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, Air Pollution Forecasting Using CNN-LSTM Deep Learning Model(IEEE, 2021-09-27) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Bridging Gaps in Ligand Binding Affinity Prediction: Empirical Machine Learning Analyses(IEEE, 2025-06-02); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hate Speech on Social Platforms through the Application of ML and NLP Methods(IEEE, 2024-05-20) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, COVID-19 Fake News Detection by Using BERT and RoBERTa models(IEEE, 2022-05-23) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of Protein Structures Using Deep Learning Models(IEEE, 2022-05-23); ; Protein molecules are very important in the organisms because they participate in different processes. Understanding the way they interact in the cells is of high importance. In order to understand that, solving the task of protein classification could be really helpful thus providing valuable knowledge about the similar proteins that belong to same class. In this paper we focus on solving the task of protein classification. First, we extract some features of the proteins thus obtaining feature vectors, and then by using deep learning architecture, we create prediction model that could be used for classifying protein structures. We present some experimental results of the obtained classification models. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Few-Shot Semantic Segmentation in Remote Sensing: A Review on Definitions, Methods, Datasets, Advances and Future Trends(MDPI AG, 2026-02-18); ; Semantic segmentation in remote sensing images, which is the task of classifying each pixel of the image in a specific category, is widely used in areas such as disaster management, environmental monitoring, precision agriculture, and many others. However, traditional semantic segmentation methods face a major challenge: they require large amounts of annotated data to train effectively. To tackle this challenge, few-shot semantic segmentation has been introduced, where the models can learn and adapt quickly to new classes from just a few annotated samples. This paper presents a comprehensive review of recent advances in few-shot semantic segmentation (FSSS) for remote sensing, covering datasets, methods, and emerging research directions. We first outline the fundamental principles of few-shot learning and summarize commonly used remote-sensing benchmarks, emphasizing their scale, geographic diversity, and relevance to episodic evaluation. Next, we categorize FSSS methods into major families (meta-learning, conditioning-based, and foundation-assisted approaches) and analyze how architectural choices, pretraining strategies, and inference protocols influence performance. The discussion highlights empirical trends across datasets, the behavior of different conditioning mechanisms, the impact of self-supervised and multimodal pretraining, and the role of reproducibility and evaluation design. Finally, we identify key challenges and future trends, including benchmark standardization, integration with foundation and multimodal models, efficiency at scale, and uncertainty-aware adaptation. Collectively, they signal a shift toward unified, adaptive models capable of segmenting novel classes across sensors, regions, and temporal domains with minimal supervision. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Processing MIMIC-III for Evaluation of Various Blood Pressure Estimation Models(2024); ;