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dc.contributor.authorHWANG, SEOK HWAN-
dc.contributor.authorYulisa, Arma-
dc.contributor.authorSANG, HYEOK PARK-
dc.contributor.authorCHOI, SOO JIN-
dc.contributor.authorCHAIRATTANAWAT, CHAYANEE-
dc.date.accessioned2022-03-04T07:42:33Z-
dc.date.available2022-03-04T07:42:33Z-
dc.date.created2022-03-04-
dc.date.issued2021-11-16-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/110428-
dc.languageEnglish-
dc.publisherAk İnovasyon Danışmanlık Ltd and Erciyes University-
dc.relation.isPartOf5th International Conference series on Alternative Fuels, Energy and Environment: Future and Challenges (ICAFEE 2021)-
dc.titlePredicting total ammonia nitrogen concentration in fish waste anaerobiosis using machine learning modelling approaches-
dc.typeConference-
dc.type.rimsCONF-
dc.identifier.bibliographicCitation5th International Conference series on Alternative Fuels, Energy and Environment: Future and Challenges (ICAFEE 2021)-
dc.citation.conferenceDate2021-11-16-
dc.citation.conferencePlaceTU-
dc.citation.title5th International Conference series on Alternative Fuels, Energy and Environment: Future and Challenges (ICAFEE 2021)-
dc.contributor.affiliatedAuthorHWANG, SEOK HWAN-
dc.contributor.affiliatedAuthorYulisa, Arma-
dc.contributor.affiliatedAuthorSANG, HYEOK PARK-
dc.contributor.affiliatedAuthorCHOI, SOO JIN-
dc.contributor.affiliatedAuthorCHAIRATTANAWAT, CHAYANEE-
dc.description.journalClass1-
dc.description.journalClass1-

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황석환HWANG, SEOK HWAN
Div of Environmental Science & Enginrg
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