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.Todorovska, AnaHas files: Yes

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    Empowering Academic Excellence: A Case Study of Student-Faculty Collaboration in Developing and Maintaining IT Solutions at FCSE
    (2024-04-20)
    Atanasoska, Elena
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    Marojevikj, Bojana
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    Todorovska, Ana
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    Bidikov, Vladislav
    The Faculty of Computer Science and Engineer ing (FCSE) at Ss. Cyril and Methodius University in North Macedonia has adopted an innovative approach to develop and maintain IT solutions through student-faculty collaboration. This paper presents a case study of this collaboration, focusing on projects undertaken within the Web Programming course. Through the integration of students into the development and management of services, FCSE addresses operational challenges while providing students with valuable learning experiences. The paper outlines the implementation strategy, emphasizing the use of a centralized database and multiple web applications aligned with course content. Furthermore, it discusses the increasing student enrollment in the course, indicating growing interest and participation in collaborative projects. Overall, this paper highlights the benefits of this collaborative approach in preparing students for future endeavors in web development and fostering continuous improvement within FCSE.
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    Analysis of the relationship between traditional markets and commodities trade
    (Ss Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedonia, 2023-07)
    Todorovska, Ana
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    Milev, Stefan
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    Vodenska, Irena
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    T. Chitkushev, Lubomir
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    In a global world, no country, market, or economy is isolated and can function independently. Interconnectivity is a fundamental feature of economic systems, including both traditional financial markets and novel markets. This study aims to explore the relationships between traditional markets and commodities trade. We develop a methodology for analyzing the relationships between five stock market indexes (S&P500, Dow Jones, BSE, Hang Seng, FTSE) and three commodities (crude oil, natural gas, gold), based on multimodal publicly available datasets incorporating structured numerical and unstructured news and social network data. To find the existence of direc tional 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 between the assets and discuss our conclusions.
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    The influence of stock market indexes (S&P500 and Dow Jones) on cryptocurrencies prices
    (2022)
    Angelovski, Gorast
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    Todorovska, Ana
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    Rusevski, Ivan
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    Marojevikj, Jovana
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    Spirovska, Eva
    In this paper we analyze openly available time series data for the prices of 18 cryptocurrencies and 2 stock market indexes (S&P500 and Dow Jones). First, we calculate the correlation values between the cryptocurrencies and indexes datasets. Then, we use a state of the art time series prediction library (XGBoost) in order to make prediction models for the daily prices of all the cryptocurrencies, using the stock market index datasets as input features in the training model. We calculate metrics for the difference between the actual prices and the prices predicted using our models. Finally, we show the feature importance score that our model attributed to each prediction model, and compare the score between the three input features (S&P500 dataset, Dow Jones dataset, and the actual cryptocurrency dataset).
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    A novel platform for sharing and renting clothing to reduce environmental pollution
    (Ss Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedonia, 2020-05-08)
    Todorovska, Ana
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    Krajchevska, Evgenija
    In today’s rapidly developing, technology driven world, the environment is being polluted in numerous ways, significantly more than in the past. Considering the fact that the fashion industry is the second largest polluter worldwide, it is evident that changes are needed in the way clothing is viewed and used. In this paper, we present a possible solution to this problem, which is built using the constantly advancing concept of shared economy. The platform we present is intended to create an online space that is used to share clothes that are not considered useful by their owners. Furthermore, the rental of expensive clothing for special occasions is provided. Adhering to the importance of the business aspect of any novelty being introduced, a market research was also conducted in our country. This paper includes these results, together with the conclusions drawn and the possible improvements suggested.