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DC Field | Value | Language |
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dc.rights.license | restrictedAccess | - |
dc.contributor.author | Prljic, Stefan | - |
dc.contributor.author | Nikitovic Z. | - |
dc.contributor.author | Stojanovic Golubovic, Aleksandra | - |
dc.contributor.author | Cogoljevic D. | - |
dc.contributor.author | Pesic G. | - |
dc.contributor.author | Alizamir M. | - |
dc.date.accessioned | 2021-04-20T17:55:31Z | - |
dc.date.available | 2021-04-20T17:55:31Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0378-4371 | - |
dc.identifier.uri | https://scidar.kg.ac.rs/handle/123456789/11268 | - |
dc.description.abstract | © 2017 Elsevier B.V. Economic profit could be influenced by economic rents. However natural resource rents provided different impact on the economic growth or economic profit. The main focus of the study was to evaluate the economic growth as function of natural resource rents. For such a purpose machine learning approach, artificial neural network, was used. The used natural resource rents were coal rents, forest rents, mineral rents, natural gas rents and oil rents. Based on the results it is concluded that the machine learning approach could be used as the tool for the economic growth evaluation as function of natural resource rents. Moreover the more advanced approaches should be incorporated to improve more the forecasting accuracy. | - |
dc.rights | info:eu-repo/semantics/restrictedAccess | - |
dc.source | Physica A: Statistical Mechanics and its Applications | - |
dc.title | Management of business economic growth as function of resource rents | - |
dc.type | article | - |
dc.identifier.doi | 10.1016/j.physa.2017.09.087 | - |
dc.identifier.scopus | 2-s2.0-85030785797 | - |
Appears in Collections: | Faculty of Engineering, Kragujevac |
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PaperMissing.pdf Restricted Access | 29.86 kB | Adobe PDF | ![]() View/Open |
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