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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Author: search.filters.author.Gushev, MarjanStart date: 2025

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    Improving Atrial Fibrillation Detection with Machine Learning Models
    (IEEE, 2025-06-02)
    Tudjarski, Stojancho
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    Madevska Bogdanova, Ana
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    Stankovski, Aleksandar
    In this paper, we focus on feature engineering to detect Atrial Fibrillation by determining whether the heart rhythm has irregularities without patterns. We experiment with a broad spectrum of features derived from the duration of heartbeat-to-heartbeat intervals in the benchmark electrocardiogram databases MIT-BIH Arrhythmia Database, MIT-BIH Atrial Fibrillation Database, and Long Term AF Database. The experiments included position-based features, fluctuation indices, standard deviations, mean average values, Shannon entropy, and statistical measures, such as compressed time series data length. The research questions are to detect the most influential features that result in the best-performing model and the impact of the training dataset. Our approach is to evaluate the model on a completely different dataset from the one it was trained on. Achieved F1 scores vary between 62,32% and 85.08%. The results prove that positional features increase the model's performance.
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    Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs
    (IEEE, 2025-06-02)
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    Petrovski, Nikola
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    Training large language models requires extensive processing, made possible by many high-performance computing resources. This study compares multi-node and multi-GPU environments for training large language models using electrocardiogram data. It provides a detailed mapping of current frameworks for distributed deep learning in multi-node and multi-GPU settings, including Horovod from Uber, DeepSpeed from Microsoft, and the built-in distributed capabilities of PyTorch and TensorFlow. We compare various multi-GPU setups for different dataset configurations, utilizing multiple HPC nodes independently and focusing on scalability, speedup, efficiency, and overhead. The analysis leverages HPC infrastructure with SLURM, Apptainer (Singularity) containers, CUDA, PyTorch, and shell scripts to support training workflows and automation. We achieved a sub-linear speedup when scaling the number of GPUs, with values of 1.6x for two GPUs and 1.9x for four GPUs.
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    Efficient Time-Series Heart Rate Variability Metrics in C++
    (IEEE, 2025-06-02)
    Shekerov, A.
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    Angjelevska, A.
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    Time-Series Heart Rate Variability is an essential set of metrics in modern medicine and cardiology. Algorithms for HRV calculations exhibit quadratic time complexity in the worst cases, as metrics are calculated at many pre-defined time intervals with different widths and offsets, especially for patients with potentially week-long recordings. In this paper, we aim to develop an efficient software solution for electrocardiograms from wearable sensors, which requires careful preprocessing and filtering steps to eliminate noise and erroneous values, detect heartbeats, and classify arrhythmia. This paper describes the utilized method and presents an efficient C++ implementation. We evaluate efficiency by comparing the new solution with the existing Python implementation and conclude that the new implementation performs exceptionally better, with a median speedup of 81.4 for single-day patient recordings and an average speedup of 11.9 for multi-day patient recordings. The results are presented for an extensive set of preprocessed patient databases, discussing the benefits and drawbacks.
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    Benchmarking Functions Versus Containers for Compute-Intensive Workloads
    (IEEE, 2025-11-25)
    Krajchevska, Evgenija
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    Platform choices for compute-intensive jobs are often made without a disciplined, workload-specific comparison. This paper examines two dominant execution models, Functions-as-a-Service and Containers-as-a-Service, using a simple CPUbound kernel. We implement functionally equivalent services on both models, vary input sizes and resource allocations, and exercise sequential and bursty request patterns at comparable resource tiers to characterize performance, cost trade-offs, and scaling behavior. The study provides a controlled, reproducible protocol for comparing the models on compute-bound tasks and reports how memory and concurrency influence latency. The methodology is extensible, meaning the kernel can be swapped for other algorithms while preserving the measurement pipeline.
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    Scalability Analysis of SaaS Infomatrix ERP
    (IEEE, 2025-11-25)
    Ordanoski, Petar
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    his paper presents the implementation and performance evaluation of scalability in the Infomatrix ERP system, a true cloud-based SaaS developed by Infoproject LLC. Through architectural analysis and performance testing, the system demonstrates horizontal scalability. A shared database supports up to 5,000 concurrent users while maintaining 3 -second response times. Results confirm efficient scaling, cost reduction, and the effectiveness of true SaaS architecture in enterprise environments.
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    Overview of Interpretable and Explainable Artificial Intelligence for Atrial Fibrillation
    (IEEE, 2025-11-25)
    Tudjarski, Stojancho
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    Angjelevska, Ana
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    Madevska Bogdanova, Ana
    Accurate and interpretable detection of irregular work of the heart, such as atrial fibrillation, from electrocardiogram (ECG) signals is crucial for timely diagnosis and effective patient management. While machine learning (ML) models, particularly deep learning architectures, have achieved state-of-the-art performance in ECG arrhythmia classification, their black-box nature limits clinical adoption. This paper explores explainable artificial intelligence (XAI) techniques applicable to ML models trained on ECG data, highlighting both global and local interpretability approaches. We provide an overview of posthoc methods, including SHAP, LIME, PFI, and LIG, among others, treating various types of ECG recordings. As a practical case study, we present our findings analyzing the results of PFI and LIG methods applied to a transformer-based model fine-tuned for atrial fibrillation detection and explain its decision process. Our findings underscore the value of integrating XAI into ECG analysis pipelines to enhance transparency, foster clinician trust, and support more informed decision-making in cardiovascular diagnostics.
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    Deep Learning Image Embeddings for Ventricular Beat Classification
    (IEEE, 2025-11-25)
    Tudjarski, Stojancho
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    Petrovski, Nikola
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    Ventricular arrhythmias pose significant risks to cardiovascular health, necessitating accurate detection from electrocardiogram signals. This study investigates the effectiveness of vector embeddings derived from various convolutional neural networks and contrastive language-image pretraining-based models for classifying ventricular heartbeats presented as images. A comparative analysis revealed that the combination of the ResNet50 model and support vector machines achieved the top F1 score of 79.30%. The results provide insights into how deep neural network models can produce effective vector embeddings of heartbeats to train heartbeat classification models.
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    Transformer-based heart language model with electrocardiogram annotations
    (Springer Science and Business Media LLC, 2025-02-14)
    Tudjarski, Stojancho
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    Kanoulas, Evangelos
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    Small Prompts, Big Energy and CO2 Impact: Benchmarking Ollama LLMs on CPU and GPU
    (IEEE, 2025-11-25)
    Kolovska, Ana
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    Energy efficiency is a crucial challenge when deploying Large Language Models (LLMs). Electricity usage and related CO2 emissions can differ greatly depending on model architecture, parameter size, prompt length, and inference hardware. In this study, we evaluate 31 popular Ollama models across CPU and GPU inference, resulting in 60 testing scenarios. Energy and carbon metrics were gathered using the NVML and CodeCarbon libraries, providing insights into the environmental impact of LLM inference in data center settings.
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    An Overview of Legal Artificial Intelligence Assistants Landscape
    (IEEE, 2025-11-25)
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    Kostov, Alen
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    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.