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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Author: search.filters.author.Kitanovski, DimitarStart date: 2020

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    Exploring the Potential of Topological Data Analysis for Explainable Large Language Models: A Scoping Review
    (Zenodo, 2026)
    Sekuloski, Petar
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    Kitanovski, Dimitar
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    Large language models (LLMs) have become central to modern artificial intelligence, yet their internal decision-making processes remain difficult to interpret. As interest grows in making these models more transparent and reliable, topological data analysis (TDA) has emerged as a promising mathematical approach for exploring their structure. This scoping review maps the current landscape of research where TDA tools—such as persistent homology and Mapper—are used to examine LLM components like attention patterns, latent representations, and training dynamics. By analyzing topological features across layers and tasks, these methods provide new ways to understand how language models generalize, respond to unfamiliar inputs, and shift under fine-tuning. The review also considers how TDA-based techniques contribute to broader goals in interpretability and robustness, especially in detecting hallucinations, out-of-distribution behavior, and representational collapse. Overall, the findings suggest that TDA offers a rigorous and versatile framework for studying LLMs, helping researchers uncover deeper patterns in how these models learn and reason.
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    Network-based diversification of stock and cryptocurrency portfolios
    (2024-08-21)
    Kitanovski, Dimitar
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    Stojkoski, Viktor
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    Maintaining a balance between returns and volatility is a common strategy for portfolio diversification, whether investing in traditional equities or digital assets like cryptocurrencies. One approach for diversification is the application of community detection or clustering, using a network representing the relationships between assets. We examine two network representations, one based on a standard distance matrix based on correlation, and another based on mutual information. The Louvain and Affinity propagation algorithms were employed for finding the network communities (clusters) based on annual data. Furthermore, we examine building assets’ co-occurrence networks, where communities are detected for each month throughout a whole year, and then the links represent how often assets belong to the same community. Portfolios are then constructed by selecting several assets from each community based on local properties (degree centrality), global properties (closeness centrality), or explained variance (Principal component analysis), with three value ranges (max, med, min), calculated on a minimal spanning tree or a fully connected community sub-graph. We explored these various strategies on data from the S&P 500 and the Top 203 cryptocurrencies with a market cap above 2M USD in the period from Jan 2019 to Sep 2022. Moreover, we study in more detail the periods of the beginning of the COVID-19 outbreak and the start of the war in Ukraine. The results confirm some of the 1previous findings already known for traditional stock markets and provide some further insights, while they reveal an opposing trend in the crypto-assets market.
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    Cryptocurrency Portfolio Diversification Using Network Community Detection
    (IEEE, 2022-12-22)
    Kitanovski, Dimitar
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    As of the end of 2013 till now we are witnessing huge volatility and risk in the cryptocurrency market compared to flat currency or stock market. Thus, in this market the portfolio diversification is of big importance in order to reduce volatility and keep the optimal return for the investors. A usual approach for portfolio construction is to keep a balance between returns and volatility, based on their interdependence and individual returns. One way of diversification is employing clustering or community detection algorithms to select a more diverse set of assets. We study the utilization of the Louvain algorithm and affinity propagation for community detection, based on correlation and mutual information between cryptocurrencies, for potential application in portfolio diversification.
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    Cryptocurrency Portfolio Diversification Using Network Community Detection
    (IEEE, 2022-11-15)
    Kitanovski, Dimitar
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    ;
    ;
    As of the end of 2013 till now we are witnessing huge volatility and risk in the cryptocurrency market compared to flat currency or stock market. Thus, in this market the portfolio diversification is of big importance in order to reduce volatility and keep the optimal return for the investors. A usual approach for portfolio construction is to keep a balance between returns and volatility, based on their interdependence and individual returns. One way of diversification is employing clustering or community detection algorithms to select a more diverse set of assets. We study the utilization of the Louvain algorithm and affinity propagation for community detection, based on correlation and mutual information between cryptocurrencies, for potential application in portfolio diversification.
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    Implementation of a Cloud-Based Personal Health System for Cross-Border Collaboration
    (ICT inovations, 2021)
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    Jolevski, Ilija
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    Blazeska-Tabakovska, Natasha
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    Bocevska, Andrijana
    This paper presents a brief overview of the concepts of collaboration, communication, data exchange and challenges for a patients’ centric health information system, simultaneously used in two different countries for crossborder citizens. The system intends to create a Personal Health Record (PHR) for participants (patients, doctors, pharmacists) that includes the patient’s current and past health status, prescriptions and referrals. Using these data, we can contribute to creating a better and higher-quality health service system in crossborder regions. The electronic prescription (E-Prescription) and the electronic referral (E-Referral) are the important points in the process of digitization of the cross-border PHR system. Their transformation from written to electronic form creates digitized records that can enable the treatment of the patients in another participating country. The main goal of the software system presented in this paper is to enable cross-border collaboration in the healthcare domain between two neighboring countries: North Macedonia and Greece. Both countries have their own health record systems. In this paper, we address the challenges in communication and synchronization between the created webPHR systems for the Cross4all project.
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    Implementation of a Cloud-Based Personal Health System for Cross-Border Collaboration
    (ICT inovations, 2021-09)
    ;
    Jolevski, Ilija
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    Blazheska Tabakovska, Natasha
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    Bocevska, Andrijana
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    Kitanovski, Dimitar
    This paper presents a brief overview of the concepts of collaboration, communication, data exchange and challenges for a patients’ centric health information system, simultaneously used in two different countries for crossborder citizens. The system intends to create a Personal Health Record (PHR) for participants (patients, doctors, pharmacists) that includes the patient’s current and past health status, prescriptions and referrals. Using these data, we can contribute to creating a better and higher-quality health service system in crossborder regions. The electronic prescription (E-Prescription) and the electronic referral (E-Referral) are the important points in the process of digitization of the cross-border PHR system. Their transformation from written to electronic form creates digitized records that can enable the treatment of the patients in another participating country. The main goal of the software system presented in this paper is to enable cross-border collaboration in the healthcare domain between two neighboring countries: North Macedonia and Greece. Both countries have their own health record systems. In this paper, we address the challenges in communication and synchronization between the created webPHR systems for the Cross4all project.