Enhancing Fake News Classification Through DL Models: Encoder-Decoder Architecture with BLSTM for Improved Accuracy

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dc.contributor.authorSumeyra B.P.
dc.date.accessioned2025-04-16T05:57:56Z
dc.date.available2025-04-16T05:57:56Z
dc.date.issued2024
dc.description.abstractIn today’s world, the subject of fake news is crucial. It discusses how social media and traditional media spread false stories or misinformation. This effort aims to use deep learning models to increase the accuracy of fake news classification. We investigate the utilization of bidirectional long short-term memory (BLSTM) networks and attention processes in conjunction with encoder-decoder design to boost accuracy
dc.identifier.citationSumeyra B.P / Enhancing Fake News Classification Through DL Models: Encoder-Decoder Architecture with BLSTM for Improved Accuracy / 2024 / 7M06102 - Computer Science
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/1713
dc.language.isoen
dc.publisherFaculty of Engineering and Natural Science
dc.subjectBLSTM
dc.subjectDL Model
dc.subjectfake news
dc.titleEnhancing Fake News Classification Through DL Models: Encoder-Decoder Architecture with BLSTM for Improved Accuracy
dc.typeThesis

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