Machine learning algorithm estimation and comparison of live network values of the inputs which have the most effect on the FEC parameter in DWDM systems

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dc.contributor.authorYucel, Murat
dc.contributor.authorOsmanca, Mustafa Serdar
dc.contributor.authorMercimek, I. Fatih
dc.date.accessioned2024-09-25T19:57:40Z
dc.date.available2024-09-25T19:57:40Z
dc.date.issued2024
dc.departmentAbant İzzet Baysal Üniversitesien_US
dc.description.abstractThe purpose of this study is to determine the effect of 7 different algorithms on the FEC value, which is one of the most important parameters of the quality measurement metric in DWDM networks, analyzing these changes through machine learning algorithms has determined which parameter is the most important input affecting the FEC parameter according to the live network values. To determine the algorithm that gives the most accurate FEC value according to the estimation results in machine learning, it is aimed to make analyzes vendor agnostic. As a result; In this analysis, which was conducted with 945 live network values from 3 different vendors, it was determined that the most important parameters affecting the FEC value are the number of channels, fiber attenuation, and fiber distance, and these parameters were estimated most accurately with the decision tree machine learning algorithm.en_US
dc.identifier.doi10.2339/politeknik.1109101
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue1en_US
dc.identifier.trdizinid1227713en_US
dc.identifier.urihttps://doi.org/10.2339/politeknik.1109101
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1227713
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13544
dc.identifier.volume27en_US
dc.identifier.wosWOS:000831308400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherGazi Univen_US
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzYK_20240925en_US
dc.subjectDWDM performanceen_US
dc.subjectperformance measurementen_US
dc.subjectFECen_US
dc.subjectmachine learningen_US
dc.titleMachine learning algorithm estimation and comparison of live network values of the inputs which have the most effect on the FEC parameter in DWDM systemsen_US
dc.typeArticleen_US

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