Deep Learning Based Classification Using Academic Studies in Doc2Vec Model

dc.contributor.authorSafali, Yasar
dc.contributor.authorNergiz, Gozde
dc.contributor.authorAvaroglu, Erdinc
dc.contributor.authorDogan, Emre
dc.date.accessioned2026-08-12T16:08:33Z
dc.date.issued2019
dc.departmentFırat Üniversitesi
dc.description2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019 -- 21 September 2019 through 22 September 2019 -- Malatya -- 153040
dc.description.abstractThe number of academic studies published on the internet is increasing day by day. Researchers spend a long part of their time studying academic studies. They examine the harmony of their fields by looking at the title and summary of the studies. In this study, academic studies are classified based on deep learning by using Doc2vec word embeddings method. During the classification process, the studies were repeated in 9 different categories using repeated neural networks (Rnn's) and LSTM architectures. © 2019 IEEE.
dc.identifier.doi10.1109/IDAP.2019.8875877
dc.identifier.isbn978-172812932-7
dc.identifier.scopus2-s2.0-85074877043
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP.2019.8875877
dc.identifier.urihttps://hdl.handle.net/11508/41288
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260511
dc.subjectDeep learning; Doc2vec; Doc2vec model; LSTM; Recurrent Neural Networks; RNN; text classification; Word Embedding
dc.titleDeep Learning Based Classification Using Academic Studies in Doc2Vec Model
dc.title.alternativeTurkçe Akademik Calismalarin Doc2Vec Modeli Kullanilarak Derin Ögrenme Tabanli Siniflandirilmasi
dc.typeConference Object

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