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

Access type: UnknownAuthor: search.filters.author.Evkoski, Bojan

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Demographic analysis of music preferences in streaming service networks
    (Springer, Cham, 2020-02-22)
    Jovanovska, Lidija
    ;
    Evkoski, Bojan
    ;
    ;
    As Daniel J. Levitin noted, music is a cross-cultural phenomenon, a ubiquitous activity found in every known human culture. It is indeed, a living matter that flows through cultures, which makes it a complex system potentially holding valuable information. Therefore, we model country-to-country interactions to reveal macro-level music trends. The purpose of this paper is twofold. Firstly, we explore the way specific demographic characteristics, such as language and geographic location affect the global community structure in streaming service networks. Secondly, we examine whether a clear flow of musical trends exists in the world by identifying countries who are prominent leaders on the music streaming charts. The community analysis shows that there is strong support for the first claim. Next, we find that the flow of musical trends is not strongly directional globally, although we were still able to identify prominent leaders and followers within the communities. The obtained results can further lead to the development of more sophisticated music recommendation systems, kindle new cultural studies and bring discoveries in the field of musicology.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Air Pollution Prediction Using LSTM Neural Networks
    (2019-05)
    Evkoski, Bojan
    ;
    Stojanovski, Zafir
    ;
    Trajkovski, Aleksandar
    ;
    Gjorgjevikj, Dejan
    Air pollution in North Macedonia is 20 times over the EU limit. Recently Skopje is mentioned as the most polluted city in Europe. As a result, this is believed to contribute to 2000 annual premature deaths in Skopje, Tetovo and Bitola only. Being able to forecast air pollution levels to take timely precaution could drastically reduce these numbers. Using state of the art recurrent neural networks known as LSTMs, we were able to predict these levels by combining historical pollution data and weather forecasts through meta models, achieving mean RMSE for all sensors around 20, with the best results having RMSE as low as 8.78, with PM10 measurements ranging from 0 to above 1000 and are usually accompanied by a lot of noise. In this paper we present several approaches we have tried for solving the problem and a basic comparison between them and we also propose a way to expand these models into a realtime system for multitarget predictions.