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dc.contributor.authorGunduç, Semra
dc.contributor.authorEryiğit, Recep
dc.date.accessioned2021-11-30T11:39:03Z
dc.date.available2021-11-30T11:39:03Z
dc.date.issued2019-06-30
dc.identifier.urihttp://hdl.handle.net/20.500.12575/76518
dc.description.abstractIt is discussed that economic development has an essential effect on the country’s CO2 emission which plays an important role in global warming. In this research well-known machine learning algorithm Extreme Learning Machine, ELM, is used to investigate the relationship between CO2 emission and energy intensity for countries in OECD. The results indicate a strong correlation and the method perform well for estimation.tr_TR
dc.language.isoentr_TR
dc.publisherAnkara Üniversitesi Fen Fakültesitr_TR
dc.subjectExtreme Learning Machine (ELM)tr_TR
dc.subjectEnergy Intensitytr_TR
dc.subjectCO2 emissiontr_TR
dc.titleEstimating co2 Emissions By Using Energy Intensıty Data of Oecd Countriestr_TR
dc.typeArticletr_TR
dc.relation.journalCommunications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineeringtr_TR
dc.contributor.departmentBilgisayar Mühendisliğitr_TR
dc.identifier.volume69tr_TR
dc.identifier.issue1tr_TR
dc.identifier.startpage68tr_TR
dc.identifier.endpage75tr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR
dc.identifier.issn/e-issn2618-6462
dc.contributor.facultyMühendislik Fakültesitr_TR
dc.description.indexTrdizintr_TR


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