Прегледај по Аутор "Ćosović, Marijana"
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- СтавкаA Survey on Writer Identification and Recognition Methods with a Special Focus on Cultural Heritage(CEUR Workshop Proceedings, 2022) Ćosović, Marijana; Janković Babić, Radmila; Amelio, AlessiaThis paper reviews the state-of-the-art contributions for writer identification and recognition with a special focus on applications in the domain of cultural heritage. The task of writer recognition has only recently been recognized as a problem that can be solved by the methods available in the computer vision domain. A number of researchers have explored the performance of deep learning and transfer learning techniques for writer identification in historical documents, and for this purpose various datasets have been used, including the Avila Bible dataset, Historical-WI, HisFragIR20, IAM, HWDB and others. This paper analyses relevant methods used for writer identification and recognition in historical and medieval documents. It also makes a distinction between classification based on words, patches, or whole pages. The results indicate that the current literature supports using deep learning and transfer learning methods, as they are found to achieve the highest performance.
- СтавкаAnalyzing 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, MarijanaThis 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.
- СтавкаClassication 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, EminaThis 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.
- СтавкаCNN Classification of the Cultural Heritage Images(Faculty of Electrical Engineering, University of East Sarajevo, 2020) Ćosović, Marijana; Janković, RadmilaThe 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%.
- СтавкаGame-Based Learning in Museums—Cultural Heritage Applications(MDPI, 2020) Ćosović, Marijana; Ramić Brkić, BelmaAs traditional museums migrate to the virtual world, they offer wider access to the exhibit collections but often fail to present content of those collections in more engaging way. Game-based learning is one of the solutions to mitigate this inevitable transition and support active learning in the process. It is increasingly gaining interest from the cultural heritage scientific community for the purpose of promoting cultural heritage, raising awareness of its importance and motivating users to visit cultural institutions such as museums more often. There are numerous examples of serious games that are based on or contain heritage content. Tangible cultural heritage is more represented in the virtual worlds and mainly based on applications of 3D technology. Recently, intangible cultural heritage is gaining more visibility within cultural heritage scope as a domain in which game-based learning could assist in its preservation. This paper attempts to address pros and cons of game-based learning in general and reflect on the choices of using serious games in the museum environment.
- СтавкаPreservation of Cultural Heritage Sites using IoT(Faculty of Electrical Engineering, University of East Sarajevo, 2019) Maksimović, Mirjana; Ćosović, MarijanaReligious/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.
- СтавкаTime Series Prediction of Air Pollutants(Faculty of Electrical Engineering, University of East Sarajevo, 2019) Janković, Radmila; Ćosović, Marijana; Amelio, AlessiaPollution 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