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- W4313015005 abstract "The spread of false information is increasingly commonplace due to the internet's ease of use and people's hyperactivity on social media. Fake news spreads quickly in numerous industries such as politics, education, health, and finance, causing significant losses and having a negative impact on public life. Models centered on the Transformer infrastructure is currently showing promise in a vast scope of natural language processing tasks, featuring machine translation. So, the pre-trained deep learning-based model BERT is used in this research effort, recognizing the necessity of detecting fake news in Bangla language. Two distinct datasets used to train and test our model is publicly available. After comparing BERT model’s precision, the LSTM with regularization model, the SVM model, the NB model and the CNN model with difference evaluation matrices, we inferred that our suggested BERT model outperforms them all. Our proposed model has 95% precision rate." @default.
- W4313015005 created "2023-01-05" @default.
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- W4313015005 date "2022-01-01" @default.
- W4313015005 modified "2023-10-14" @default.
- W4313015005 title "Bengali Fake News Detection: Transfer Learning Based Technique with Masked LM Process by BERT" @default.
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- W4313015005 doi "https://doi.org/10.1007/978-3-031-20977-2_7" @default.
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