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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A Unified Framework for Alzheimer’s Disease Knowledge Graphs: Architectures, Principles, and Clinical Translation
(MDPI, 2025-05-19)
Dobreva, Jovana
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This review paper synthesizes the application of knowledge graphs (KGs) in Alzheimer’s disease (AD) research, based on two basic questions, as follows: what types of input data are available to construct these knowledge graphs, and what purpose the knowledge graph is intended to fulfill. We synthesize results from existing works to illustrate how diverse knowledge graph structures behave in different data availability settings with distinct application targets in AD research. By comparative analysis, we define the best methodology practices by data type (literature, structured databases, neuroimaging, and clinical records) and application of interest (drug repurposing, disease classification, mechanism discovery, and clinical decision support). From this analysis, we recommend AD-KG 2.0, which is a new framework that coalesces best practices into a unifying architecture with well-defined decision pathways for implementation. Our key contributions are as follows: (1) a dynamic adaptation mechanism that adapts methodological elements automatically according to both data availability and application objectives, (2) a specialized semantic alignment layer that harmonizes terminologies across biological scales, and (3) a multi-constraint optimization approach for knowledge graph building. The framework accommodates a variety of applications, including drug repurposing, patient stratification for precision medicine, disease progression modeling, and clinical decision support. Our system, with a decision tree structured and pipeline layered architecture, offers research precise directions on how to use knowledge graphs in AD research by aligning methodological choice decisions with respective data availability and application goals. We provide precise component designs and adaptation processes that deliver optimal performance across varying research and clinical settings. We conclude by addressing implementation challenges and future directions for translating knowledge graph technologies from research tool to clinical use, with a specific focus on interpretability, workflow integration, and regulatory matters.
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RAGCare-QA: A Benchmark Dataset for Evaluating Retrieval-Augmented Generation Pipelines in Theoretical Medical Knowledge
(Cold Spring Harbor Laboratory Press, 2025)
Dobreva, Jovana
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Karasmanakis, Ivana
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Ivanisevic, Filip
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Horvat, Tadej
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Gams, Matjaz
The paper introduces RAGCare-QA, an extensive dataset of 420 theoretical medical knowledge questions for assessing Retrieval-Augmented Generation (RAG) pipelines in medical education and evaluation settings. The dataset includes one-choice-only questions from six medical specialties (Cardiology, Endocrinology, Gastroenterology, Family Medicine, Oncology, and Neurology) with three levels of complexity (Basic, Intermediate, and Advanced). Each question is accompanied by the best fit of RAG implementation complexity level, such as Basic RAG (315 questions, 75.0%), Multi-vector RAG (82 questions, 19.5%), and Graph-enhanced RAG (23 questions, 5.5%). The questions emphasize theoretical medical knowledge on fundamental concepts, pathophysiology, diagnostic criteria, and treatment principles important in medical education. The dataset is a useful tool for the assessment of RAG- based medical education systems, allowing researchers to fine-tune retrieval methods for various categories of theoretical medical knowledge questions.
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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, 2024-12-10)
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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Automating feature extraction from entity-relation models: Experimental evaluation of machine learning methods for relational learning
(MDPI, 2024-04-01)
Stanoev, Boris
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Mitrov, Goran
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Mirceva, Georgina
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With the exponential growth of data, extracting actionable insights becomes resource-intensive. In many organizations, normalized relational databases store a significant portion of this data, where tables are interconnected through some relations. This paper explores relational learning, which involves joining and merging database tables, often normalized in the third normal form. The subsequent processing includes extracting features and utilizing them in machine learning (ML) models. In this paper, we experiment with the propositionalization algorithm (i.e., Wordification) for feature engineering. Next, we compare the algorithms PropDRM and PropStar, which are designed explicitly for multi-relational data mining, to traditional machine learning algorithms. Based on the performed experiments, we concluded that Gradient Boost, compared to PropDRM, achieves similar performance (F1 score, accuracy, and AUC) on multiple datasets. PropStar consistently underperformed on some datasets while being comparable to the other algorithms on others. In summary, the propositionalization algorithm for feature extraction makes it feasible to apply traditional ML algorithms for relational learning directly. In contrast, approaches tailored specifically for relational learning still face challenges in scalability, interpretability, and efficiency. These findings have a practical impact that can help speed up the adoption of machine learning in business contexts where data is stored in relational format without requiring domain-specific feature extraction.
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Exploring Current Challenges on Security and Privacy in an Operational EHealth Information System
(2024)
Denkovski, Viktor
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Stojmenovska, Irena
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Gavrilov, Goce
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Radevski, Vladimir
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Trajkovik, Vladimir
Bearing in mind that patient data is extremely sensitive, it is crucial to establish strong protection when the security and privacy of healthcare data are concerned. Prioritizing data security and privacy is essential for the overall healthcare industry in order to maintain the reliability of electronic healthcare (eHealth) information systems. This study explores the gathered data and information from the surveys and interviews by looking at the security and privacy concerns in using eHealth information technologies. The surveys and interviews were performed on the medical practitioners in N. Macedonia. The main goal is to find out how well-informed are the medical practitioners on the already in-place privacy measures that have been implemented by the medical authorities and to assess their attitudes regarding the need for additional improvements of the system. From the executed interviews, eight healthcare professionals participated in a thorough email interview in order to discover security and privacy issues associated with eHealth systems usage. This information served as the groundwork for administrating an online survey, to which 370 medical practitioners responded from primary and secondary healthcare. The findings emphasize how essential it is to promptly address the system usability concerns on the security and privacy procedures that are implemented when using eHealth technologies.
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Mathematical Foundations and Implementation of CONIKS Key Transparency
(MDPI, 2024-10-24)
Mollakuqe, Elissa
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Dag, Hasan
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This research paper explores the CONIKS key management system’s security and efficiency, a system designed to ensure transparency and privacy in cryptographic operations. We conducted a comprehensive analysis of the underlying mathematical principles, focusing on cryptographic hash functions and digital signature schemes, and their implementation in the CONIKS model. Through the use of Merkle trees, we verified the integrity of the system, while zero-knowledge proofs were utilized to ensure the confidentiality of key bindings. We conducted experimental evaluations to measure the performance of cryptographic operations like key generation, signing, and verification with varying key sizes and compared the results against theoretical expectations. Our findings demonstrate that the system performs as predicted by cryptographic theory, with only minor deviations in computational time complexities. The analysis also reveals significant trade-offs between security and efficiency, particularly when larger key sizes are used. These results confirm that the CONIKS system offers a robust framework for secure and efficient key management, highlighting its potential for real-world applications in secure communication systems.
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On dementia, duties, and daughters. An ethical analysis of healthcare professionals being confronted with conflicts regarding filial duties in informal dementia care
(Frontiers Media SA, 2024-10-01)
Dogan, Vildan
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Taneska, Marija
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Novotni, Gabriela
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Iloski, Svetlana
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Background: Existing literature on moral conflicts that healthcare professionals encounter in dementia care has explored, amongst others, issues related to autonomy, decision-making capacity, privacy, and more. Notably, conflicts related to healthcare professionals who support informal dementia caregiving and who are confronted with family members being overburdened with their care responsibly remains an underexplored topic in the current literature, particularly in the context of Low-and Middle-Income Countries. The present paper introduces such an encounter, presenting an ethical case analysis of a conflict that occurred during a larger research project conducted in North Macedonia. Case to be studied: Due to the absence of formal care services that could have relieved an overburdened family caregiver, healthcare professionals felt compelled to reach out to the uninvolved adult daughters, requesting them to participate in their parents’ care. Wondering about whether their reaching out to the daughters might count as an attempt of pressure and undue interference, professionals conflicted over the appropriateness of their action. This paper follows up on their concern, ethically assessing the professionals’ action. To answer the question on whether the healthcare professionals acted appropriately or not, and to what extent, theories of filial duties are applied, embedding their action in the larger context of dementia care in North Macedonia. Results and conclusion: It is argued that the lack of formal care services in North Macedonia is of utmost relevance to the conflict. Thus, the conclusion is that the ethical inappropriateness of the case is to be located not so much with the action of the healthcare professionals but with the state because of its failure to provide professional care services that allow healthcare professionals to take ethically sound actions to counteract overarching burdens that family members face when providing informal dementia care.
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Applications of homomorphic encryption in secure computation
(F1000 Research Limited, 2024-07-29)
Mollakuqe, Elissa
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Parduzi, Arber
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Rexhepi, Shasivar
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Jakupi, Samir
Background Homomorphic encryption (HE) represents a pivotal innovation in modern cryptography, offering a pathway to secure computation on encrypted data. This paper embarks on a comprehensive exploration of HE's applications, elucidating its transformative potential in bolstering data security and privacy across various domains. Methods The research employs a mixed-methods approach to evaluate HE technologies. Quantitatively, it develops realistic datasets simulating healthcare and financial data, assessing HE's performance in encrypted computations. Various encryption schemes are rigorously tested for efficiency and accuracy under different conditions. Qualitatively, insights from expert interviews and case studies of HE implementations provide additional context on practical challenges and strategic benefits. Results The simulations and analyses showcase the efficiency, scalability, and security of HE techniques in diverse scenarios. The empirical evidence validates the real-world applicability of HE, demonstrating its versatility and efficacy in secure computation outsourcing, privacy-preserving data analysis, and secure multi-party computation. Conclusions This research paper highlights the transformative power of homomorphic encryption, advocating for its widespread adoption and integration. By bridging the gap between theoretical understanding and practical implementation, the paper contributes to advancing secure computation practices, addressing contemporary challenges in data security and privacy amidst evolving cybersecurity threats and the increasing ubiquity of sensitive data. In essence, this research serves as a beacon of insight into the future of data confidentiality and integrity, promoting HE as a crucial tool for revolutionizing the landscape of data security and privacy in an interconnected world.
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Comparative analysis of identity management, access control, and authorization practices in public and private universities
(2024-07-29)
Mollakuqe, Elissa
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Background This research delves into the critical aspects of identity management, access control, and authorization practices within the domains of public and private universities. Identity management involves the meticulous management and control of user identities, encompassing the establishment and maintenance of user profiles, role assignments, and access privileges. Access control is the practice of defining and enforcing policies that govern who can access an IT system or application and which resources they can interact with. Authorization, meanwhile, determines the specific actions and privileges granted to users based on their roles and permissions. Methods To understand the variances in identity management and access control approaches, we conducted a comparative analysis between public and private universities. Our investigation scrutinized the user populations with access to university systems, the enforcement of access limitations, authentication methods, and password policies. Additionally, we examined the nuances of authorization processes, levels of authorization, access approval authorities, user status and role changes, unique user account management, account deletion procedures, user authentication methods, password complexity and expiration policies, password storage methods, and session termination policies. Results This study revealed that both public and private universities prioritize these security measures, with a common categorization of these processes. Nevertheless, there exist disparities, such as the inclusion of contractors and vendors in the user population at private universities, the manual deletion of user accounts in private institutions, and variations in password policies and storage methods. Private universities tend to enforce stricter password policies, employ more secure password storage methods, and implement automatic session termination features. Conclusions This research provides valuable insights into the practices and approaches adopted by public and private universities to safeguard their digital environments. The findings serve as a valuable resource for enhancing identity management, access control, and authorization protocols, enabling institutions to fortify their cybersecurity defenses in an ever-evolving threat landscape.
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North Macedonia interprofessional dementia care (NOMAD)–personalized care plans for people with dementia and caregiver psychoeducation delivered at home by interprofessional teams
(Frontiers Media SA, 2024-04-10)
Novotni, Gabriela
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Taneska, Marija
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Fischer, Julia
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Iloski, Svetlana
Introduction: The increasing number of people living with dementia and its burden on families and systems particularly in low- and middle-income countries require comprehensive and efficient post-diagnostic management. This study aimed to explore the acceptability and efficacy of a multi-professional case management and psychoeducation model (North Macedonia Interprofessional Dementia Care, or NOMAD) delivered by mobile teams for people with dementia and their caregivers in North Macedonia. Method: We conducted a two-arm randomized controlled trial comparing the intervention with treatment as usual. Participants were recruited from 12 general practitioner (GP) offices in the Skopje region. The NOMAD intervention included the delivery of a personalized care plan over four home visits to dyads of people with dementia and their caregivers by a team including a dementia nurse and a social worker, in collaboration with GPs and dementia experts, and the introduction of a caregiver manual. We assessed caregivers' depressive symptoms, burden, and quality of life and the neuropsychiatric symptoms, daily living activities, and service utilization of people with dementia at baseline and follow-up; we also assessed the acceptability of the intervention by analyzing case notes and attendance rates. Results: One hundred and twenty dyads were recruited and randomized to either the control (n = 60) or the intervention group (n = 60). At follow-up, caregivers in the intervention group had, on average, scores that were 2.69 lower for depressive symptoms (95% CI [−4.75, −0.62], p = 0.012), and people with dementia had, on average, 11.32 fewer neuropsychiatric symptoms (95% CI [−19.74, −2.90], p = 0.009) and used, on average, 1.81 fewer healthcare services (95% CI [−2.61, −1.00], p < 0.001) compared to the control group. The completion of the home visits was 100%, but the intervention's acceptability was underpinned by relationship building, GP competencies, and resources to support families with dementia. There were no differences in the caregivers' quality of life and burden levels or daily living activities in people with dementia. NOMAD is the first case management, non-pharmacological, and multi-professional intervention tested in North Macedonia. Discussion: The trial showed that it is effective in reducing caregivers' depressive symptoms and neuropsychiatric symptoms in people with dementia and the burden on health and social care services, and it is acceptable for families. Implementing NOMAD in practice will require building primary care capacity and recognizing dementia as a national priority.

Faculty of Computer Science and Engineering

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