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, An analytical review of optimization techniques in information retrieval for enhanced decision support(Elsevier BV, 2025-12); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring the Educational Potential of Virtual Reality and Mixed Reality: Immersive Learning, Student Engagement, and Knowledge Retention(Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia, 2025-12); ; The integration of Virtual Reality (VR) and Mixed Reality (MR) technologies in education presents new opportunities for immersive and interactive learning. This paper reviews recent applications of VR/MR in educational contexts, emphasizing their impact on student engagement, cognitive development, and knowledge retention. The analysis highlights key benefits such as enhanced motivation, improved practical skills, and effective visualization of abstract content, while also acknowledging limitations including cognitive load and motion sickness. In addition to the literature review, the grounds of an experimental study are presented. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Using the BBC Micro:bit in Educational Settings: Recommendations for N. Macedonia(Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia, 2025-12); ; Atanaskoski, ZivkoThe integration of the BBC Micro:bit into educational settings has been gaining momentum across various countries due to its potential to foster computational thinking, digital literacy, and hands-on learning. This paper examines the role of the Micro:bit in enhancing STEM education through case studies from Slovakia, Sweden, and the UK. The study explores teaching approaches, technical considerations, student engagement, pedagogical insights, challenges, and cultural contexts, offering valuable insights into the effectiveness of the BBC Micro:bit in different educational environments. Further, based on the findings, we propose a set of recommendations for N. Macedonia. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Testing Strategy for Multi-Tenant Web Applications Using TestContainers with Use Case: MealMatrix(2025-11); This paper addresses the problem of missing a standardized approach for verifying the architectural setup of multi-tenant applications by offering a testing strategy that covers scenarios of common problems that multitenant applications face (divided in 3 areas: data isolation, data integrity and constraints, tenant context). Using TestContainers to create a replica of a production environment, with a big bang integration testing approach, we showcase the practical usage of the proposed testing strategy with a Spring Boot and Kotlin web application for managing meal orders – MealMatrix. The results show that the testing approach is effective in identifying faulty setup for multi-tenant environments, with a limitation that TestContainers does not cover an easy-setup for the multiple databases, multiple schemas model with a single instance of the application serving multiple tenants. This work contributes to the field of software testing by offering an easily applicable, high-level testing approach for multi-tenant web applications. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data-Driven Adaptive Course Framework - Case Study: Impact on Success and Engagement(MDPI, 2025-07-19) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gender Impact on Performance in Adaptive Learning Settings: Insights from a Four-Year University Study(MDPI, 2025-06-16) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Using Peer-Learning and Game-Based Instruction for Achieving Long-Lasting Knowledge of Cybersecurity in Primary Schools(Institute of Electrical and Electronics Engineers (IEEE), 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Blood Oxygen Saturation Estimation Using PPG Signals from the MIMIC-III Database(Springer, Cham, 2025-04-23); Madevska Bogdanova, AnaPhotoplethysmogram (PPG) signals are pivotal in cardiovascular monitoring, offering real-time insights into heart rate and oxygen saturation (SpO2). This study explores the creation of deep learning and machine learning models - specifically Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, Recurrent Neural Networks (RNNs), and Random Forest Regressors (RFRs)-to estimate SpO2 levels from single-channel PPG data. Another point is developing algorithms for using the data sourced from the PhysioNet MIMIC-III database. The patients used for training and testing are distinct, ensuring no overlap between the datasets and enabling rigorous model evaluation. A comprehensive analyses reveal that LSTM-based model achieve significant accuracy in SpO2 estimation, with R-squared value reaching up to 0.59. Specifically, the LSTM model demonstrated an MAE of 1.26, MSE of 3.11 and RMSE of 1.76. These results demonstrate the potential of machine learning techniques in advancing clinical monitoring and decision-making processes within critical care environments, thereby enhancing patient care outcomes.