The impact of attention mechanisms and deep learning on multi-class occupation prediction

Basit Öğe Kaydı
dc.contributor.authorÇıplak, Zeki
dc.contributor.authorKotan, Muhammed
dc.date.accessioned2026-02-06T09:02:05Z
dc.date.available2026-02-06T09:02:05Z
dc.date.issued2026
dc.departmentMeslek Yüksekokulu, Gedik Meslek Yüksekokulu, Bilgisayar Programcılığı Programı
dc.description9th International Conference on Information and Communication Technology for Intelligent Systems, 4-6 April 2025, Bangkok
dc.description.abstractToday, analyzing big data sources is becoming increasingly important for understanding individuals’ occupational, personal, and social characteristics. Social media platforms contribute to solving complex problems, such as occupational prediction, through text-based content that provides clues about users’ daily lives and professions. Such analyses are critical not only at the individual level but also for understanding sectoral and societal trends. In this study, several deep learning architectures were developed to predict both occupational groups (36 classes) and individual occupations (65 classes) using a dataset of approximately 500,000 Turkish X posts. In addition to LSTM, ImprovedLSTM, GRU, and RNN models, a total of 64 different models were created using architectures enriched with Attention mechanisms. For the 36-class occupational group prediction, the best model was LSTM, achieving an accuracy of 97.13%, while for the 65-class individual occupation prediction, the best model was the Attention-based GRU, which achieved an accuracy of 93.34%. The results of the study demonstrate that deep learning techniques offer more efficient and scalable solutions by reducing the need for preprocessing. It was also found that Attention mechanisms play an important role in maintaining model accuracy as the number of classes increases.
dc.identifier.doi10.1007/978-981-96-8898-2_35
dc.identifier.endpage435
dc.identifier.isbn9789819688975
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-105028257932
dc.identifier.scopusqualityQ4
dc.identifier.startpage413
dc.identifier.urihttps://doi.org/10.1007/978-981-96-8898-2_35
dc.identifier.urihttps://hdl.handle.net/11501/2610
dc.identifier.volume1518 LNNS
dc.indekslendigikaynakScopus
dc.institutionauthorÇıplak, Zeki
dc.institutionauthorid0000-0002-0086-3223
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartof9th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAttention Mechanism
dc.subjectDeep Learning
dc.subjectGated Recurrent Unit
dc.subjectLong Short-Term Memory
dc.subjectOccupation Prediction
dc.subjectRecurrent Neural Network
dc.titleThe impact of attention mechanisms and deep learning on multi-class occupation prediction
dc.typeConference Object

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Kapalı Erişim
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed to upon submission
Açıklama:
İndir

Koleksiyon

Gedik Meslek Yüksekokulu Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu