A novel bidirectional long short-term memory model with multi-head attention for accurate language detection

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dc.contributor.authorToklu, Sinan
dc.contributor.authorKabakus, Abdullah Talha
dc.date.accessioned2025-10-11T20:47:59Z
dc.date.available2025-10-11T20:47:59Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractLanguage detection, one of the most important elements used in natural language processing, is used extensively in various applications such as machine translation, sentiment analysis, and information retrieval. Thanks to language detection, communication between people in many different countries is possible. In addition, human-animal interaction can also be carried out in this area. In this paper, a novel Bidirectional Long Short-Term Memory model with Multi-Head Attention mechanism is proposed to accurately classify text into 17 languages, namely Arabic, Danish, Dutch, English, French, German, Greek, Hindi, Italian, Kannada, Malayalam, Portuguese, Russian, Spanish, Swedish, Tamil, and Turkish. A publicly available dataset consisting of 10,337 texts written in the above-mentioned languages is utilized to train and evaluate the proposed model. The proposed novel model achieved an extraordinary accuracy, precision, recall, and F1-score of 99.9%, outperforming the state-of-the-art baseline models. In particular, the proposed model demonstrated perfect precision (100%) for 15 languages, namely Arabic, Dutch, English, French, German, Greek, Hindi, Italian, Kannada, Malayalam, Portuguese, Russian, Swedish, Tamil, and Turkish. This research highlights the effectiveness of deep learning techniques in language detection, providing promising avenues for further advances in the field of multilingual text processing.en_US
dc.identifier.doi10.17341/gazimmfd.1543854
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-105013632417en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.1543854
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21677
dc.identifier.volume40en_US
dc.identifier.wosWOS:001569394800039en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherGazi Univ, Fac Engineering Architectureen_US
dc.relation.ispartofJournal of the Faculty of Engineeringand Architecture of Gazi Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectLanguage detectionen_US
dc.subjectlanguage classificationen_US
dc.subjecttranslationen_US
dc.subjectdeep learningen_US
dc.subjectlong short-term memoryen_US
dc.titleA novel bidirectional long short-term memory model with multi-head attention for accurate language detectionen_US
dc.typeArticleen_US

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