Електротехнички факултет [Конференције] / Faculty of Electrical Engineering [Conference paper]

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https://vaseljena.ues.rs.ba/handle/123456789/821

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Прегледај Електротехнички факултет [Конференције] / Faculty of Electrical Engineering [Conference paper] по Аутор "Ćosović, Marijana"

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    Analyzing the Еffects of Мobility and Season on COVID-19 Cases Using Negative Binomial Regression: a European Case Study
    (Faculty of Electrical Engineering, University of East Sarajevo, 2021) Janković, Radmila; Amelio, Alessia; Ćosović, Marijana
    This paper develops a Generalized Linear Model using the Negative Binomial Regression with log link function to analyze the effects of mobility trends and seasons on COVID-19 cases. The data of four European countries was used, namely Austria, Greece, Italy, and Czech Republic. The dataset includes daily observations of registered COVID-19 cases, and the data of six types of mobility trends: retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential mobility for the period Feb 15 - Nov 15, 2020. The results suggest that the number of COVID-19 cases differs between seasons and different mobility trends.
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    Classication Methods in Cultural Heritage
    (Research Area of ​​the CNR of Pisa Institute of Information Science and Technologies “A. Faedo” (ISTI), 2019) Ćosović, Marijana; Amelio, Alessia; Junuz, Emina
    This paper describes relevant classifi cation methods applied to the cultural heritage context. In particular, a categorisation of the classifi cation methods is provided according to tangible and intangible cultural heritage, where movable and immovable objects can be in the focus. A short description of each method is reported for each cultural heritage category in terms of feature representation, classifi cation approach and obtained results. The proposed survey can be useful in the research community of pattern recognition and visual computing for exploring the current literature about the topic. It will hopefully provide new insights for the advancement of knowledge discovery in cultural heritage.
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    CNN Classification of the Cultural Heritage Images
    (Faculty of Electrical Engineering, University of East Sarajevo, 2020) Ćosović, Marijana; Janković, Radmila
    The cultural heritage image classification represents one of the most important tasks in the process of digitalization. In this paper, a deep learning neural network was applied in order to classify images of architectural heritage belonging to ten categories, in particular: (i) bell tower, (ii) stained glass, (iii) vault, (iv) column, (v) outer dome, (vi) altar, (vii) apse, (viii) inner dome, (ix) flying buttress, and (x) gargoyle. The Convolutional neural network was used for image classification, with the same architecture applied on two sets of the data: the full dataset consisting of 10 categories as well as dataset with 5 different image categories. The results show that both architectures performed well and obtained accuracy of up to 90%.
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    Preservation of Cultural Heritage Sites using IoT
    (Faculty of Electrical Engineering, University of East Sarajevo, 2019) Maksimović, Mirjana; Ćosović, Marijana
    Religious/historical buildings ought to be preserved for as long as possible. The ancient structures themselves and the rich collections they store represent irreplaceable wealth for future generations. They also provide the space for the customs and traditions to sustain. In addition, cultural heritage sites can stimulate economic growth but need to be maintained as well. This paper proposes an effective and affordable solution for the monitoring of preservation conditions of the Church belonging to Eastern Orthodox cultural heritage. The solution is based on utilization of modern Information and Communication Technologies (ICT) and services with general structure and main design principles using the three-layer IoT architecture. This research is an ongoing work only involving the first step towards realization of preservation monitoring system.
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    Time Series Prediction of Air Pollutants
    (Faculty of Electrical Engineering, University of East Sarajevo, 2019) Janković, Radmila; Ćosović, Marijana; Amelio, Alessia
    Pollution levels are highly dependent on the meteorological parameters, as the weather conditions dictate pollution dispersion and concentration. With the rise of global environmental protection initiatives, there is also a need for accurate prediction of pollution levels. This paper presents a time series prediction of NO2 and CO given four meteorological parameters: (i) air pressure, (ii) relative humidity, (iii) average daily temperature, and (iv) wind speed, using a Nonlinear Autoregressive Exogenous (NARX) neural network. The research is a case study of three European countries: (i) Serbia, (ii) Bosnia and Herzegovina, and (iii) Italy, and involves data from 2014 to 2016 for a total of 1096 instances. The results show that the best prediction accuracy is obtained for CO for data regarding Italy and Bosnia and Herzegovina, and for NO2 for data regarding Serbia. Moreover, the best predictor variables of NO2 are air pressure and relative humidity, followed by the wind speed. The best predictor variables of CO are pressure and temperature for Bosnia and Italy, and wind speed for Serbia