concentration of graphene oxide (GO). The initial results show that the novel hydrogel’s formulation improves self- healing and mechanical properties making them a potential candidate for biomedical applications.
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    Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution
    (Public Library of Science (PLoS), 2024)
    Kostadinov, Martin
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    Coelho, Paulo Jorge
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    Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. Historical air quality measurement data were gathered from various local sensors positioned at different sites in Skopje, along with data on meteorological conditions from publicly available APIs. Various implementations and hyperparameters of several deep learning models were compared. Additionally, an analysis was conducted to assess the influence of urban traffic on air and noise pollution, leveraging the COVID-19 lockdown periods when traffic was virtually non-existent. The outcomes suggest that the proposed models can effectively predict air pollution. From the urban traffic perspective, the findings indicate that car traffic is not the major contributing factor to air pollution.
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    Sensor-based systems for the measurement of Functional Reach Test results: a systematic review
    (PeerJ, 2024)
    Francisco, Luís
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    Duarte, João
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    Godinho, António Nunes
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    Albuquerque, Carlos
    The 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.