Predicting High Technology Exports of Countries for Sustainable Economic Growth by Using Machine Learning Techniques: The Case of Turkey

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dc.authoridUNAL, Zeynep/0000-0002-9954-1151
dc.authoridGulzar, Yonis/0000-0002-6515-1569
dc.authoridCelik, Pinar/0000-0003-0599-4086
dc.authoridKAYAKUS, Mehmet/0000-0003-0394-5862
dc.contributor.authorGulzar, Yonis
dc.contributor.authorOral, Ceren
dc.contributor.authorKayakus, Mehmet
dc.contributor.authorErdogan, Dilsad
dc.contributor.authorUnal, Zeynep
dc.contributor.authorEksili, Nisa
dc.contributor.authorCaylak, Pinar Celik
dc.date.accessioned2024-11-07T13:34:43Z
dc.date.available2024-11-07T13:34:43Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this study, the estimation of high-tech exports for Turkey's foreign trade target in line with sustainable development was carried out. The research was carried out for Turkey since it has been focusing on sustainable and environmentally friendly production and an export-oriented growth model, with a transformation in its economic growth strategy as of 2021, and high-tech products are a determining factor in the export target. In this research, three different machine learning techniques, namely artificial neural networks, logistic regression, and support vector regression, were used to determine a successful prediction method close to the ideal scenario. In the models, high technology exports for the period of 2007-2023 with data obtained from the World Bank were taken as the dependent variable, while the gross national product, number of patents, and research and development expenditures were taken as independent variables. By calculating the R2, MAPE, and MSE metrics, the success of the model with the least error was evaluated, and it was seen that artificial neural networks (ANNs) were the most successful model, with values of 94.2%, 0.011, and 0.073, respectively. The ANN model was followed by support regression and logistic regression.
dc.description.sponsorshipDeanship of Scientific Research under the Vice Presidency for Graduate Studies and Scientific Research of King Faisal University in Saudi Arabia [GRANTA093]
dc.description.sponsorshipThis work was supported by the Deanship of Scientific Research under the Vice Presidency for Graduate Studies and Scientific Research of King Faisal University in Saudi Arabia under Project GRANTA093.
dc.identifier.doi10.3390/su16135601
dc.identifier.issn2071-1050
dc.identifier.issue13
dc.identifier.scopus2-s2.0-85198406600
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su16135601
dc.identifier.urihttps://hdl.handle.net/11480/16135
dc.identifier.volume16
dc.identifier.wosWOS:001269130200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjecttechnology
dc.subjectexport
dc.subjecteconomy
dc.subjectmachine learning
dc.titlePredicting High Technology Exports of Countries for Sustainable Economic Growth by Using Machine Learning Techniques: The Case of Turkey
dc.typeArticle

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