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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Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution
(Public Library of Science (PLoS), 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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A systematic review of artificial intelligence applications in education: Emerging trends and challenges
(Elsevier BV, 2025-06)
Matos, Tomás
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Santos, Walter
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Coelho, Paulo Jorge
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Pires, Ivan Miguel
The academic world is becoming increasingly interested in the applications of Artificial Intelligence technology in education. A systematic review examines AI applications in education, focusing on their effectiveness, challenges, and implications. A comprehensive analysis of studies published between 2011 and 2024 encompassed 45 research articles from major databases, such as PubMed Central, IEEE Xplore, Elsevier, Springer, MDPI, ACM, and PMC. The findings highlight the predominant use of generative AI tools like ChatGPT (30%), followed by other advanced technologies, such as GPT-4, machine learning, and virtual reality. Research across global regions, particularly in Canada (18%), the United States (12%), and China (8%), highlights the multifaceted applications of AI in enhancing personalized learning, fostering critical thinking, and supporting professional education. Tools such as ChatGPT have demonstrated strong performance in theoretical knowledge delivery and medical education, while augmented and virtual reality excels in practical skill development. Despite these advances, challenges such as data privacy concerns, algorithmic bias, and the need for specialized educator training remain critical.
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Ten Meter Walk Test for motor function assessment with technological devices based on lower members’ movements: A systematic review
(Elsevier BV, 2025-03)
Santos, Maykol
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Albuquerque, Carlos
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Coelho, Paulo Jorge
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Pires, Ivan Miguel
Objective: The Ten Meter Walk Test (10MWT) is a vital diagnostic tool for identifying neuromuscular and neurodegenerative conditions. This systematic review explores the potential of wearables, mobile devices, and sensors to enhance the 10MWT’s use in medical gait analysis based on lower limb movements. Methods: This systematic review explores the use of wearables, mobile devices, and sensors to improve the 10MWT in medical gait analysis based on lower limb movements. The study uses the PRISMA approach to assess literature from January 2010 to October 2023, highlighting the importance of new technologies like machine learning and artificial intelligence in improving the accuracy and efficiency of the 10MWT. Results: The findings demonstrate how technology-enabled 10MWT can help develop specialized treatment strategies and provide a more accurate understanding of disease pathophysiology. Conclusions: The paper reviews 17 studies on lower limb movements during the 10MWT, highlighting their importance in assessing medical diseases and gait analysis as a diagnostic tool. It emphasizes the role of technology in rehabilitation and physical therapy, where some studies combine Transcranial Direct Current Stimulation with robotic or wearable technologies. Significance: The review comprehensively explains these technologies’ advantages and current use in therapeutic contexts.
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Overcoming technical barriers in healthcare with blockchain: A systematic review
(Elsevier BV, 2026-03)
Costa, Diana
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Coelho, Paulo Jorge
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Albuquerque, Carlos
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Pires, Ivan Miguel
The rising digitalization of healthcare has increased reliance on complex information systems, creating the need for better integration and interoperability. Despite technology developments, healthcare organizations still confront technical challenges that restrict data transmission, scalability, and secure information sharing. This systematic review highlights important technological challenges to healthcare information system integration and examines the potential of blockchain technology to address them. Following PRISMA principles, a structured search of PubMed and Scopus revealed 24 peer-reviewed studies published from 2020 to 2024. The investigation suggests that interoperability restrictions, lack of data and language standardization, scalability challenges, cybersecurity hazards, and insufficient technical expertise are the most significant hurdles. Evidence suggests that blockchain technology can increase data integrity, security, and regulated interoperability through decentralized and permissioned systems. However, obstacles persist involving technical complexity, regulatory compliance, energy consumption, and organizational readiness. This paper highlights current knowledge on technical integration hurdles in healthcare and presents evidence-based insights on the potential role of blockchain in allowing interoperable, safe, and sustainable healthcare information systems.
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Technological methods for sensors’ data analysis for Y-balance test results: A systematic review
(Elsevier BV, 2026-07)
Pimenta, Luís
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Lopes, Mário
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Al-Jumaili, Saif
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Albuquerque, Carlos
Objective To evaluate the impact of sensor integration on YBT outcomes, including data precision, injury risk prediction, real-time monitoring, and athletic performance assessment. A secondary objective is to identify gaps in automated real-time evaluation methods. Methods A systematic review was conducted using a search window covering studies published between 2020 and 2026. After screening and eligibility assessment, the final included studies were published between 2021 and 2025. This review focused on studies that either applied sensor-based or technology-assisted methods directly to Y-Balance Test assessment or used YBT as a functional outcome alongside technology-supported measurement approaches. Results After screening and eligibility assessment, 21 studies met the inclusion criteria and were included in the qualitative and descriptive synthesis. The reviewed studies suggest that technology-assisted approaches can broaden the assessment of Y-Balance Test performance by adding biomechanical, functional, or task-related information beyond conventional manual scoring. Several studies reported improved monitoring of balance-related outcomes or intervention-related changes, but direct evidence for improved measurement precision and formal injury prediction was limited. Conclusions Sensor-assisted approaches in YBT show promising potential to improve measurement objectivity and broaden functional assessment in clinical and athletic settings. However, the current literature does not yet demonstrate a fully automated or real-time YBT system, and further development is required before such applications can be considered established for routine practice. Future progress will require larger and more diverse cohorts, methodological standardization, robust validation procedures, and the development of portable real-time YBT-specific systems suitable for routine implementation. Significance This review contributes a structured evidence map of sensor-assisted YBT research and highlights the gap between existing technology-supported assessment approaches and truly automated, real-time, YBT-specific systems.
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Use of machine learning for predicting stress episodes based on wearable sensor data: A systematic review
(Elsevier BV, 2025-11)
Pataca, António Oseas
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Coelho, Paulo Jorge
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Garcia, Nuno M.
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Deryuck, Margot
Objective: This study consists of a systematic literature review that aims to explore the potential of integrating wearable sensor data and machine learning (ML) techniques for predicting stress episodes. It aims to identify prevalent sensors, key physiological features, and the effectiveness of ML methods in real-world stress monitoring and prediction. Methods: This systematic review follows the PRISMA methodology, analyzing literature from January 2010 to June 2025. Data sources included IEEE Xplore, Elsevier, Springer, Multidisciplinary Digital Publishing Institute (MDPI), and paper repositories such as PubMed Central and the Association of Computing Machinery (ACM). The inclusion criteria encompassed studies that employed wearable devices for ML stress prediction, focusing on physiological data such as heart rate variability (HRV), skin conductance, and sleep patterns. Articles were screened for originality, clinical relevance, and methodological rigor. Results: Key findings highlighted the use of diverse wearable sensors, including electrodermal activity (EDA), photoplethysmography (PPG), and accelerometers. Commonly extracted features included HRV metrics, skin conductance levels, and respiratory patterns. ML models, such as Random Forest (RF), Support Vector Machines (SVM), and deep neural networks (DNN), have demonstrated high predictive accuracy (e.g., up to 99%). Despite promising results, challenges such as small sample sizes, variability in data quality, and the need for standardized protocols were noted. Conclusion: Wearable sensors combined with ML algorithms provide scalable, real-time stress monitoring solutions, emphasizing proactive healthcare management. However, advancing this field requires addressing limitations through interdisciplinary collaboration and focusing on the accessibility and usability of technologies. Significance: This study highlights the transformative role of wearable technologies in predicting stress, with implications for personalized health interventions, mental health support, and enhanced healthcare efficiency.
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Driving Healthcare Monitoring with IoT and Wearable Devices: A Systematic Review
(Association for Computing Machinery (ACM), 2025-06-11)
Baiense, João
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Coelho, Paulo
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Pires, Ivan Miguel
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Velez, Fernando J.
Wearable technologies have become a significant part of the healthcare industry, collecting personal health data and extracting valuable information for real-time assistance. This review article analyzes 35 scientific publications on driving healthcare monitoring with IoT and wearable device applications. These articles were considered in a quantitative and qualitative analysis using the Natural Language Processing framework and the PRISMA methodology to filter the search results. The selected articles were published between January 2010 and May 2024 in one of the following scientific databases: IEEE Xplore, Springer, ScienceDirect (i.e., Elsevier), Association for Computing Machinery (ACM), Multidisciplinary Digital Publishing Institute (MDPI), or PubMed Central. The analysis considers population, methods, hardware, features, and communications. The research highlights that data collected from one or numerous sensors is processed and accessible in a database server for various uses, such as informing professional careers or assisting users. The review suggests that robust and efficient driving healthcare monitoring with IoT and wearable devices applications can be designed considering the valuable principles presented in this review.
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Optimizing document retrieval using massive text embeddings and LLM prompt engineering
(Springer Science and Business Media LLC, 2026-04-14)
Mitrov, Goran
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Stanoev, Boris
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Kampel, Martin
Background The rapid expansion of digital data poses a unique challenge for retrieving relevant and insightful information efficiently. In particular, the increasing volume of scientific publications has made literature reviews time-consuming. The emergence of large language models (LLMs) offers new opportunities to streamline this process. Methods This paper explores the use of generative artificial intelligence (GenAI) for query reformulation and evaluates the performance of nine massive text embedding models, varying in size and fine-tuning strategies, in the context of document retrieval. We apply multiple prompt engineering techniques to evaluate the ability of LLMs to generate effective queries, comparing them with human-crafted queries. These are used to retrieve documents utilizing nine embedding models. The evaluation is across five datasets using metrics such as recall, average precision, and rank-based measures. Results Results show that embedding models fine-tuned for semantic similarity consistently outperform general-purpose models, with UAE Large proving most robust across diverse domains. Furthermore, queries generated using zero-shot and few-shot prompting techniques often surpass the performance of human-formulated queries. Conclusion These findings highlight the value of integrating LLMs and massive text embeddings to reduce manual effort in literature reviews. GenAI provides a reliable starting point for query formulation, with human input reserved for refinement when needed.
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Scoping Review of Technology Enabled Healthcare Integration - Towards Sustainable Care
(IEEE, 2025-06-16)
Loncar-Turukalo, Tatjana
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Madevska Bogdanova, Ana
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Solarevic, Milica
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Lehocki, Fedor
Technology serves as a relevant facilitator in integration of care, supporting healthcare across many pillars, such as diagnoses, drug-development, administration and coordination of services, but as well playing a pivotal role in disease prevention and pervasive health monitoring. This study presents the scoping review of the literature corpus related to technology trends supporting healthcare integration. The study explores 5 digital libraries from major publishers, aiming to identify changes in research trends in the decade from 2014 to 2024, and investigate which implementation challenges, barriers and risks present the major focus in healthcare integration. In total 14721 relevant studies were identified and included in collating and summarizing of the results in this work. The study shows that there is a triple increase in the number of studies from 2020 onwards, mainly focused on technology and implementation challenges. The main topic of the studies shifts from Internet of Medical Things (IoMT) until 2019 to AI in healthcare, which gained its momentum by continual advances in AI performance across different tasks. Enhanced health care, holistic care and patient experience are the most targeted benefits and opportunities of integrated care, while security and privacy of health related data remain the main concerns and playground for further research.
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Benchmarking OpenAI's APIs and Large Language Models for Repeatable, Efficient Question Answering Across Multiple Documents
(Polish Information Processing Society, 2024-10-23)
Filipovska, Elena
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Mladenovska, Ana
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Bajrami, Merxhan
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Dobreva, Jovana
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Hillman, Vellislava

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