TY  - JOUR
AU  - Babović, Zoran
AU  - Bajat, Branislav
AU  - Đokić, Vladan
AU  - Đorđević, Filip
AU  - Drašković, Dražen
AU  - Filipović, Nenad
AU  - Furht, Borko
AU  - Gačić, Nikola
AU  - Ikodinović, Igor
AU  - Ilić, Marija
AU  - Irfanoglu, Ayhan
AU  - Jelenković, Branislav
AU  - Kartelj, Aleksandar
AU  - Klimeck, Gerhard
AU  - Korolija, Nenad
AU  - Kotlar, Miloš
AU  - Kovačević, Miloš
AU  - Kuzmanović, Vladan
AU  - Marinković, Marko
AU  - Marković, Slobodan
AU  - Mendelson, Avi
AU  - Milutinović, Veljko
AU  - Nešković, Aleksandar
AU  - Nešković, Nataša
AU  - Mitić, Nenad
AU  - Nikolić, Boško
AU  - Novoselov, Konstantin
AU  - Prakash, Arun
AU  - Ratković, Ivan
AU  - Stojadinović, Zoran
AU  - Ustyuzhanin, Andrey
AU  - Zak, Stan
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3113
AB  - This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).
PB  - Springer
T2  - Journal of Big Data
T1  - Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems
VL  - 10
DO  - 10.1186/s40537-023-00731-6
ER  - 

@article{
author = "Babović, Zoran and Bajat, Branislav and Đokić, Vladan and Đorđević, Filip and Drašković, Dražen and Filipović, Nenad and Furht, Borko and Gačić, Nikola and Ikodinović, Igor and Ilić, Marija and Irfanoglu, Ayhan and Jelenković, Branislav and Kartelj, Aleksandar and Klimeck, Gerhard and Korolija, Nenad and Kotlar, Miloš and Kovačević, Miloš and Kuzmanović, Vladan and Marinković, Marko and Marković, Slobodan and Mendelson, Avi and Milutinović, Veljko and Nešković, Aleksandar and Nešković, Nataša and Mitić, Nenad and Nikolić, Boško and Novoselov, Konstantin and Prakash, Arun and Ratković, Ivan and Stojadinović, Zoran and Ustyuzhanin, Andrey and Zak, Stan",
year = "2023",
abstract = "This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).",
publisher = "Springer",
journal = "Journal of Big Data",
title = "Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems",
volume = "10",
doi = "10.1186/s40537-023-00731-6"
}

Babović, Z., Bajat, B., Đokić, V., Đorđević, F., Drašković, D., Filipović, N., Furht, B., Gačić, N., Ikodinović, I., Ilić, M., Irfanoglu, A., Jelenković, B., Kartelj, A., Klimeck, G., Korolija, N., Kotlar, M., Kovačević, M., Kuzmanović, V., Marinković, M., Marković, S., Mendelson, A., Milutinović, V., Nešković, A., Nešković, N., Mitić, N., Nikolić, B., Novoselov, K., Prakash, A., Ratković, I., Stojadinović, Z., Ustyuzhanin, A.,& Zak, S.. (2023). Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems. in Journal of Big Data
Springer., 10.
https://doi.org/10.1186/s40537-023-00731-6

Babović Z, Bajat B, Đokić V, Đorđević F, Drašković D, Filipović N, Furht B, Gačić N, Ikodinović I, Ilić M, Irfanoglu A, Jelenković B, Kartelj A, Klimeck G, Korolija N, Kotlar M, Kovačević M, Kuzmanović V, Marinković M, Marković S, Mendelson A, Milutinović V, Nešković A, Nešković N, Mitić N, Nikolić B, Novoselov K, Prakash A, Ratković I, Stojadinović Z, Ustyuzhanin A, Zak S. Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems. in Journal of Big Data. 2023;10.
doi:10.1186/s40537-023-00731-6 .

Babović, Zoran, Bajat, Branislav, Đokić, Vladan, Đorđević, Filip, Drašković, Dražen, Filipović, Nenad, Furht, Borko, Gačić, Nikola, Ikodinović, Igor, Ilić, Marija, Irfanoglu, Ayhan, Jelenković, Branislav, Kartelj, Aleksandar, Klimeck, Gerhard, Korolija, Nenad, Kotlar, Miloš, Kovačević, Miloš, Kuzmanović, Vladan, Marinković, Marko, Marković, Slobodan, Mendelson, Avi, Milutinović, Veljko, Nešković, Aleksandar, Nešković, Nataša, Mitić, Nenad, Nikolić, Boško, Novoselov, Konstantin, Prakash, Arun, Ratković, Ivan, Stojadinović, Zoran, Ustyuzhanin, Andrey, Zak, Stan, "Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems" in Journal of Big Data, 10 (2023),
https://doi.org/10.1186/s40537-023-00731-6 . .

TY  - CONF
AU  - Vranešević, Diana
AU  - Nedeljković, Đorđe
AU  - Kovačević, Miloš
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3235
AB  - This study demonstrates the potential of application of unstructured textual data for predicting
real estate prices and compares different protocols for extracting features from textual data.
Performance of the different models for price prediction was evaluated on data set of real estate
listings, which included numerical and categorical features, as well as text descriptions. The
experiments showed that adding features extracted from both the translated description text, as
well as noun chunks from it, resulted in the highest R2 score of 0.768, representing an
improvement over the R2 score of 0.71 for the baseline model without text-based features. The
findings from this study indicate how the performance of real estate price prediction models can
be improved by utilizing text-based features, in turn benefiting property market stakeholders in
making informed decisions and evaluating competitive pricing strategies.
C3  - 2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI) Kragujevac, Serbia, May 19-20, 2023
T1  - Application of unstructured text based features in prediction of real estate prices: A comparative study
UR  - https://hdl.handle.net/21.15107/rcub_grafar_3235
ER  - 

@conference{
author = "Vranešević, Diana and Nedeljković, Đorđe and Kovačević, Miloš",
year = "2023",
abstract = "This study demonstrates the potential of application of unstructured textual data for predicting
real estate prices and compares different protocols for extracting features from textual data.
Performance of the different models for price prediction was evaluated on data set of real estate
listings, which included numerical and categorical features, as well as text descriptions. The
experiments showed that adding features extracted from both the translated description text, as
well as noun chunks from it, resulted in the highest R2 score of 0.768, representing an
improvement over the R2 score of 0.71 for the baseline model without text-based features. The
findings from this study indicate how the performance of real estate price prediction models can
be improved by utilizing text-based features, in turn benefiting property market stakeholders in
making informed decisions and evaluating competitive pricing strategies.",
journal = "2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI) Kragujevac, Serbia, May 19-20, 2023",
title = "Application of unstructured text based features in prediction of real estate prices: A comparative study",
url = "https://hdl.handle.net/21.15107/rcub_grafar_3235"
}

Vranešević, D., Nedeljković, Đ.,& Kovačević, M.. (2023). Application of unstructured text based features in prediction of real estate prices: A comparative study. in 2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI) Kragujevac, Serbia, May 19-20, 2023.
https://hdl.handle.net/21.15107/rcub_grafar_3235

Vranešević D, Nedeljković Đ, Kovačević M. Application of unstructured text based features in prediction of real estate prices: A comparative study. in 2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI) Kragujevac, Serbia, May 19-20, 2023. 2023;.
https://hdl.handle.net/21.15107/rcub_grafar_3235 .

Vranešević, Diana, Nedeljković, Đorđe, Kovačević, Miloš, "Application of unstructured text based features in prediction of real estate prices: A comparative study" in 2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI) Kragujevac, Serbia, May 19-20, 2023 (2023),
https://hdl.handle.net/21.15107/rcub_grafar_3235 .

TY  - JOUR
AU  - Babović, Zoran
AU  - Bajat, Branislav
AU  - Barac, Dusan
AU  - Bengin, Vesna
AU  - Đokić, Vladan
AU  - Đorđević, Filip
AU  - Drašković, Dražen
AU  - Filipović, Nenad
AU  - French, Stephan
AU  - Furht, Borko
AU  - Ilić, Marija
AU  - Irfanoglu, Ayhan
AU  - Kartelj, Aleksandar
AU  - Kilibarda, Milan
AU  - Klimeck, Gerhard
AU  - Korolija, Nenad
AU  - Kotlar, Miloš
AU  - Kovačević, Miloš
AU  - Kuzmanović, Vladan
AU  - Lehn, Jean-Marie
AU  - Madić, Dejan
AU  - Marinković, Marko
AU  - Mateljević, Miodrag
AU  - Mendelson, Avi
AU  - Mesinger, Fedor
AU  - Milovanović, Gradimir
AU  - Milutinović, Veljko
AU  - Mitić, Nenad
AU  - Nešković, Aleksandar
AU  - Nešković, Nataša
AU  - Nikolić, Boško
AU  - Novoselov, Konstantin
AU  - Prakash, Arun
AU  - Protić, Jelica
AU  - Ratković, Ivan
AU  - Rios, Diego
AU  - Shechtman, Dan
AU  - Stojadinović, Zoran
AU  - Ustyuzhanin, Andrey
AU  - Zak, Stan
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3114
AB  - This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, and a number of other related fields. The course derived from this strategy includes four parts: (a) Computing with a selected set of modern paradigms—the stress is on Control Flow and Data Flow computing paradigms, but paradigms conditionally referred to as Energy Flow and Diffusion Flow are also covered; (b) Project management that is holistic—the stress is on the wide plethora of issues spanning from the preparation of project proposals, all the way to incorporation activities to follow after the completion of a successful project; (c) Examples from past research and development experiences—the stress is on experiences of leading experts from academia and industry; (d) Student projects that stimulate creativity—the stress is on methods that educators could use to induce and accelerate the creativity of students in general. Finally, the article ends with selected pearls of wisdom that could be treated as suggestions for further elaboration.
PB  - Springer
T2  - Journal of Big Data
T1  - Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains
VL  - 10
DO  - 10.1186/s40537-023-00730-7
ER  - 

@article{
author = "Babović, Zoran and Bajat, Branislav and Barac, Dusan and Bengin, Vesna and Đokić, Vladan and Đorđević, Filip and Drašković, Dražen and Filipović, Nenad and French, Stephan and Furht, Borko and Ilić, Marija and Irfanoglu, Ayhan and Kartelj, Aleksandar and Kilibarda, Milan and Klimeck, Gerhard and Korolija, Nenad and Kotlar, Miloš and Kovačević, Miloš and Kuzmanović, Vladan and Lehn, Jean-Marie and Madić, Dejan and Marinković, Marko and Mateljević, Miodrag and Mendelson, Avi and Mesinger, Fedor and Milovanović, Gradimir and Milutinović, Veljko and Mitić, Nenad and Nešković, Aleksandar and Nešković, Nataša and Nikolić, Boško and Novoselov, Konstantin and Prakash, Arun and Protić, Jelica and Ratković, Ivan and Rios, Diego and Shechtman, Dan and Stojadinović, Zoran and Ustyuzhanin, Andrey and Zak, Stan",
year = "2023",
abstract = "This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, and a number of other related fields. The course derived from this strategy includes four parts: (a) Computing with a selected set of modern paradigms—the stress is on Control Flow and Data Flow computing paradigms, but paradigms conditionally referred to as Energy Flow and Diffusion Flow are also covered; (b) Project management that is holistic—the stress is on the wide plethora of issues spanning from the preparation of project proposals, all the way to incorporation activities to follow after the completion of a successful project; (c) Examples from past research and development experiences—the stress is on experiences of leading experts from academia and industry; (d) Student projects that stimulate creativity—the stress is on methods that educators could use to induce and accelerate the creativity of students in general. Finally, the article ends with selected pearls of wisdom that could be treated as suggestions for further elaboration.",
publisher = "Springer",
journal = "Journal of Big Data",
title = "Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains",
volume = "10",
doi = "10.1186/s40537-023-00730-7"
}

Babović, Z., Bajat, B., Barac, D., Bengin, V., Đokić, V., Đorđević, F., Drašković, D., Filipović, N., French, S., Furht, B., Ilić, M., Irfanoglu, A., Kartelj, A., Kilibarda, M., Klimeck, G., Korolija, N., Kotlar, M., Kovačević, M., Kuzmanović, V., Lehn, J., Madić, D., Marinković, M., Mateljević, M., Mendelson, A., Mesinger, F., Milovanović, G., Milutinović, V., Mitić, N., Nešković, A., Nešković, N., Nikolić, B., Novoselov, K., Prakash, A., Protić, J., Ratković, I., Rios, D., Shechtman, D., Stojadinović, Z., Ustyuzhanin, A.,& Zak, S.. (2023). Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains. in Journal of Big Data
Springer., 10.
https://doi.org/10.1186/s40537-023-00730-7

Babović Z, Bajat B, Barac D, Bengin V, Đokić V, Đorđević F, Drašković D, Filipović N, French S, Furht B, Ilić M, Irfanoglu A, Kartelj A, Kilibarda M, Klimeck G, Korolija N, Kotlar M, Kovačević M, Kuzmanović V, Lehn J, Madić D, Marinković M, Mateljević M, Mendelson A, Mesinger F, Milovanović G, Milutinović V, Mitić N, Nešković A, Nešković N, Nikolić B, Novoselov K, Prakash A, Protić J, Ratković I, Rios D, Shechtman D, Stojadinović Z, Ustyuzhanin A, Zak S. Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains. in Journal of Big Data. 2023;10.
doi:10.1186/s40537-023-00730-7 .

Babović, Zoran, Bajat, Branislav, Barac, Dusan, Bengin, Vesna, Đokić, Vladan, Đorđević, Filip, Drašković, Dražen, Filipović, Nenad, French, Stephan, Furht, Borko, Ilić, Marija, Irfanoglu, Ayhan, Kartelj, Aleksandar, Kilibarda, Milan, Klimeck, Gerhard, Korolija, Nenad, Kotlar, Miloš, Kovačević, Miloš, Kuzmanović, Vladan, Lehn, Jean-Marie, Madić, Dejan, Marinković, Marko, Mateljević, Miodrag, Mendelson, Avi, Mesinger, Fedor, Milovanović, Gradimir, Milutinović, Veljko, Mitić, Nenad, Nešković, Aleksandar, Nešković, Nataša, Nikolić, Boško, Novoselov, Konstantin, Prakash, Arun, Protić, Jelica, Ratković, Ivan, Rios, Diego, Shechtman, Dan, Stojadinović, Zoran, Ustyuzhanin, Andrey, Zak, Stan, "Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains" in Journal of Big Data, 10 (2023),
https://doi.org/10.1186/s40537-023-00730-7 . .

TY  - CONF
AU  - Kovačević, Miloš
AU  - Bursać, Petar
AU  - Bajat, Branislav
AU  - Kilibarda, Milan
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2804
AB  - Soil organic carbon represents the main nutrient source for crop yields, which is of great importance to agricultural production. This research investigates the usage of a transfer learning-based neural network model to predict SOC values from geochemical soil parameters. The results on datasets representing five European countries showed that the model was able to capture the valuable information contained in grassland soil samples when predicting the SOC values in cropland areas.
C3  - 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia
T1  - AI in Agriculture
UR  - https://hdl.handle.net/21.15107/rcub_grafar_2804
ER  - 

@conference{
author = "Kovačević, Miloš and Bursać, Petar and Bajat, Branislav and Kilibarda, Milan",
year = "2022",
abstract = "Soil organic carbon represents the main nutrient source for crop yields, which is of great importance to agricultural production. This research investigates the usage of a transfer learning-based neural network model to predict SOC values from geochemical soil parameters. The results on datasets representing five European countries showed that the model was able to capture the valuable information contained in grassland soil samples when predicting the SOC values in cropland areas.",
journal = "1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia",
title = "AI in Agriculture",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2804"
}

Kovačević, M., Bursać, P., Bajat, B.,& Kilibarda, M.. (2022). AI in Agriculture. in 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia.
https://hdl.handle.net/21.15107/rcub_grafar_2804

Kovačević M, Bursać P, Bajat B, Kilibarda M. AI in Agriculture. in 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia. 2022;.
https://hdl.handle.net/21.15107/rcub_grafar_2804 .

Kovačević, Miloš, Bursać, Petar, Bajat, Branislav, Kilibarda, Milan, "AI in Agriculture" in 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia (2022),
https://hdl.handle.net/21.15107/rcub_grafar_2804 .

TY  - JOUR
AU  - Bursać, Petar
AU  - Kovačević, Miloš
AU  - Bajat, Branislav
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2801
AB  - Soil organic carbon (SOC) is a vital component for sustainable agricultural
production. This research investigates the transfer learning-based neural
network model to improve classical machine learning estimation of SOC
values from other geochemical and physical soil parameters. The results on
datasets based on LUCAS data from 2015 showed that the Instance-based
transfer learning model captured the valuable information contained in different
source domains (cropland and grassland) of soil samples when estimating the
SOC values in arable cropland areas. The effects of using transfer learning are
more pronounced in the case of different source (grassland) and target
(cropland) domains. Obtained results indicate that the transfer learning (TL)
approach provides better or at least equal output results compared to the
classical machine learning procedure. The proposed TL methodology could be
used to generate a pedotransfer function (PTF) for target domains with
described samples and unknown related PTF outputs if the described
samples with known related PTF outputs from a different geographic or
similar land class source domain are available
T2  - Frontiers in Environmental Science
T1  - Instance-based transfer learning for soil organic carbon estimation
DO  - https://doi.org/10.3389/fenvs.2022.1003918
ER  - 

@article{
author = "Bursać, Petar and Kovačević, Miloš and Bajat, Branislav",
year = "2022",
abstract = "Soil organic carbon (SOC) is a vital component for sustainable agricultural
production. This research investigates the transfer learning-based neural
network model to improve classical machine learning estimation of SOC
values from other geochemical and physical soil parameters. The results on
datasets based on LUCAS data from 2015 showed that the Instance-based
transfer learning model captured the valuable information contained in different
source domains (cropland and grassland) of soil samples when estimating the
SOC values in arable cropland areas. The effects of using transfer learning are
more pronounced in the case of different source (grassland) and target
(cropland) domains. Obtained results indicate that the transfer learning (TL)
approach provides better or at least equal output results compared to the
classical machine learning procedure. The proposed TL methodology could be
used to generate a pedotransfer function (PTF) for target domains with
described samples and unknown related PTF outputs if the described
samples with known related PTF outputs from a different geographic or
similar land class source domain are available",
journal = "Frontiers in Environmental Science",
title = "Instance-based transfer learning for soil organic carbon estimation",
doi = "https://doi.org/10.3389/fenvs.2022.1003918"
}

Bursać, P., Kovačević, M.,& Bajat, B.. (2022). Instance-based transfer learning for soil organic carbon estimation. in Frontiers in Environmental Science.
https://doi.org/https://doi.org/10.3389/fenvs.2022.1003918

Bursać P, Kovačević M, Bajat B. Instance-based transfer learning for soil organic carbon estimation. in Frontiers in Environmental Science. 2022;.
doi:https://doi.org/10.3389/fenvs.2022.1003918 .

Bursać, Petar, Kovačević, Miloš, Bajat, Branislav, "Instance-based transfer learning for soil organic carbon estimation" in Frontiers in Environmental Science (2022),
https://doi.org/https://doi.org/10.3389/fenvs.2022.1003918 . .

TY  - JOUR
AU  - Milošević, Igor
AU  - Kovačević, Miloš
AU  - Petronijević, Predrag
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2422
AB  - Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine) and geographical and economic (describing the target market). The research suggests that the ensemble model based on random forest, light gradient boosting, and neural network members, as well as support vector regression as a decision unit, gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large data set of 500,000 machines advertised in 50 US states from 1989 to 2012.
PB  - http://cedb.asce.org
T2  - Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers
T1  - Estimating residual value of heavy construction equipment using ensemble learning
IS  - 7
VL  - 147
DO  - 10.1061/(ASCE)CO.1943-7862.0002088
ER  - 

@article{
author = "Milošević, Igor and Kovačević, Miloš and Petronijević, Predrag",
year = "2021",
abstract = "Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine) and geographical and economic (describing the target market). The research suggests that the ensemble model based on random forest, light gradient boosting, and neural network members, as well as support vector regression as a decision unit, gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large data set of 500,000 machines advertised in 50 US states from 1989 to 2012.",
publisher = "http://cedb.asce.org",
journal = "Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers",
title = "Estimating residual value of heavy construction equipment using ensemble learning",
number = "7",
volume = "147",
doi = "10.1061/(ASCE)CO.1943-7862.0002088"
}

Milošević, I., Kovačević, M.,& Petronijević, P.. (2021). Estimating residual value of heavy construction equipment using ensemble learning. in Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers
http://cedb.asce.org., 147(7).
https://doi.org/10.1061/(ASCE)CO.1943-7862.0002088

Milošević I, Kovačević M, Petronijević P. Estimating residual value of heavy construction equipment using ensemble learning. in Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers. 2021;147(7).
doi:10.1061/(ASCE)CO.1943-7862.0002088 .

Milošević, Igor, Kovačević, Miloš, Petronijević, Predrag, "Estimating residual value of heavy construction equipment using ensemble learning" in Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers, 147, no. 7 (2021),
https://doi.org/10.1061/(ASCE)CO.1943-7862.0002088 . .

TY  - JOUR
AU  - Stojadinović, Zoran I.
AU  - Kovačević, Miloš
AU  - Marinković, Dejan
AU  - Stojadinović, Božidar
PY  - 2021
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2396
AB  - This paper proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A Random Forest classification model predicts a damage probability distribution that, combined with an expert-defined repair cost matrix, enables the calculation of the expected repair costs for each building and, in aggregate, of direct losses in the earthquake-affected area. The proposed building representation does not include explicit information about the earthquake and the soil type. Instead, such information is implicitly contained in the spatial distribution of damage. To capture this distribution, a sampling algorithm, based on K-means clustering, is used to select a minimal number of buildings that represent the area of interest in terms of its seismic risk, independently of future earthquakes. To observe damage states in the representative set after an earthquake, the proposed framework utilizes a local network of trained damage assessors. The model is updated after each damage observation cycle, thus increasing the accuracy of the current loss assessment. The proposed framework is exemplified using the 2010 Kraljevo, Serbia earthquake dataset.
T2  - Earthquake Spectra
T1  - Rapid earthquake loss assessment based on machine learning and representative sampling
EP  - 26
SP  - 1
DO  - 10.1177/87552930211042393
ER  - 

@article{
author = "Stojadinović, Zoran I. and Kovačević, Miloš and Marinković, Dejan and Stojadinović, Božidar",
year = "2021",
abstract = "This paper proposes a new framework for rapid earthquake loss assessment based on a machine learning damage classification model and a representative sampling algorithm. A Random Forest classification model predicts a damage probability distribution that, combined with an expert-defined repair cost matrix, enables the calculation of the expected repair costs for each building and, in aggregate, of direct losses in the earthquake-affected area. The proposed building representation does not include explicit information about the earthquake and the soil type. Instead, such information is implicitly contained in the spatial distribution of damage. To capture this distribution, a sampling algorithm, based on K-means clustering, is used to select a minimal number of buildings that represent the area of interest in terms of its seismic risk, independently of future earthquakes. To observe damage states in the representative set after an earthquake, the proposed framework utilizes a local network of trained damage assessors. The model is updated after each damage observation cycle, thus increasing the accuracy of the current loss assessment. The proposed framework is exemplified using the 2010 Kraljevo, Serbia earthquake dataset.",
journal = "Earthquake Spectra",
title = "Rapid earthquake loss assessment based on machine learning and representative sampling",
pages = "26-1",
doi = "10.1177/87552930211042393"
}

Stojadinović, Z. I., Kovačević, M., Marinković, D.,& Stojadinović, B.. (2021). Rapid earthquake loss assessment based on machine learning and representative sampling. in Earthquake Spectra, 1-26.
https://doi.org/10.1177/87552930211042393

Stojadinović ZI, Kovačević M, Marinković D, Stojadinović B. Rapid earthquake loss assessment based on machine learning and representative sampling. in Earthquake Spectra. 2021;:1-26.
doi:10.1177/87552930211042393 .

Stojadinović, Zoran I., Kovačević, Miloš, Marinković, Dejan, Stojadinović, Božidar, "Rapid earthquake loss assessment based on machine learning and representative sampling" in Earthquake Spectra (2021):1-26,
https://doi.org/10.1177/87552930211042393 . .

TY  - CONF
AU  - Kovačević, Miloš
AU  - Stojadinović, Zoran I.
AU  - Marinković, Dejan
AU  - Stojadinović, Božidar
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2176
AB  - Rapid earthquake damage and loss assessment is crucial both for insuring the safety of inhabitants in the immediate aftermath of an earthquake and for the recovery of the stricken communities in the long run. This paper investigates the potential of different machine learning methods for building a rapid earthquake loss assessment system intended for residential houses in a municipal area. The system is trained on a pre-earthquake selected representative set of residential houses, after observing their damage and loss states. Two representative sampling strategies and three machine learning algorithms are described and evaluated on the 2010 Kraljevo M5.4 earthquake data set. The proposed models showed satisfactory accuracy in predicting the total expected repair cost (less than 20% error with the representative sample size of 10% of the inventory). The approach is independent of geological and earthquake data and does not require local peak ground acceleration values.
C3  - 11th National Conference on Earthquake Engineering
T1  - Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System
UR  - https://hdl.handle.net/21.15107/rcub_grafar_2176
ER  - 

@conference{
author = "Kovačević, Miloš and Stojadinović, Zoran I. and Marinković, Dejan and Stojadinović, Božidar",
year = "2018",
abstract = "Rapid earthquake damage and loss assessment is crucial both for insuring the safety of inhabitants in the immediate aftermath of an earthquake and for the recovery of the stricken communities in the long run. This paper investigates the potential of different machine learning methods for building a rapid earthquake loss assessment system intended for residential houses in a municipal area. The system is trained on a pre-earthquake selected representative set of residential houses, after observing their damage and loss states. Two representative sampling strategies and three machine learning algorithms are described and evaluated on the 2010 Kraljevo M5.4 earthquake data set. The proposed models showed satisfactory accuracy in predicting the total expected repair cost (less than 20% error with the representative sample size of 10% of the inventory). The approach is independent of geological and earthquake data and does not require local peak ground acceleration values.",
journal = "11th National Conference on Earthquake Engineering",
title = "Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2176"
}

Kovačević, M., Stojadinović, Z. I., Marinković, D.,& Stojadinović, B.. (2018). Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System. in 11th National Conference on Earthquake Engineering.
https://hdl.handle.net/21.15107/rcub_grafar_2176

Kovačević M, Stojadinović ZI, Marinković D, Stojadinović B. Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System. in 11th National Conference on Earthquake Engineering. 2018;.
https://hdl.handle.net/21.15107/rcub_grafar_2176 .

Kovačević, Miloš, Stojadinović, Zoran I., Marinković, Dejan, Stojadinović, Božidar, "Sampling and Machine Learning Methods for a Rapid Earthquake Loss Assessment System" in 11th National Conference on Earthquake Engineering (2018),
https://hdl.handle.net/21.15107/rcub_grafar_2176 .

TY  - CONF
AU  - Marinković, Dejan
AU  - Stojadinović, Zoran I.
AU  - Kovačević, Miloš
AU  - Stojadinović, Božidar
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2175
AB  - Earthquake resilience starts with a sudden drop of performance when an earthquake strikes followed by a relatively long recovery phase. The dynamics and volume of investment directly affect the rate and level of recovery. In this paper, the recovery process of the city of Kraljevo, Serbia following the M5.4 November 3, 2010 Kraljevo earthquake is analyzed. The base for this analysis is recorded reconstruction data that includes building types, damage states, damage survey dates, repair methods, and repair design, permit, and completion dates. Tracking the rate of building re-occupation and the rate of investment in repairs during the recovery process made it possible to construct empirical housing resilience curves for a community of about 70,000 people. The recorded data also provides insights into the reasons for different recovery rates for different building types and damages states, as well as a basis to evaluate the housing recovery management process and identify the significant influence of the reconstruction funding volume and rate on the sequencing, design and construction of the post-earthquake repairs. A comparison between the housing recovery after the 2010 Kraljevo and the 2009 Yunnan earthquakes shows significant simulates, pointing to the need to further improve, optimize and standardize post-earthquake housing recovery strategies worldwide.
C3  - 16th European Conference on Earthquake Engineering
T1  - 2010 Kraljevo Earthquake Recovery Process Metrics Derived from Recorded Reconstruction Data
UR  - https://hdl.handle.net/21.15107/rcub_grafar_2175
ER  - 

@conference{
author = "Marinković, Dejan and Stojadinović, Zoran I. and Kovačević, Miloš and Stojadinović, Božidar",
year = "2018",
abstract = "Earthquake resilience starts with a sudden drop of performance when an earthquake strikes followed by a relatively long recovery phase. The dynamics and volume of investment directly affect the rate and level of recovery. In this paper, the recovery process of the city of Kraljevo, Serbia following the M5.4 November 3, 2010 Kraljevo earthquake is analyzed. The base for this analysis is recorded reconstruction data that includes building types, damage states, damage survey dates, repair methods, and repair design, permit, and completion dates. Tracking the rate of building re-occupation and the rate of investment in repairs during the recovery process made it possible to construct empirical housing resilience curves for a community of about 70,000 people. The recorded data also provides insights into the reasons for different recovery rates for different building types and damages states, as well as a basis to evaluate the housing recovery management process and identify the significant influence of the reconstruction funding volume and rate on the sequencing, design and construction of the post-earthquake repairs. A comparison between the housing recovery after the 2010 Kraljevo and the 2009 Yunnan earthquakes shows significant simulates, pointing to the need to further improve, optimize and standardize post-earthquake housing recovery strategies worldwide.",
journal = "16th European Conference on Earthquake Engineering",
title = "2010 Kraljevo Earthquake Recovery Process Metrics Derived from Recorded Reconstruction Data",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2175"
}

Marinković, D., Stojadinović, Z. I., Kovačević, M.,& Stojadinović, B.. (2018). 2010 Kraljevo Earthquake Recovery Process Metrics Derived from Recorded Reconstruction Data. in 16th European Conference on Earthquake Engineering.
https://hdl.handle.net/21.15107/rcub_grafar_2175

Marinković D, Stojadinović ZI, Kovačević M, Stojadinović B. 2010 Kraljevo Earthquake Recovery Process Metrics Derived from Recorded Reconstruction Data. in 16th European Conference on Earthquake Engineering. 2018;.
https://hdl.handle.net/21.15107/rcub_grafar_2175 .

Marinković, Dejan, Stojadinović, Zoran I., Kovačević, Miloš, Stojadinović, Božidar, "2010 Kraljevo Earthquake Recovery Process Metrics Derived from Recorded Reconstruction Data" in 16th European Conference on Earthquake Engineering (2018),
https://hdl.handle.net/21.15107/rcub_grafar_2175 .

TY  - CHAP
AU  - Đurić, Uroš
AU  - Bajat, Branislav
AU  - Abolmasov, Biljana
AU  - Kovačević, Miloš
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1101
AB  - This chapter introduces theoretical and practical aspects for applying GIS and geocomputation methods in landslide assessment problems. Machine Learning techniques in combination with GIS are proven useful for computation and building of complex non-linear spatial models, which is why they have been chosen in our work. Modeling principles that include basic Machine Learning techniques (Artificial Neural Networks, Decision trees, Support Vector Machines) and additional useful procedures are described to show how they can be applied to address a complex problem such as landslide assessment. Two types of models are proposed in the work herein that are useful for describing landslide susceptibility and landslide prediction. The region of Halenkovice in Czech Republic is presented as a case study to illustrate and bring closer the practical aspects of landslide assessment. These aspects consider data preparation and preprocessing, scale effects, model optimization, and evaluation. The results show that Support Vector Machines and similar Machine Learning (ML) techniques can be successfully applied to address the zoning of landslide susceptibility, which might be an important breakthrough for potential applications in regional planning and decision-making.
PB  - Cham: Springer International Publishing
T2  - GeoComputational Analysis and Modeling of Regional Systems
T1  - Machine Learning and Landslide Assessment in a GIS Environment
EP  - 213
SP  - 191
DO  - 10.1007/978-3-319-59511-5_11
ER  - 

@inbook{
author = "Đurić, Uroš and Bajat, Branislav and Abolmasov, Biljana and Kovačević, Miloš",
year = "2018",
abstract = "This chapter introduces theoretical and practical aspects for applying GIS and geocomputation methods in landslide assessment problems. Machine Learning techniques in combination with GIS are proven useful for computation and building of complex non-linear spatial models, which is why they have been chosen in our work. Modeling principles that include basic Machine Learning techniques (Artificial Neural Networks, Decision trees, Support Vector Machines) and additional useful procedures are described to show how they can be applied to address a complex problem such as landslide assessment. Two types of models are proposed in the work herein that are useful for describing landslide susceptibility and landslide prediction. The region of Halenkovice in Czech Republic is presented as a case study to illustrate and bring closer the practical aspects of landslide assessment. These aspects consider data preparation and preprocessing, scale effects, model optimization, and evaluation. The results show that Support Vector Machines and similar Machine Learning (ML) techniques can be successfully applied to address the zoning of landslide susceptibility, which might be an important breakthrough for potential applications in regional planning and decision-making.",
publisher = "Cham: Springer International Publishing",
journal = "GeoComputational Analysis and Modeling of Regional Systems",
booktitle = "Machine Learning and Landslide Assessment in a GIS Environment",
pages = "213-191",
doi = "10.1007/978-3-319-59511-5_11"
}

Đurić, U., Bajat, B., Abolmasov, B.,& Kovačević, M.. (2018). Machine Learning and Landslide Assessment in a GIS Environment. in GeoComputational Analysis and Modeling of Regional Systems
Cham: Springer International Publishing., 191-213.
https://doi.org/10.1007/978-3-319-59511-5_11

Đurić U, Bajat B, Abolmasov B, Kovačević M. Machine Learning and Landslide Assessment in a GIS Environment. in GeoComputational Analysis and Modeling of Regional Systems. 2018;:191-213.
doi:10.1007/978-3-319-59511-5_11 .

Đurić, Uroš, Bajat, Branislav, Abolmasov, Biljana, Kovačević, Miloš, "Machine Learning and Landslide Assessment in a GIS Environment" in GeoComputational Analysis and Modeling of Regional Systems (2018):191-213,
https://doi.org/10.1007/978-3-319-59511-5_11 . .

TY  - CONF
AU  - Stojadinović, Zoran I.
AU  - Kovačević, Miloš
AU  - Marinković, Dejan
AU  - Stojadinović, Božidar
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2174
AB  - This paper presents an earthquake damage and repair cost prediction framework for individual residential buildings and portfolios of residential buildings in a municipal area in a region where the seismological networks are sparse and the structural engineering data on the existing residential building stock is poor. 
The proposed data driven framework is based on the damage and reconstruction data from an actual earthquake, in this case, the M5.4 November 3, 2010 Kraljevo, Serbia, earthquake. It belongs to a more general class of hybrid building portfolio vulnerability models. The earthquake in the model is defined by its magnitude and epicenter location. The geographical distribution of the intensity of the earthquake at the location of the buildings is modeled using the 2013 Akkar-Sandikkaya-Bommer ground motion prediction model suitable for seismically active crustal regions in Europe, with the peak ground acceleration as the intensity measure. The data on the soil type distribution was collected form the municipality building department sources. The residential building stock was classified into six types by identifying typical architecture layouts, structural systems and elements. The residential building damage was surveyed after the 2010 Kraljevo earthquake by local engineers using a locally-developed survey form. The form contained the information about the individual damage, classified into four categories ranging from slight damage to collapse, varying amount of building-specific details, and addresses from which geographic locations of the buildings were derived. 
A random forest machine-learning algorithm was used to derive a predictive model for residential building portfolio seismic damage and repair cost using a portion of the 2010 Kraljevo data as the learning dataset. The model outputs both the individual building fragility and the aggregate portfolio-level vulnerability data. The calculation of the expected repair cost for each building type was done using an expert-defined matrix that specifies average repair costs for each building type and damage category. The model is verified on a separate test portion of the 2010 Kraljevo dataset, yielding a satisfactory relative error when comparing total predicted to total actual repair costs. 
The model is limited to regions with similar seismicity and similar building stock. However, there many regions in the Balkans that fit this constraint. The proposed framework is, however, more general. It can be applied to other regions with different seismicity and building stock using the data from a recent earthquake as its learning input dataset and an expert-defined repair cost matrix for analyzing the repair cost scenarios.
C3  - 16th World Conference on Earthquake Engineering (16WCEE)
T1  - Data-Driven Housing Damage and Repair Cost Prediction Framework Based on The 2010 Kraljevo Earthquake Data
UR  - https://hdl.handle.net/21.15107/rcub_grafar_2174
ER  - 

@conference{
author = "Stojadinović, Zoran I. and Kovačević, Miloš and Marinković, Dejan and Stojadinović, Božidar",
year = "2017",
abstract = "This paper presents an earthquake damage and repair cost prediction framework for individual residential buildings and portfolios of residential buildings in a municipal area in a region where the seismological networks are sparse and the structural engineering data on the existing residential building stock is poor. 
The proposed data driven framework is based on the damage and reconstruction data from an actual earthquake, in this case, the M5.4 November 3, 2010 Kraljevo, Serbia, earthquake. It belongs to a more general class of hybrid building portfolio vulnerability models. The earthquake in the model is defined by its magnitude and epicenter location. The geographical distribution of the intensity of the earthquake at the location of the buildings is modeled using the 2013 Akkar-Sandikkaya-Bommer ground motion prediction model suitable for seismically active crustal regions in Europe, with the peak ground acceleration as the intensity measure. The data on the soil type distribution was collected form the municipality building department sources. The residential building stock was classified into six types by identifying typical architecture layouts, structural systems and elements. The residential building damage was surveyed after the 2010 Kraljevo earthquake by local engineers using a locally-developed survey form. The form contained the information about the individual damage, classified into four categories ranging from slight damage to collapse, varying amount of building-specific details, and addresses from which geographic locations of the buildings were derived. 
A random forest machine-learning algorithm was used to derive a predictive model for residential building portfolio seismic damage and repair cost using a portion of the 2010 Kraljevo data as the learning dataset. The model outputs both the individual building fragility and the aggregate portfolio-level vulnerability data. The calculation of the expected repair cost for each building type was done using an expert-defined matrix that specifies average repair costs for each building type and damage category. The model is verified on a separate test portion of the 2010 Kraljevo dataset, yielding a satisfactory relative error when comparing total predicted to total actual repair costs. 
The model is limited to regions with similar seismicity and similar building stock. However, there many regions in the Balkans that fit this constraint. The proposed framework is, however, more general. It can be applied to other regions with different seismicity and building stock using the data from a recent earthquake as its learning input dataset and an expert-defined repair cost matrix for analyzing the repair cost scenarios.",
journal = "16th World Conference on Earthquake Engineering (16WCEE)",
title = "Data-Driven Housing Damage and Repair Cost Prediction Framework Based on The 2010 Kraljevo Earthquake Data",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2174"
}

Stojadinović, Z. I., Kovačević, M., Marinković, D.,& Stojadinović, B.. (2017). Data-Driven Housing Damage and Repair Cost Prediction Framework Based on The 2010 Kraljevo Earthquake Data. in 16th World Conference on Earthquake Engineering (16WCEE).
https://hdl.handle.net/21.15107/rcub_grafar_2174

Stojadinović ZI, Kovačević M, Marinković D, Stojadinović B. Data-Driven Housing Damage and Repair Cost Prediction Framework Based on The 2010 Kraljevo Earthquake Data. in 16th World Conference on Earthquake Engineering (16WCEE). 2017;.
https://hdl.handle.net/21.15107/rcub_grafar_2174 .

Stojadinović, Zoran I., Kovačević, Miloš, Marinković, Dejan, Stojadinović, Božidar, "Data-Driven Housing Damage and Repair Cost Prediction Framework Based on The 2010 Kraljevo Earthquake Data" in 16th World Conference on Earthquake Engineering (16WCEE) (2017),
https://hdl.handle.net/21.15107/rcub_grafar_2174 .

TY  - JOUR
AU  - Nedeljković, Đorđe
AU  - Kovačević, Miloš
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/898
AB  - During a construction project lifecycle, an extensive corpus of unstructured or semistructured text documents is generated. The nature of unstructured sources impedes users' acquisition, analysis, and reuse of relevant information in an integral form, leading to a possible reduction in project performance because of untimely or inadequate decisions. This paper explores the representation of information from unstructured documents in the form of a key-phrase network, intended to provide users with the possibility to visualize and analyze valuable project facts with less effort. A network of key phrases automatically extracted from various types of unstructured documents, with relations based on contextual similarity, was implemented as a graph database, enabling project participants to extract and visualize various patterns in data. With the objective of constructing a domain-independent key-phrase network with minimal expert involvement, an approach to detect key phrases in a multilingual environment was examined by using measures of association between words while avoiding text content from less informative contexts. A possible application is demonstrated using key-phrase networks generated from two complex international construction projects.
PB  - American Society of Civil Engineers (ASCE)
T2  - Journal of Computing in Civil Engineering
T1  - Building a Construction Project Key-Phrase Network from Unstructured Text Documents
IS  - 6
VL  - 31
DO  - 10.1061/(ASCE)CP.1943-5487.0000708
ER  - 

@article{
author = "Nedeljković, Đorđe and Kovačević, Miloš",
year = "2017",
abstract = "During a construction project lifecycle, an extensive corpus of unstructured or semistructured text documents is generated. The nature of unstructured sources impedes users' acquisition, analysis, and reuse of relevant information in an integral form, leading to a possible reduction in project performance because of untimely or inadequate decisions. This paper explores the representation of information from unstructured documents in the form of a key-phrase network, intended to provide users with the possibility to visualize and analyze valuable project facts with less effort. A network of key phrases automatically extracted from various types of unstructured documents, with relations based on contextual similarity, was implemented as a graph database, enabling project participants to extract and visualize various patterns in data. With the objective of constructing a domain-independent key-phrase network with minimal expert involvement, an approach to detect key phrases in a multilingual environment was examined by using measures of association between words while avoiding text content from less informative contexts. A possible application is demonstrated using key-phrase networks generated from two complex international construction projects.",
publisher = "American Society of Civil Engineers (ASCE)",
journal = "Journal of Computing in Civil Engineering",
title = "Building a Construction Project Key-Phrase Network from Unstructured Text Documents",
number = "6",
volume = "31",
doi = "10.1061/(ASCE)CP.1943-5487.0000708"
}

Nedeljković, Đ.,& Kovačević, M.. (2017). Building a Construction Project Key-Phrase Network from Unstructured Text Documents. in Journal of Computing in Civil Engineering
American Society of Civil Engineers (ASCE)., 31(6).
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000708

Nedeljković Đ, Kovačević M. Building a Construction Project Key-Phrase Network from Unstructured Text Documents. in Journal of Computing in Civil Engineering. 2017;31(6).
doi:10.1061/(ASCE)CP.1943-5487.0000708 .

Nedeljković, Đorđe, Kovačević, Miloš, "Building a Construction Project Key-Phrase Network from Unstructured Text Documents" in Journal of Computing in Civil Engineering, 31, no. 6 (2017),
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000708 . .

TY  - JOUR
AU  - Locatelli, Giorgio
AU  - Mikić, Miljan
AU  - Kovačević, Miloš
AU  - Brookes, Naomi
AU  - Ivanišević, Nenad
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/869
AB  - Megaprojects are often associated with poor delivery performance and poor benefits realization. This article provides a method of identifying, in a quantitative and rigorous manner, the characteristics related to project management success in megaprojects. It provides an investigation of how stakeholders can use this knowledge to ensure more effective design and delivery for megaprojects. The research is grounded in 44 megaprojects and a systematic, empirically based methodology that employs the Fisher's exact test and machine learning techniques to identify the correlation between megaprojects' characteristics and performance, paving the way to an understanding of their causation.
PB  - SAGE Publications Inc.
T2  - Project Management Journal
T1  - The Successful Delivery of Megaprojects: A Novel Research Method
IS  - 5
SP  - 78
VL  - 48
DO  - 10.1177/875697281704800506
ER  - 

@article{
author = "Locatelli, Giorgio and Mikić, Miljan and Kovačević, Miloš and Brookes, Naomi and Ivanišević, Nenad",
year = "2017",
abstract = "Megaprojects are often associated with poor delivery performance and poor benefits realization. This article provides a method of identifying, in a quantitative and rigorous manner, the characteristics related to project management success in megaprojects. It provides an investigation of how stakeholders can use this knowledge to ensure more effective design and delivery for megaprojects. The research is grounded in 44 megaprojects and a systematic, empirically based methodology that employs the Fisher's exact test and machine learning techniques to identify the correlation between megaprojects' characteristics and performance, paving the way to an understanding of their causation.",
publisher = "SAGE Publications Inc.",
journal = "Project Management Journal",
title = "The Successful Delivery of Megaprojects: A Novel Research Method",
number = "5",
pages = "78",
volume = "48",
doi = "10.1177/875697281704800506"
}

Locatelli, G., Mikić, M., Kovačević, M., Brookes, N.,& Ivanišević, N.. (2017). The Successful Delivery of Megaprojects: A Novel Research Method. in Project Management Journal
SAGE Publications Inc.., 48(5), 78.
https://doi.org/10.1177/875697281704800506

Locatelli G, Mikić M, Kovačević M, Brookes N, Ivanišević N. The Successful Delivery of Megaprojects: A Novel Research Method. in Project Management Journal. 2017;48(5):78.
doi:10.1177/875697281704800506 .

Locatelli, Giorgio, Mikić, Miljan, Kovačević, Miloš, Brookes, Naomi, Ivanišević, Nenad, "The Successful Delivery of Megaprojects: A Novel Research Method" in Project Management Journal, 48, no. 5 (2017):78,
https://doi.org/10.1177/875697281704800506 . .

TY  - JOUR
AU  - Samardžić-Petrović, Mileva
AU  - Kovačević, Miloš
AU  - Bajat, Branislav
AU  - Dragićević, Suzana
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/865
AB  - The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN), and Support Vector Machines (SVM) for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.
PB  - MDPI AG
T2  - Isprs International Journal of Geo-Information
T1  - Machine Learning Techniques for Modelling Short Term Land-Use Change
IS  - 12
VL  - 6
DO  - 10.3390/ijgi6120387
ER  - 

@article{
author = "Samardžić-Petrović, Mileva and Kovačević, Miloš and Bajat, Branislav and Dragićević, Suzana",
year = "2017",
abstract = "The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN), and Support Vector Machines (SVM) for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.",
publisher = "MDPI AG",
journal = "Isprs International Journal of Geo-Information",
title = "Machine Learning Techniques for Modelling Short Term Land-Use Change",
number = "12",
volume = "6",
doi = "10.3390/ijgi6120387"
}

Samardžić-Petrović, M., Kovačević, M., Bajat, B.,& Dragićević, S.. (2017). Machine Learning Techniques for Modelling Short Term Land-Use Change. in Isprs International Journal of Geo-Information
MDPI AG., 6(12).
https://doi.org/10.3390/ijgi6120387

Samardžić-Petrović M, Kovačević M, Bajat B, Dragićević S. Machine Learning Techniques for Modelling Short Term Land-Use Change. in Isprs International Journal of Geo-Information. 2017;6(12).
doi:10.3390/ijgi6120387 .

Samardžić-Petrović, Mileva, Kovačević, Miloš, Bajat, Branislav, Dragićević, Suzana, "Machine Learning Techniques for Modelling Short Term Land-Use Change" in Isprs International Journal of Geo-Information, 6, no. 12 (2017),
https://doi.org/10.3390/ijgi6120387 . .

TY  - JOUR
AU  - Locatelli, Giorgio
AU  - Mikić, Miljan
AU  - Kovačević, Miloš
AU  - Brookes, Naomi
AU  - Ivanišević, Nenad
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1844
AB  - Megaprojects are often associated with poor delivery performance and poor benefits realization. This article provides a method of identifying, in a quantitative and rigorous manner, the characteristics related to project management success in megaprojects. It provides an investigation of how stakeholders can use this knowledge to ensure more effective design and delivery for megaprojects. The research is grounded in 44 megaprojects and a systematic, empirically based methodology that employs the Fisher's exact test and machine learning techniques to identify the correlation between megaprojects' characteristics and performance, paving the way to an understanding of their causation.
PB  - SAGE Publications Inc.
T2  - Project Management Journal
T1  - The Successful Delivery of Megaprojects: A Novel Research Method
IS  - 5
SP  - 78
VL  - 48
DO  - 10.1177/875697281704800506
ER  - 

@article{
author = "Locatelli, Giorgio and Mikić, Miljan and Kovačević, Miloš and Brookes, Naomi and Ivanišević, Nenad",
year = "2017",
abstract = "Megaprojects are often associated with poor delivery performance and poor benefits realization. This article provides a method of identifying, in a quantitative and rigorous manner, the characteristics related to project management success in megaprojects. It provides an investigation of how stakeholders can use this knowledge to ensure more effective design and delivery for megaprojects. The research is grounded in 44 megaprojects and a systematic, empirically based methodology that employs the Fisher's exact test and machine learning techniques to identify the correlation between megaprojects' characteristics and performance, paving the way to an understanding of their causation.",
publisher = "SAGE Publications Inc.",
journal = "Project Management Journal",
title = "The Successful Delivery of Megaprojects: A Novel Research Method",
number = "5",
pages = "78",
volume = "48",
doi = "10.1177/875697281704800506"
}

Locatelli, G., Mikić, M., Kovačević, M., Brookes, N.,& Ivanišević, N.. (2017). The Successful Delivery of Megaprojects: A Novel Research Method. in Project Management Journal
SAGE Publications Inc.., 48(5), 78.
https://doi.org/10.1177/875697281704800506

Locatelli G, Mikić M, Kovačević M, Brookes N, Ivanišević N. The Successful Delivery of Megaprojects: A Novel Research Method. in Project Management Journal. 2017;48(5):78.
doi:10.1177/875697281704800506 .

Locatelli, Giorgio, Mikić, Miljan, Kovačević, Miloš, Brookes, Naomi, Ivanišević, Nenad, "The Successful Delivery of Megaprojects: A Novel Research Method" in Project Management Journal, 48, no. 5 (2017):78,
https://doi.org/10.1177/875697281704800506 . .

TY  - CONF
AU  - Kovačević, Miloš
PY  - 2016
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/810
AB  - The structure and properties of the web graph originating from the air traffic safety community could reveal many interesting relationships which exist between key organizations in the field. Therefore, an attempt is made to collect a representative sample of relevant web pages from different sources, by utilizing an intelligent software agent capable of recognizing the concept of a typical air traffic safety page. Starting from a seed set of thirty relevant pages, the agent collected nearly 200,000 on-topic pages and recorded the links between them. The analysis of the compiled graph detected the most relevant pages and sites, common linking patterns and popular keywords used for titles and links texts.
C3  - Proceedings of the Third International Conference On Traffic and Transport Engineering (Ictte)
T1  - Web graph analysis of the air traffic safety community
EP  - 6
SP  - 1
UR  - https://hdl.handle.net/21.15107/rcub_grafar_810
ER  - 

@conference{
author = "Kovačević, Miloš",
year = "2016",
abstract = "The structure and properties of the web graph originating from the air traffic safety community could reveal many interesting relationships which exist between key organizations in the field. Therefore, an attempt is made to collect a representative sample of relevant web pages from different sources, by utilizing an intelligent software agent capable of recognizing the concept of a typical air traffic safety page. Starting from a seed set of thirty relevant pages, the agent collected nearly 200,000 on-topic pages and recorded the links between them. The analysis of the compiled graph detected the most relevant pages and sites, common linking patterns and popular keywords used for titles and links texts.",
journal = "Proceedings of the Third International Conference On Traffic and Transport Engineering (Ictte)",
title = "Web graph analysis of the air traffic safety community",
pages = "6-1",
url = "https://hdl.handle.net/21.15107/rcub_grafar_810"
}

Kovačević, M.. (2016). Web graph analysis of the air traffic safety community. in Proceedings of the Third International Conference On Traffic and Transport Engineering (Ictte), 1-6.
https://hdl.handle.net/21.15107/rcub_grafar_810

Kovačević M. Web graph analysis of the air traffic safety community. in Proceedings of the Third International Conference On Traffic and Transport Engineering (Ictte). 2016;:1-6.
https://hdl.handle.net/21.15107/rcub_grafar_810 .

Kovačević, Miloš, "Web graph analysis of the air traffic safety community" in Proceedings of the Third International Conference On Traffic and Transport Engineering (Ictte) (2016):1-6,
https://hdl.handle.net/21.15107/rcub_grafar_810 .

TY  - JOUR
AU  - Samardžić-Petrović, Mileva
AU  - Dragićević, Suzana
AU  - Kovačević, Miloš
AU  - Bajat, Branislav
PY  - 2016
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/777
AB  - Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation-based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM-based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.
PB  - Blackwell Publishing Ltd
T2  - Transactions in Gis
T1  - Modeling Urban Land Use Changes Using Support Vector Machines
EP  - 734
IS  - 5
SP  - 718
VL  - 20
DO  - 10.1111/tgis.12174
ER  - 

@article{
author = "Samardžić-Petrović, Mileva and Dragićević, Suzana and Kovačević, Miloš and Bajat, Branislav",
year = "2016",
abstract = "Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation-based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM-based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.",
publisher = "Blackwell Publishing Ltd",
journal = "Transactions in Gis",
title = "Modeling Urban Land Use Changes Using Support Vector Machines",
pages = "734-718",
number = "5",
volume = "20",
doi = "10.1111/tgis.12174"
}

Samardžić-Petrović, M., Dragićević, S., Kovačević, M.,& Bajat, B.. (2016). Modeling Urban Land Use Changes Using Support Vector Machines. in Transactions in Gis
Blackwell Publishing Ltd., 20(5), 718-734.
https://doi.org/10.1111/tgis.12174

Samardžić-Petrović M, Dragićević S, Kovačević M, Bajat B. Modeling Urban Land Use Changes Using Support Vector Machines. in Transactions in Gis. 2016;20(5):718-734.
doi:10.1111/tgis.12174 .

Samardžić-Petrović, Mileva, Dragićević, Suzana, Kovačević, Miloš, Bajat, Branislav, "Modeling Urban Land Use Changes Using Support Vector Machines" in Transactions in Gis, 20, no. 5 (2016):718-734,
https://doi.org/10.1111/tgis.12174 . .

TY  - JOUR
AU  - Lazarević, Luka
AU  - Kovačević, Miloš
AU  - Popović, Zdenka
PY  - 2015
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/713
AB  - European transport policy, defined in the White Paper, supports shift from road to rail and waterborne transport. The hypothesis of the paper is that changes in the economic environment influence rail traffic volume. Therefore, a model for prediction of rail traffic volume applied in different economic contexts could be a valuable tool for the transport planners. The model was built using common Machine Learning techniques that learn from the past experience. In the model preparation, world development indicators defined by the World Bank were used as input parameters.
PB  - Univerzitet u Nišu, Niš
T2  - Facta universitatis - series: Mechanical Engineering
T1  - Rail traffic volume estimation based on world development indicators
EP  - 141
IS  - 2
SP  - 133
VL  - 13
UR  - https://hdl.handle.net/21.15107/rcub_grafar_713
ER  - 

@article{
author = "Lazarević, Luka and Kovačević, Miloš and Popović, Zdenka",
year = "2015",
abstract = "European transport policy, defined in the White Paper, supports shift from road to rail and waterborne transport. The hypothesis of the paper is that changes in the economic environment influence rail traffic volume. Therefore, a model for prediction of rail traffic volume applied in different economic contexts could be a valuable tool for the transport planners. The model was built using common Machine Learning techniques that learn from the past experience. In the model preparation, world development indicators defined by the World Bank were used as input parameters.",
publisher = "Univerzitet u Nišu, Niš",
journal = "Facta universitatis - series: Mechanical Engineering",
title = "Rail traffic volume estimation based on world development indicators",
pages = "141-133",
number = "2",
volume = "13",
url = "https://hdl.handle.net/21.15107/rcub_grafar_713"
}

Lazarević, L., Kovačević, M.,& Popović, Z.. (2015). Rail traffic volume estimation based on world development indicators. in Facta universitatis - series: Mechanical Engineering
Univerzitet u Nišu, Niš., 13(2), 133-141.
https://hdl.handle.net/21.15107/rcub_grafar_713

Lazarević L, Kovačević M, Popović Z. Rail traffic volume estimation based on world development indicators. in Facta universitatis - series: Mechanical Engineering. 2015;13(2):133-141.
https://hdl.handle.net/21.15107/rcub_grafar_713 .

Lazarević, Luka, Kovačević, Miloš, Popović, Zdenka, "Rail traffic volume estimation based on world development indicators" in Facta universitatis - series: Mechanical Engineering, 13, no. 2 (2015):133-141,
https://hdl.handle.net/21.15107/rcub_grafar_713 .

TY  - JOUR
AU  - Samardžić-Petrović, Mileva
AU  - Dragićević, Suzana
AU  - Bajat, Branislav
AU  - Kovačević, Miloš
PY  - 2015
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1043
T2  - Geomatica
T1  - Exploring the Decision Tree Method for Modelling Urban Land Use Change
EP  - 325
IS  - 3
SP  - 313
VL  - 69
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1043
ER  - 

@article{
author = "Samardžić-Petrović, Mileva and Dragićević, Suzana and Bajat, Branislav and Kovačević, Miloš",
year = "2015",
journal = "Geomatica",
title = "Exploring the Decision Tree Method for Modelling Urban Land Use Change",
pages = "325-313",
number = "3",
volume = "69",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1043"
}

Samardžić-Petrović, M., Dragićević, S., Bajat, B.,& Kovačević, M.. (2015). Exploring the Decision Tree Method for Modelling Urban Land Use Change. in Geomatica, 69(3), 313-325.
https://hdl.handle.net/21.15107/rcub_grafar_1043

Samardžić-Petrović M, Dragićević S, Bajat B, Kovačević M. Exploring the Decision Tree Method for Modelling Urban Land Use Change. in Geomatica. 2015;69(3):313-325.
https://hdl.handle.net/21.15107/rcub_grafar_1043 .

Samardžić-Petrović, Mileva, Dragićević, Suzana, Bajat, Branislav, Kovačević, Miloš, "Exploring the Decision Tree Method for Modelling Urban Land Use Change" in Geomatica, 69, no. 3 (2015):313-325,
https://hdl.handle.net/21.15107/rcub_grafar_1043 .

TY  - CONF
AU  - Nedeljković, Đorđe
AU  - Kovačević, Miloš
PY  - 2015
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/682
AB  - This paper addresses the problem of automatic concept detection in a construction project documentation with the aim of increasing the efficiency of information retrieval for all stakeholders in real-time in cases when documents lack previously defined metadata or when the semantic knowledge is not taken into account. Introduction of significant concepts, in a user-specific problem domain would improve retrieval of relevant documents. Concepts, represented as word pairs, were ranked by using different statistical measures for semantic similarity in order to compare the observed and the expected co-occurrence under a null model. Experiments suggested that using the statistical measures in different combinations yielded better performance when compared to their individual usage. The proposed approach was tested on several data sets compiled from the documents originating from a smelting project in Bor in the Republic of Serbia. Common information retrieval measures, precision and recall, were calculated for different combinations of word span, context scope and applied statistical measures, and further discussed taking into account the complexity and specificity of the observed construction project documentation.
PB  - Civil-Comp Press
C3  - Civil-Comp Proceedings
T1  - Detecting Concepts in Construction Project Documents using Statistical Measures for Semantic Similarity
VL  - 108
UR  - https://hdl.handle.net/21.15107/rcub_grafar_682
ER  - 

@conference{
author = "Nedeljković, Đorđe and Kovačević, Miloš",
year = "2015",
abstract = "This paper addresses the problem of automatic concept detection in a construction project documentation with the aim of increasing the efficiency of information retrieval for all stakeholders in real-time in cases when documents lack previously defined metadata or when the semantic knowledge is not taken into account. Introduction of significant concepts, in a user-specific problem domain would improve retrieval of relevant documents. Concepts, represented as word pairs, were ranked by using different statistical measures for semantic similarity in order to compare the observed and the expected co-occurrence under a null model. Experiments suggested that using the statistical measures in different combinations yielded better performance when compared to their individual usage. The proposed approach was tested on several data sets compiled from the documents originating from a smelting project in Bor in the Republic of Serbia. Common information retrieval measures, precision and recall, were calculated for different combinations of word span, context scope and applied statistical measures, and further discussed taking into account the complexity and specificity of the observed construction project documentation.",
publisher = "Civil-Comp Press",
journal = "Civil-Comp Proceedings",
title = "Detecting Concepts in Construction Project Documents using Statistical Measures for Semantic Similarity",
volume = "108",
url = "https://hdl.handle.net/21.15107/rcub_grafar_682"
}

Nedeljković, Đ.,& Kovačević, M.. (2015). Detecting Concepts in Construction Project Documents using Statistical Measures for Semantic Similarity. in Civil-Comp Proceedings
Civil-Comp Press., 108.
https://hdl.handle.net/21.15107/rcub_grafar_682

Nedeljković Đ, Kovačević M. Detecting Concepts in Construction Project Documents using Statistical Measures for Semantic Similarity. in Civil-Comp Proceedings. 2015;108.
https://hdl.handle.net/21.15107/rcub_grafar_682 .

Nedeljković, Đorđe, Kovačević, Miloš, "Detecting Concepts in Construction Project Documents using Statistical Measures for Semantic Similarity" in Civil-Comp Proceedings, 108 (2015),
https://hdl.handle.net/21.15107/rcub_grafar_682 .

TY  - CONF
AU  - Samardžić-Petrović, Mileva
AU  - Bajat, Branislav
AU  - Kovačević, Miloš
AU  - Dragićević, Suzana
PY  - 2014
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1196
PB  - Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna
C3  - Extended Abstract Proceedings of the GIScience 2014, 23-26 September, Vienna, Austria
T1  - Sensitivity analysis of Support Vector Machine land use change modelling method
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1196
ER  - 

@conference{
author = "Samardžić-Petrović, Mileva and Bajat, Branislav and Kovačević, Miloš and Dragićević, Suzana",
year = "2014",
publisher = "Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna",
journal = "Extended Abstract Proceedings of the GIScience 2014, 23-26 September, Vienna, Austria",
title = "Sensitivity analysis of Support Vector Machine land use change modelling method",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1196"
}

Samardžić-Petrović, M., Bajat, B., Kovačević, M.,& Dragićević, S.. (2014). Sensitivity analysis of Support Vector Machine land use change modelling method. in Extended Abstract Proceedings of the GIScience 2014, 23-26 September, Vienna, Austria
Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna..
https://hdl.handle.net/21.15107/rcub_grafar_1196

Samardžić-Petrović M, Bajat B, Kovačević M, Dragićević S. Sensitivity analysis of Support Vector Machine land use change modelling method. in Extended Abstract Proceedings of the GIScience 2014, 23-26 September, Vienna, Austria. 2014;.
https://hdl.handle.net/21.15107/rcub_grafar_1196 .

Samardžić-Petrović, Mileva, Bajat, Branislav, Kovačević, Miloš, Dragićević, Suzana, "Sensitivity analysis of Support Vector Machine land use change modelling method" in Extended Abstract Proceedings of the GIScience 2014, 23-26 September, Vienna, Austria (2014),
https://hdl.handle.net/21.15107/rcub_grafar_1196 .

TY  - CONF
AU  - Lazarević, Luka
AU  - Kovačević, Miloš
AU  - Popović, Zdenka
PY  - 2014
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1171
AB  - The rail system, as a system with high capacity, the least air and water pollution, solvable noise and vibration emission problem and the least space usage, is competitive with other modes of transportation. In addition, European transport policy supports shift from road to rail and waterborne transport. It is expected that changes in rail traffic volume be followed by changes in certain economic parameters. On the other hand, changes in economy influence rail traffic volume. The aim of this paper is creation of the data driven models for prediction of rail traffic volume in different economic contexts, using world development indicators, defined by the World Bank, as input parameters.
PB  - Faculty of Mechanical Engineering, Niš
C3  - Proceedings / XVI Scientific-Expert Conference on Railways - RAILCON 14, October 09-10, 2014, Niš
T1  - Data driven model for prediction of rail traffic
EP  - 184
SP  - 181
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1171
ER  - 

@conference{
author = "Lazarević, Luka and Kovačević, Miloš and Popović, Zdenka",
year = "2014",
abstract = "The rail system, as a system with high capacity, the least air and water pollution, solvable noise and vibration emission problem and the least space usage, is competitive with other modes of transportation. In addition, European transport policy supports shift from road to rail and waterborne transport. It is expected that changes in rail traffic volume be followed by changes in certain economic parameters. On the other hand, changes in economy influence rail traffic volume. The aim of this paper is creation of the data driven models for prediction of rail traffic volume in different economic contexts, using world development indicators, defined by the World Bank, as input parameters.",
publisher = "Faculty of Mechanical Engineering, Niš",
journal = "Proceedings / XVI Scientific-Expert Conference on Railways - RAILCON 14, October 09-10, 2014, Niš",
title = "Data driven model for prediction of rail traffic",
pages = "184-181",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1171"
}

Lazarević, L., Kovačević, M.,& Popović, Z.. (2014). Data driven model for prediction of rail traffic. in Proceedings / XVI Scientific-Expert Conference on Railways - RAILCON 14, October 09-10, 2014, Niš
Faculty of Mechanical Engineering, Niš., 181-184.
https://hdl.handle.net/21.15107/rcub_grafar_1171

Lazarević L, Kovačević M, Popović Z. Data driven model for prediction of rail traffic. in Proceedings / XVI Scientific-Expert Conference on Railways - RAILCON 14, October 09-10, 2014, Niš. 2014;:181-184.
https://hdl.handle.net/21.15107/rcub_grafar_1171 .

Lazarević, Luka, Kovačević, Miloš, Popović, Zdenka, "Data driven model for prediction of rail traffic" in Proceedings / XVI Scientific-Expert Conference on Railways - RAILCON 14, October 09-10, 2014, Niš (2014):181-184,
https://hdl.handle.net/21.15107/rcub_grafar_1171 .

TY  - CONF
AU  - Samardžić-Petrović, Mileva
AU  - Bajat, Branislav
AU  - Kovačević, Miloš
PY  - 2013
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/533
AB  - Techniques for evaluating similarities between categorical maps obtained by different spatial modeling techniques or representing similar space in different time instances are still developing. This paper reviews a current approach for assessing the similarity in land use maps that are used in city planning processes. The performance of recently designed kappa location and kappa histo measures as well as fuzzy set map comparison approach were tested on a case study area that comprises three cities of Belgrade's municipalities with different urban characteristics. By assessing similarities between the land use map of the Master plan designed for the year 2021 and the map representing the currently observed land use conditions, the level of realized planned activities as well as the level of discrepancy from the Master plan could be evaluated.
PB  - Technical University of Ostrava, Czech Rebublic
C3  - Gis Ostrava 2013 - Geoinformatics for City Transformation
T1  - Assesing similarities between planned and observed land use maps: the Belgrade's municipalities case study
EP  - 23
SP  - 13
UR  - https://hdl.handle.net/21.15107/rcub_grafar_533
ER  - 

@conference{
author = "Samardžić-Petrović, Mileva and Bajat, Branislav and Kovačević, Miloš",
year = "2013",
abstract = "Techniques for evaluating similarities between categorical maps obtained by different spatial modeling techniques or representing similar space in different time instances are still developing. This paper reviews a current approach for assessing the similarity in land use maps that are used in city planning processes. The performance of recently designed kappa location and kappa histo measures as well as fuzzy set map comparison approach were tested on a case study area that comprises three cities of Belgrade's municipalities with different urban characteristics. By assessing similarities between the land use map of the Master plan designed for the year 2021 and the map representing the currently observed land use conditions, the level of realized planned activities as well as the level of discrepancy from the Master plan could be evaluated.",
publisher = "Technical University of Ostrava, Czech Rebublic",
journal = "Gis Ostrava 2013 - Geoinformatics for City Transformation",
title = "Assesing similarities between planned and observed land use maps: the Belgrade's municipalities case study",
pages = "23-13",
url = "https://hdl.handle.net/21.15107/rcub_grafar_533"
}

Samardžić-Petrović, M., Bajat, B.,& Kovačević, M.. (2013). Assesing similarities between planned and observed land use maps: the Belgrade's municipalities case study. in Gis Ostrava 2013 - Geoinformatics for City Transformation
Technical University of Ostrava, Czech Rebublic., 13-23.
https://hdl.handle.net/21.15107/rcub_grafar_533

Samardžić-Petrović M, Bajat B, Kovačević M. Assesing similarities between planned and observed land use maps: the Belgrade's municipalities case study. in Gis Ostrava 2013 - Geoinformatics for City Transformation. 2013;:13-23.
https://hdl.handle.net/21.15107/rcub_grafar_533 .

Samardžić-Petrović, Mileva, Bajat, Branislav, Kovačević, Miloš, "Assesing similarities between planned and observed land use maps: the Belgrade's municipalities case study" in Gis Ostrava 2013 - Geoinformatics for City Transformation (2013):13-23,
https://hdl.handle.net/21.15107/rcub_grafar_533 .

TY  - CONF
AU  - Petronijević, Predrag
AU  - Kovačević, Miloš
AU  - Ivanišević, Nenad
AU  - Arizanović, Dragan
PY  - 2013
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2342
AB  - In everyday work, civil engineers are forced to search for technological information. Out of the many available sources of information, there is no one perfect source. The speed of finding the required information has often a key impact on the quality of their work. In this paper, the ways in which selected representatives of the group of civil engineers gain information on technology are analyzed, their grading of various sources of technological information, as well as the advantages and weakness of each of them. As a conclusion, recommendation for the preparation of an optimal source of information on technology for this specific civil engineering field is given.
PB  - Faculty of Civil Engineering University of Zagreb
C3  - 11th International Conference - organization, technology and management in construction - OTMC
T1  - Analisys of sources of information on technology for project managers in civil engineering
SP  - 308
UR  - https://hdl.handle.net/21.15107/rcub_grafar_2342
ER  - 

@conference{
author = "Petronijević, Predrag and Kovačević, Miloš and Ivanišević, Nenad and Arizanović, Dragan",
year = "2013",
abstract = "In everyday work, civil engineers are forced to search for technological information. Out of the many available sources of information, there is no one perfect source. The speed of finding the required information has often a key impact on the quality of their work. In this paper, the ways in which selected representatives of the group of civil engineers gain information on technology are analyzed, their grading of various sources of technological information, as well as the advantages and weakness of each of them. As a conclusion, recommendation for the preparation of an optimal source of information on technology for this specific civil engineering field is given.",
publisher = "Faculty of Civil Engineering University of Zagreb",
journal = "11th International Conference - organization, technology and management in construction - OTMC",
title = "Analisys of sources of information on technology for project managers in civil engineering",
pages = "308",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2342"
}

Petronijević, P., Kovačević, M., Ivanišević, N.,& Arizanović, D.. (2013). Analisys of sources of information on technology for project managers in civil engineering. in 11th International Conference - organization, technology and management in construction - OTMC
Faculty of Civil Engineering University of Zagreb., 308.
https://hdl.handle.net/21.15107/rcub_grafar_2342

Petronijević P, Kovačević M, Ivanišević N, Arizanović D. Analisys of sources of information on technology for project managers in civil engineering. in 11th International Conference - organization, technology and management in construction - OTMC. 2013;:308.
https://hdl.handle.net/21.15107/rcub_grafar_2342 .

Petronijević, Predrag, Kovačević, Miloš, Ivanišević, Nenad, Arizanović, Dragan, "Analisys of sources of information on technology for project managers in civil engineering" in 11th International Conference - organization, technology and management in construction - OTMC (2013):308,
https://hdl.handle.net/21.15107/rcub_grafar_2342 .

TY  - JOUR
AU  - Marjanović, Miloš
AU  - Kovačević, Miloš
AU  - Bajat, Branislav
AU  - Mihalić, Snježana
AU  - Abolmasov, Biljana
PY  - 2011
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/396
AB  - In this research, machine learning algorithms were compared in a landslide-susceptibility assessment. Given the input set of GIS layers for the Starca Basin, which included geological, hydrogeological, morphometric, and environmental data, a classification task was performed to classify the grid cells to: (i) landslide and non-landslide cases, (ii) different landslide types (dormant and abandoned, stabilized and suspended, reactivated). After finding the optimal parameters, C4.5 decision trees and Support Vector Machines were compared using kappa statistics. The obtained results showed that classifiers were able to distinguish between the different landslide types better than between the landslide and non-landslide instances. In addition, the Support Vector Machines classifier performed slightly better than the C4.5 in all the experiments. Promising results were achieved when classifying the grid cells into different landslide types using 20% of all the available landslide data for the model creation, reaching kappa values of about 0.65 for both algorithms.
T2  - Acta Geotechnica Slovenica
T1  - Landslide assessment of the Starca basin (Croatia) using machine learning algorithms
EP  - 55
IS  - 2
SP  - 45
VL  - 8
UR  - https://hdl.handle.net/21.15107/rcub_grafar_396
ER  - 

@article{
author = "Marjanović, Miloš and Kovačević, Miloš and Bajat, Branislav and Mihalić, Snježana and Abolmasov, Biljana",
year = "2011",
abstract = "In this research, machine learning algorithms were compared in a landslide-susceptibility assessment. Given the input set of GIS layers for the Starca Basin, which included geological, hydrogeological, morphometric, and environmental data, a classification task was performed to classify the grid cells to: (i) landslide and non-landslide cases, (ii) different landslide types (dormant and abandoned, stabilized and suspended, reactivated). After finding the optimal parameters, C4.5 decision trees and Support Vector Machines were compared using kappa statistics. The obtained results showed that classifiers were able to distinguish between the different landslide types better than between the landslide and non-landslide instances. In addition, the Support Vector Machines classifier performed slightly better than the C4.5 in all the experiments. Promising results were achieved when classifying the grid cells into different landslide types using 20% of all the available landslide data for the model creation, reaching kappa values of about 0.65 for both algorithms.",
journal = "Acta Geotechnica Slovenica",
title = "Landslide assessment of the Starca basin (Croatia) using machine learning algorithms",
pages = "55-45",
number = "2",
volume = "8",
url = "https://hdl.handle.net/21.15107/rcub_grafar_396"
}

Marjanović, M., Kovačević, M., Bajat, B., Mihalić, S.,& Abolmasov, B.. (2011). Landslide assessment of the Starca basin (Croatia) using machine learning algorithms. in Acta Geotechnica Slovenica, 8(2), 45-55.
https://hdl.handle.net/21.15107/rcub_grafar_396

Marjanović M, Kovačević M, Bajat B, Mihalić S, Abolmasov B. Landslide assessment of the Starca basin (Croatia) using machine learning algorithms. in Acta Geotechnica Slovenica. 2011;8(2):45-55.
https://hdl.handle.net/21.15107/rcub_grafar_396 .

Marjanović, Miloš, Kovačević, Miloš, Bajat, Branislav, Mihalić, Snježana, Abolmasov, Biljana, "Landslide assessment of the Starca basin (Croatia) using machine learning algorithms" in Acta Geotechnica Slovenica, 8, no. 2 (2011):45-55,
https://hdl.handle.net/21.15107/rcub_grafar_396 .