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
;
Todorovska, Ana
;
Trajkovikj, Filip
;
Hristov, Nikola
;
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
;
Fetaji, Fjola
;
Dobreva, Jovana
;
Trajanoska, Milena
;
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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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.
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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.
Some of the metrics are blocked by your
consent settings
Methodology for food prices forecasting
(IEEE, 2023-12-15)
Peshevski, Dimitar
;
Todorovska, Ana
;
Trajkovikj, Filip
;
Hristov, Nikola
;
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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Using ML and Explainable AI to understand the interdependency networks between classical economic indicators and crypto-markets
(North-Holland, 2023-08-15)
Todorovska, Ana
;
Peshov, Hristijan
;
Rusevski, Ivan
;
Vodenska, Irena
;
Chitkushev, T. Lubomir
In a global world, no country, market, or economy is isolated. Interconnectivity is becoming a fundamental feature of economic systems, including macroeconomic trends, traditional financial markets, and digital markets. Cryptocurrencies, as a new digital asset, are becoming an integral part of the global economy. This study aims to explore the relationships between cryptocurrencies and traditional financial markets. We develop a methodology for analyzing the relationships between the largest cryptocurrencies and select global market-based economic indicators based on multimodal publicly available datasets incorporating structured numerical and unstructured news and social network data. To find the existence of directional associations, we develop an Explainable ML model that first learns the dependencies between different assets and then explains them in a form understandable by humans. We apply our methodology to analyze connectivity networks of seven cryptocurrencies (Bitcoin, Ethereum, Cardano, Chainlink, Litecoin, Stellar, and Ripple) and seven classical economic indicators, including five market indexes (BSE, Dow Jones, S&P500, FTSE, and Hang Seng) and two commodity prices (Oil and Gold).
Some of the metrics are blocked by your
consent settings
Methodology for food prices forecasting
(IEEE, 2023-12-15)
Peshevski, Dimitar
;
Todorovska, Ana
;
Trajkovikj, Filip
;
Hristov, Nikola
;
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.
Some of the metrics are blocked by your
consent settings
Assessing the Environmental Impact of Plant-Based Diets: A Comprehensive Analysis
(IEEE, 2023-12-15)
Golubova, Blagica
;
Fetaji, Fjola
;
Dobreva, Jovana
;
Trajanoska, Milena
;
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.
Some of the metrics are blocked by your
consent settings
Interdependency between Classical Economic Indicators and Crypto-Markets
(2022-08)
Todorovska, Ana
;
Rusevski, Ivan
;
Peshov, Hristijan
;
Spirovska, Eva
;
Marojevikj, Jovana
In a global world, no country, market, or economy is isolated. Interconnectivity is becoming a fundamental feature of the economic systems, including macroeconomic trends, traditional financial markets, and digital markets. Cryptocurrencies, as a new digital asset, are becoming an integral part of the global economy. This study aims to explore the relationships between cryptocurrencies and the traditional financial markets. We develop a methodology for analyzing the relationships between largest cryptocurrencies and selected global market-based economic indicators based on multimodal publicly available datasets incorporating structured numerical and unstructured news and social network data. To find the existence of directional associations we developan explainable AI model that first learns the dependencies between different assets and then explains them in a form understandable by humans. We apply our methodology to analyze connectivity networks of seven cryptocurrencies (Bitcoin, Ethereum, Cardano, Chainlink, Litecoin, Stellar, and Ripple) and seven classical economic indicators, including five marked indexes (BSE, Dow Jones, S\&P500, FTSE, and Hang Seng) and two commodity prices (Oil and Gold).

Faculty of Computer Science and Engineering

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