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https://scidar.kg.ac.rs/handle/123456789/11227
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DC Field | Value | Language |
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dc.rights.license | restrictedAccess | - |
dc.contributor.author | Cogoljevic D. | - |
dc.contributor.author | Alizamir M. | - |
dc.contributor.author | Piljan I. | - |
dc.contributor.author | Piljan T. | - |
dc.contributor.author | Prljić K. | - |
dc.contributor.author | Zimonjić S. | - |
dc.date.accessioned | 2021-04-20T17:48:55Z | - |
dc.date.available | 2021-04-20T17:48:55Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0378-4371 | - |
dc.identifier.uri | https://scidar.kg.ac.rs/handle/123456789/11227 | - |
dc.description.abstract | © 2017 Elsevier B.V. The linkage between energy resources and economic development is a topic of great interest. Research in this area is also motivated by contemporary concerns about global climate change, carbon emissions fluctuating crude oil prices, and the security of energy supply. The purpose of this research is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources. Our results indicate that GDP predictive accuracy can be improved slightly by applying a machine learning approach. | - |
dc.rights | info:eu-repo/semantics/restrictedAccess | - |
dc.source | Physica A: Statistical Mechanics and its Applications | - |
dc.title | A machine learning approach for predicting the relationship between energy resources and economic development | - |
dc.type | article | - |
dc.identifier.doi | 10.1016/j.physa.2017.12.082 | - |
dc.identifier.scopus | 2-s2.0-85039725392 | - |
Appears in Collections: | Faculty of Engineering, Kragujevac |
Files in This Item:
File | Description | Size | Format | |
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PaperMissing.pdf Restricted Access | 29.86 kB | Adobe PDF | ![]() View/Open |
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