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, Small Prompts, Big Energy and CO2 Impact: Benchmarking Ollama LLMs on CPU and GPU(IEEE, 2025-11-25) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Overview of Legal Artificial Intelligence Assistants Landscape(IEEE, 2025-11-25); ; ; This survey presents the current landscape of AI legal tools, serving both legal professionals and the general public. It compares existing solutions, while also addressing technological and business challenges that shape their development and use. The findings contribute to a clearer understanding of the role and potential of AI assistants in the legal domain, offering insights relevant to both practitioners and researchers. - 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, Check for Semantic Segmentation of Remote Sensing Images: Definition, Methods, Datasets and Applications(Springer Nature, 2024-02-26); ; ; Semantic segmentation of remote sensing images is a vital task in the field of remote sensing and computer vision. The goal is to produce a dense pixel-wise segmentation map of an image, where a specific class is assigned to each pixel, enabling detailed analysis and understanding of the Earth's surface. This paper provides an overview of semantic segmentation in remote sensing, starting with a definition of the task and its significance in extracting valuable information from remote sensing imagery. Various methods used for semantic segmentation in remote sensing are discussed, including traditional approaches such as region-based and pixel-based methods, as well as more recent deep learning-based techniques. Next, the paper delves into the available datasets for semantic segmentation of remote sensing images. Many available datasets are reviewed, highlighting their characteristics, including the number of images, image size, number of labels, spatial resolution, format and spectral bands. These datasets serve as valuable resources for training, evaluating, and benchmarking semantic segmentation algorithms in remote sensing applications. Furthermore, the paper highlights the broad range of applications enabled by semantic segmentation in remote sensing, including urban planning, land cover mapping, disaster management, environmental monitoring, and precision agriculture. Overall, this paper serves as a comprehensive guide to semantic segmentation of remote sensing images, providing insights into its definition, methods, available datasets and wide-ranging applications. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Check for Unveiling Insights: Analyzing Application Logs to Enhance Autism Therapy Outcomes(Springer Nature, 2024-02-26); Leveraging advancements in information technology and the inherent interest of children with autism in robots and technology, this study explores the crucial role of analyzing application logs in enhancing therapy experiences for children with autism. By examining these logs, valuable insights can be obtained, enabling performance tracking, evidence-based evaluation, personalization of interventions, and continuous improvement. This will allow us to get more information about children's preferences and behavior even when we are not in direct contact with them, by extending onsite robot therapies to the home environment. This research contributes to the understanding of the transformative power of log analysis and its implications for optimizing therapy experiences and advancing treatment for children with autism. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, AI in Software Testing: Revolutionizing Quality Assurance(IEEE, 2024-11-26); ; Artificial intelligence (AI) is an area of tremendous potential, especially in the software testing domain, where it has changed the dynamics of the process, storms in efficiency, accuracy, and flexibility in a given SDLC. This paper presents findings from recent investigations of AI in the testing and quality assurance focusing on its transformational potential. Particular attention is paid to such issues as automation of testing processes through AI, testing process enhancement, and possible changes in software engineering due to AI implementation. In this paper, various research perspectives have been integrated to reveal the effectiveness of AI in enhancing the perceived quality assurance processes, improving product quality, and adopting principles of agile methodology in today's software development. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Reusable Approach to Network Service Orchestration(2024-12-10); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Survey of Graph Neural Network Architectures in Ligand Binding Affinity Prediction Models(IEEE, 2024-05-20); Ligand affinity prediction plays a pivotal role in drug discovery, influencing the efficiency and success of drug development processes. Traditional methods struggle in accurately capturing the complex interactions within molecular structures, prompting the exploration of advanced techniques such as Graph Neural Networks (GNNs). This paper provides an analysis of GNNs in the context of ligand affinity prediction, exploring their architecture, applications, and potential impact on revolutionizing drug discovery. Our findings suggest that GNNs can offer improvements over traditional computational methods, particularly in handling the dynamic and complex nature of molecular interactions. We highlight innovative GNN architectures that have shown notable success in predicting ligand binding affinities, such as heterogeneous graph representation and attention mechanisms. The implications of these advancements suggest a paradigm shift in drug discovery, where GNNs can lead to more accurate predictions and accelerate the identification of potential drug candidates. This study underscores the transformative potential of GNNs in enhancing predictive accuracy and efficiency in drug development.