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, Benchmarking Sentence Encoders in Associating Indicators With Sustainable Development Goals and Targets(Institute of Electrical and Electronics Engineers (IEEE), 2025); ; The United Nations’ 2030 Agenda for Sustainable Development balances the economic, environmental, and social dimension of sustainable development in 17 Sustainable Development Goals (SDGs), monitored through a well-defined set of targets and global indicators. Although essential for humanity’s future well-being, this monitoring is still challenging due to the variable quality of the statistical data of global indicators compiled at the national level and the diversity of indicators used to monitor sustainable development at the subnational level. Associating indicators other than the global ones with the SDGs/targets may help not only to expand the statistical data, but to better align the efforts toward sustainable development taken at (sub)national level. This article presents a model-agnostic framework for associating such indicators with the SDGs and targets by comparing their textual descriptions in a common representation space. While removing the dependence on the quantity and quality of the statistical data of the indicators, it provides human experts with data-driven suggestions on the complex and not always obvious associations between the indicators and the SDGs/targets. A comprehensive domain-specific benchmarking of a diverse sentence encoder portfolio was performed first, followed by fine-tuning of the best ones on a newly created dataset. Five sets of indicators used at the (sub)national level of governance (around 800 indicators in total) were used for the evaluation. Finally, the influence of 40 factors on the results was analyzed using explainable artificial intelligence (xAI) methods. The results show that 1) certain sentence encoders are better suited to solving the task than others (potentially due to their diverse pre-training datasets), 2) the fine-tuning not only improves the predictive performance over the baselines but also reduces the sensitivity to changes in indicator description length (performance drops even by up to 17% for baseline m... - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries(MDPI AG, 2025-07-04) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multiword Discourse Markers Across Languages: A Linguistic and Computational Perspective(Wiley, 2025-04-22) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, AI Agents in Finance and Fintech: A Scientific Review of Agent-Based Systems, Applications, and Future Horizons(Tech Science Press, 2026) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Detection and Recognition of UAVs by Using Deep Learning Techniques(IEEE, 2025-11-25); The use of drones (UAVs) is multi-dimensional in positive aspects, but it is worrying that they are also used by malicious people in negative aspects. Drones can be misused for smuggling, illegal surveillance, or security violations. The development of an anti-drone system is very necessary in order to prevent the aforementioned problems. This paper discusses the construction of an intelligent system that will detect and recognize drones using data provided by optical technology (camera). Based on the obtained results, they show that the use of techniques for dataset preparation and model training is very promising. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, UNVEILING THE NEXT WAVE OF LEARNING: NAVIGATING CHATGPT'S IMPACTFUL APPLICATION IN EDUCATION(IATED, 2024-03) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, UNLOCKING THE FUTURE OF EDUCATION: A COMPREHENSIVE ANALYSIS OF KEY DOCUMENTS SHAPING ARTIFICIAL INTELLIGENCE IN EDUCATION(IATED, 2024-03); Jancheska, SofijaWith the boom of Artificial Intelligence (AI) in the past few years, there has been a notable upswing in the generation of resources dedicated to this field. Prominent international and supranational entities and initiatives, including UNESCO, European Commission, European Parliament, OECD, EdTech, AI4K12 and International Society for Technology in Education, have produced a significant number of documents in the field. This paper provides a comprehensive analysis of carefully selected documents within the field of AI, specifically focusing on its applications in education. The goal is to provide a nuanced exploration encompassing professional expertise, comprehensive guidelines, and practical recommendations to enhance our understanding of AI. Through this detailed study of documents, our objective is to offer valuable insights for the future potential of AI in education. Our paper emphasizes ethical considerations, best practices, and guidelines, useful to educators, policymakers, and stakeholders who seek to integrate AI technologies responsibly and effectively into educational settings. Our paper also aims to promote responsible and beneficial integration of AI in education, with the ultimate goal of enriching learning experiences for students. With our carefully selected documents, we address various aspects, including the promotion of gender equality and human rights, conducting privacy impact assessments, ensuring responsible use of AI in accordance with privacy regulations, and identifying the essential knowledge and skills that students should acquire in the realm of AI. We explore how to equip students with a foundational understanding of AI concepts, ethical considerations, and practical skills, positioning them as informed and responsible users of AI technologies in the future. The document selection spans topics related to integrating AI in education, encompassing challenges, opportunities, standards, data privacy and security considerations, as well as addressing biases and ethical concerns. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identification of HIV Inhibitors Using Graph Neural Networks(IEEE, 2024-05-20); Graph neural networks (GNN), primed to extract knowledge and discover patterns in graph-structured data, have received particularly increased attention in biomedical research. By integrating information from a variety of biomedical knowledge repositories they offer a fast and efficient computational alternative approach to the costly and time-consuming process of drug development and research. The core contributions of this paper include the design and empirical evaluation of several GNN-based models for the identification of potential HIV (Human Immunodeficiency Virus) inhibitors. In particular, the predictive power of model variants based on Graph Attention Network (GAT), Graph Isomorphism Network (GIN), and Continuous Kernel-Based Graph Convolutional Network, specifically developed to handle molecular data, have been investigated. To assess the effectiveness of the proposed models, the Stanford open graph benchmark dataset for molecular data ogbg-molhiv was used. Furthermore, two types of molecular fingerprints have been proposed to augment the molecular representation in the proposed graph neural models, leading to better performance standing compared to the original models. The paper provides a detailed description of the proposed models for identifying HIV inhibitors, followed by a comparative analysis of the experimental results focusing on a discussion of the challenges we face and future research directions that could be investigated. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Graph and Convolutional Methods for Advancing Ligand Binding Affinity Modeling in Drug Research(IEEE, 2025-06-02); Drug discovery increasingly relies on accurate ligand binding affinity modeling to reduce the cost and time spent on trial-and-error experiments. However, existing computational methods often exhibit limited generalization, interpretability, and training efficiency. To address these gaps, we present a novel framework that integrates graph neural networks (GNNs) and convolutional models to model proteinligand interactions. Our approach builds on recent findings that highlight the benefits of representing protein-ligand complexes through graph topologies while capturing spatial and structural features using convolutional layers. We examine two publicly available datasets, PDBbind and BindingDB, both of which exemplify diverse protein-ligand complexes. Comprehensive experiments demonstrate that our integrated GNN-convolutional model improves predictive accuracy, reduces computational costs, and enhances interpretability. In addition, ablation studies reveal the roles of graph structural encoding and convolutional feature extraction in capturing crucial interaction signals. Theoretically, our study augments existing research by providing empirical evidence that unifying graph and convolutional strategies can enrich the insight into topological and spatial representation learning in ligand binding affinity prediction. Practically, the proposed framework can be readily adopted in workflows where large-scale exploration of protein-ligand complexes is required, potentially accelerating early-stage drug discovery by refining virtual screening and lead optimization. This work closes previously identified performance and interpretability gaps, offering a rigorous pathway to future applications in ligand binding affinity modeling. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Autonomous Control and Path Planning of UAV with Deep Reinforcement Learning(IEEE, 2025-06-02); Autonomous unmanned aerial vehicles (UAV) can be applied as a substitution for many manual processes, which results in solutions that are more cost-optimal and less prone to human error. In this paper, we consider a task that requires a quadrotor UAV to reach waypoints from an environment as fast as possible. This work presents various reinforcement learning experiments on the autonomous control and path planning task while exploring the potential of current state-of-the-art algorithms, including the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which addresses the problem of overestimation of value estimates and suboptimal policies commonly present in continuous control domain actor-critic models. The experiments also include different optimization techniques for finding the best set of hyperparameters. We evaluate the trained reinforcement learning agents and provide a detailed comparison and discussion of the results.