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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    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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    Parallelism in Signature Based Virus Scanning with CUDA
    (Springer International Publishing, 2019)
    Dimitrioski, Andrej
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    Gusev, Marjan
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    Calculating average shortest path length using Compute Unified Design Architecture (CUDA)
    (IEEE, 2019-05)
    Petrushevski, Stefan
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    Gusev, Marjan
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    Efficiently Running SQL Queries on GPU
    (IEEE, 2018-11)
    Dojchinovski, Dimitri
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    Gusev, Marjan
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    How CUDA Powers the Machine Learning Revolution
    (IEEE, 2018-11)
    Ilievski, Andrej
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    Gusev, Marjan
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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.