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dc.contributor.author Lee, Hyunjong -
dc.contributor.author Jung, Han Hee -
dc.contributor.author Kwak, Jeongho -
dc.contributor.author Yea, Junwoo -
dc.contributor.author Choi, Jihwan P. -
dc.contributor.author Jang, Kyung-In -
dc.date.accessioned 2024-02-27T14:10:15Z -
dc.date.available 2024-02-27T14:10:15Z -
dc.date.created 2024-02-22 -
dc.date.issued 2023-10-12 -
dc.identifier.isbn 9798350313277 -
dc.identifier.issn 2162-1241 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/47987 -
dc.description.abstract Analyzing wine using taste data is a promising field due to the explosive expansion of online commerce. However, because of the wide variety of wine types with different flavors and aromas, it is difficult for consumers to choose the wine that suits their taste, and also difficult for sellers to recommend appropriate wines to consumers. Therefore, it is necessary to numerically analyze and classify wine, and a deep learning algorithm which mimics the human brain is appropriate for analyzing the wine data [1]. In this paper, we introduce several studies of wine classification using deep learning architectures and propose preprocessing methods for applying the taste data of wine to deep learning networks. © 2023 IEEE. -
dc.language English -
dc.publisher 한국통신학회 (The Korean Institute of Communications and Information Sciences, KICS) -
dc.title Preprocessing Taste Data for Deep Neural Networks -
dc.type Conference Paper -
dc.identifier.doi 10.1109/ICTC58733.2023.10393201 -
dc.identifier.scopusid 2-s2.0-85184592028 -
dc.identifier.bibliographicCitation International Conference on Information and Communication Technology Convergence, ICTC 2023, pp.526 - 528 -
dc.identifier.url https://2023.ictc.org/program_proceeding -
dc.citation.conferencePlace KO -
dc.citation.conferencePlace 제주 -
dc.citation.endPage 528 -
dc.citation.startPage 526 -
dc.citation.title International Conference on Information and Communication Technology Convergence, ICTC 2023 -

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