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.

Browse

Settings

Start date: 2025Subject: search.filters.subject.machine learning

Search Results

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Improving Atrial Fibrillation Detection with Machine Learning Models
    (IEEE, 2025-06-02)
    Tudjarski, Stojancho
    ;
    ;
    Madevska Bogdanova, Ana
    ;
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Overview of Interpretable and Explainable Artificial Intelligence for Atrial Fibrillation
    (IEEE, 2025-11-25)
    Tudjarski, Stojancho
    ;
    Angjelevska, Ana
    ;
    ;
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Bridging Gaps in Ligand Binding Affinity Prediction: Empirical Machine Learning Analyses
    (IEEE, 2025-06-02)
    Fetaji, Fjolla
    ;
    ;
    Predicting ligand binding affinity remains a critical challenge in computational drug discovery, as existing techniques often require extensive computational resources and are not readily generalizable to diverse protein-ligand systems. This study addresses three key gaps in current research: (1) the lack of versatile, data-efficient predictive models; (2) insufficient strategies for integrating protein and ligand structural information; and (3) limited methods for simultaneously improving accuracy and generalizability. By systematically reviewing recent advances in machine learning-including approaches derived from deep learning, graph-based methodologies, and hybrid frameworks-we show how emerging techniques enable higher predictive power and reduced computational cost. We leverage two large-scale, public datasets (PDBBind and BindingDB) to empirically evaluate a novel dual-model framework that integrates graph-based feature extraction and neural network regression. Comparative analyses illustrate how spatial and sequence representations contribute to model performance, achieving robust improvements in binding affinity prediction. The theory-based advantages of this approach demonstrate how it reveals both small-scale and wide-ranging relationships between proteins and their ligands and the operational benefits result in quicker medication development through decreased processing needs. The research confirms the necessity of building adaptable frameworks which unite structural data with sequence data for therapeutic advancement along clear research paths.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
    (MDPI AG, 2025-05-30)
    Jamil, Mahnoor
    ;
    ;
    ;
    Creutzburg, Reiner
    ;