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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Methodology for food prices forecasting
(IEEE, 2023-12-15)
Peshevski, Dimitar
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Todorovska, Ana
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Trajkovikj, Filip
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Hristov, Nikola
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Trajanoska, Milena
Fluctuations in food prices play a pivotal role in maintaining economic equilibrium and influencing the very fabric of our everyday lives. This paper presents a comprehensive framework for modeling and analyzing food price trends in 12 select European countries, spanning from January 2013 to January 2023, utilizing advanced state-of-the-art Machine Learning techniques. To achieve this objective, historical price data and technical indicators are incorporated into the proposed XGBoost model alongside a baseline model. The model results are assessed using various measures, and a benchmark is established. Notably, the average achieved R2 for predicting food prices within the time frame from January 2020 to January 2022 is 0.85 and 0.64 from January 2021 to January 2023. The findings reveal the efficacy of the proposed model, providing valuable insights into food price forecasting model interpretability and laying the groundwork for further research, including exploration into areas such as food fraud, food sustainability, and other pertinent topics in food economics.
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Assessing the Environmental Impact of Plant-Based Diets: A Comprehensive Analysis
(IEEE, 2023-12-15)
Golubova, Blagica
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Fetaji, Fjola
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Dobreva, Jovana
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Trajanoska, Milena
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Todorovska, Ana
This study examines a pressing issue related to the loss of natural resources and biodiversity driven by the high reliance of food production on ecosystem management services. The well-being of all living species is impacted by this depletion, which represents a huge obstacle in our collaborative effort to improve environmental quality. Our research aims to explain the environmental effects of food production and raise awareness of pollution levels at various phases of this process. This research combines statistical analysis and visualization to show considerable differences in CO2eq emissions among 43 different food products. In particular, it highlights how animal-based diets have much higher emissions than their plant-based equivalents. Subsequently, the products were divided into three distinct groups: plant-based, animal-based, and refined oils and sugars. This demonstrated how well an unsupervised clustering technique separates food products according to their CO2eq emissions. Where, these findings highlight how excellent plant-based products are for the environment. The main goal of this study goes beyond simple observation since it aims to provide an example of how a comprehensive, health-conscious eating habit may live with a stable ecosystem and clean surroundings. Particularly, reductions in cane sugar production yield substantial reductions in CO2 emissions, whereas even marginal decreases in meat production result in a significant reduction in emissions. These results highlight the potential for sustainable eating habits to aid in environmental conservation and deepen our understanding of the complex interactions between dietary decisions and environmental effects.
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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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A comprehensive study of food prices and food fraud in the European Union
(IEEE, 2023-12-15)
Trajkovikj, Filip
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Todorovska, Ana
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Peshevski, Dimitar
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Nakova, Lina
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Trajanoska, Milena
This research delves into the intricate dynamics of food pricing and fraud within European Union member countries. We analyze the complex interplay between food categories and countries, unraveling unique pricing trends and potential anomalies. By computing inflation-adjusted expected prices and sourcing real prices, we gain a deep understanding of inflation’s impact on actual food costs. Our multi-level analyses, network-based approaches, and cluster maps provide a global perspective, revealing international correlations in food pricing and fraud. The significance of our findings lies in setting the groundwork for understanding food fraud, informing strategies for fraud prevention, consumer protection, and, ultimately, food sustainability. Our work serves as a crucial resource for policymakers, economists, and consumers, emphasizing the importance of data-driven decision-making and transparency in the ever-evolving landscape of the European food market.
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RoBERTa for URL Classification: Enhancing Web Security and Content Filtering
(IEEE, 2023-11-21)
Ilievska, Joana
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Mihajloska Trpcheska Hristina
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Dobreva, Jovana
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The rising occurrence of malicious URLs on the internet is a major concern for users and their devices. To combat this problem, there is a need to develop effective methods to protect against these threats. The use of artificial intelligence (AI) presents an opportunity to leverage this technology to tackle this issue. This study proposes a URL classification model that uses RoBERTa transformer, an AI-based natural language processing technique, to classify URLs based on their intent. The model’s performance has been evaluated using various metrics, showcasing the potential of AI to assist in the creation of robust web security and online content filtering tools.
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AI Act Compliance Within the MyHealth@EU Framework: Tutorial
(JMIR Publications Inc., 2025-11-10)
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Dobreva, Jovana
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Bukovec, Djansel
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Gjorgjioski, Blagojche
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A Unified Framework for Alzheimer’s Disease Knowledge Graphs: Architectures, Principles, and Clinical Translation
(MDPI, 2025-05-19)
Dobreva, Jovana
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This review paper synthesizes the application of knowledge graphs (KGs) in Alzheimer’s disease (AD) research, based on two basic questions, as follows: what types of input data are available to construct these knowledge graphs, and what purpose the knowledge graph is intended to fulfill. We synthesize results from existing works to illustrate how diverse knowledge graph structures behave in different data availability settings with distinct application targets in AD research. By comparative analysis, we define the best methodology practices by data type (literature, structured databases, neuroimaging, and clinical records) and application of interest (drug repurposing, disease classification, mechanism discovery, and clinical decision support). From this analysis, we recommend AD-KG 2.0, which is a new framework that coalesces best practices into a unifying architecture with well-defined decision pathways for implementation. Our key contributions are as follows: (1) a dynamic adaptation mechanism that adapts methodological elements automatically according to both data availability and application objectives, (2) a specialized semantic alignment layer that harmonizes terminologies across biological scales, and (3) a multi-constraint optimization approach for knowledge graph building. The framework accommodates a variety of applications, including drug repurposing, patient stratification for precision medicine, disease progression modeling, and clinical decision support. Our system, with a decision tree structured and pipeline layered architecture, offers research precise directions on how to use knowledge graphs in AD research by aligning methodological choice decisions with respective data availability and application goals. We provide precise component designs and adaptation processes that deliver optimal performance across varying research and clinical settings. We conclude by addressing implementation challenges and future directions for translating knowledge graph technologies from research tool to clinical use, with a specific focus on interpretability, workflow integration, and regulatory matters.
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Analysis of Long COVID Phenotypes and their Impact on Mental Health and Daily Functioning: Insights from Twitter
(Belgrade: Institute of molecular genetics and genetic engineering, 2023)
Markovikj, Marko
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Dobreva, Jovana
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Lucas, Mary
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Vodenska, Irena
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Chitkushev, Lou
In this study, we conducted an investigation into Long COVID from a user perspective, utilizing Twitter social media data. Prior to analysis, the data underwent preprocessing to obtain raw text per tweet. Our analysis commenced with basic statistical analysis and subsequently expanded to identify characteristic periods for the phenotypes based on dynamic timelines. We also explored the relationships between the phenotypes, as well as the interdependence between phenotypes and geolocation. In the context of this research, an analysis was conducted on a collection of tweets that encompassed the timeframe from March 2020 to March 2022. The dataset consisted of approximately 1.9 million tweets. In order to concentrate on word phrases, extraneous elements such as mentions, emoticons, links, and hashtags were eliminated. Subsequently, a process of lemmatization was performed. For the purpose of reducing the number of distinct phenotypes under investigation and facilitating the presentation of results, the collected data was categorized into five overarching groups: Cardiovascular, Respiratory, Daily Living, Neurological and Mental Health, and Other. The statistical data regarding the most commonly used words by individuals describing their experiences during the Long COVID period are as follows: “Ampicillin” was tweeted 125,295 times, “Death” was tweeted 121,156 times, “Suffer” was tweeted 125,113 times, and “Vaccine” was tweeted 108,968 times. We observe distinct patterns in the emergence of certain phenotypes during this period, particularly in relation to the quality of life. On August 1, 2020, the term “quality of life” was mentioned in only 223 tweets, whereas one year later, during the same month, this phenotype garnered 1,663 tweets. Our findings reveal that the occurrence of Long COVID phenotypes is influenced by both temporal and geographical factors. The analysis shows a clear and notable trend within the dataset. Specifically, it is observed that neurological symptoms, along with symptoms that impede individuals’ daily functioning, exhibit the highest prevalence, particularly during the latter half of the analyzed tweet period. This period corresponds to a time when an increasing number of individuals have recovered from COVID-19 and are reporting their experiences with Long COVID. Notably, fatigue, depression, stress, and anxiety emerge as the most prevalent phenotypes. This scientific investigation of the complex interactions between Long COVID phenotypes, mental health, and the manifestation of diverse symptoms is offering insights into the profound consequences on individuals’ lives. These findings shed light on the significant burden posed by Long COVID and its cascading effects on various aspects of individuals’ well-being and society at large.
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Exploring Changes in Diagnoses during the COVID-19 Era: Comparative Analysis
(Belgrade: Institute of molecular genetics and genetic engineering, 2023)
Misheva, Despina
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Stojcheva, Marija
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Hasanicaj, Hana
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Mladenovska, Ana
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Dobreva, Jovana
The healthcare sector is just one of several areas of society that have been significantly impacted by the COVID-19 pandemic. This paper aims to analyze the changes observed in the medical profession’s approach to diagnosing diseases between the pre-pandemic year of 2019 and the pandemic year of 2020. By examining these shifts, we explore how medical professionals have adapted their treatment strategies, leading to modifications in diagnosis for various diseases. Based on our visualization, shown in Figure 1, we observed that the diagnoses of Obstructive Sleep Apnea and End stage renal disease had consistent distributions in both 2019 and 2020. Also we need to mention, the count value for Obstructive Sleep Apnea was higher in 2020, whereas in 2019, the count value was higher for End stage renal disease, showing their representation in each year. We can conclude that the pandemic has resulted in a marked increase in the occurrence of specific diagnoses compared to the previous year, some of them being acute pharyngitis-sore throat (J029), gastro-oesophageal reflux disease (K219) and pure hypercholesterolemia - unspecified (E7800), as can be seen on Figure 1. A notable variation can be observed when examining the months of November and December in 2020. In these months, the diagnosis Contact with and (suspected) exposure to other viral communicable diseases transitions from the third to the second position, indicating a higher occurrence of COVID-19 in December compared to November. This shift in ranking provides valuable insights into the increased prevalence of this diagnosis during the month of December. Through this analysis, we aim to examine the transformations that have taken place as a result of the pandemic, particularly in terms of the diagnosis of a specific disease, which has undergone notable changes compared to the pre-pandemic period. We highlight several significant changes that have occurred in defining diagnoses, showcasing the variations observed over the course of a year.
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A comprehensive study of food prices and food fraud in the European Union
(IEEE, 2023-12-15)
Trajkovikj, Filip
;
Todorovska, Ana
;
Peshevski, Dimitar
;
Nakova, Lina
;
Trajanoska, Milena
This research delves into the intricate dynamics of food pricing and fraud within European Union member countries. We analyze the complex interplay between food categories and countries, unraveling unique pricing trends and potential anomalies. By computing inflation-adjusted expected prices and sourcing real prices, we gain a deep understanding of inflation’s impact on actual food costs. Our multi-level analyses, network-based approaches, and cluster maps provide a global perspective, revealing international correlations in food pricing and fraud. The significance of our findings lies in setting the groundwork for understanding food fraud, informing strategies for fraud prevention, consumer protection, and, ultimately, food sustainability. Our work serves as a crucial resource for policymakers, economists, and consumers, emphasizing the importance of data-driven decision-making and transparency in the ever-evolving landscape of the European food market.

Faculty of Computer Science and Engineering

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