Browsing by Author "Tembe, L.A."

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    Fake News Detection For Portuguese Language
    (Institute of Electrical and Electronics Engineers Inc., 2023) Tembe, L.A.; Anand Kumar, M.
    In this research, we employ Deep Learning to distinguish between true and false news. These approaches are used to identify false information on both trustworthy and shady platforms and sources. These models utilise various Deep Learning approaches to determine a predetermined frequency and news count. We used a wide range of labelled data to train the model. The dataset was chosen from hugging faces and consists of fake news with 20478 entries and True news with 2720 entries. We will use different news outlets, like Twitter and Facebook, to analyse the news to determine if it is true or false. Overall, tree-based LSTM, Bidirectional LSTM model and Bayesian LSTM exhibit superior accuracy. © 2023 IEEE.
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    Hate Speech Detection Using Audio in Portuguese Language
    (Springer Science and Business Media Deutschland GmbH, 2024) Tembe, L.A.; Anand Kumar, M.
    This study focuses on hate speech in Portuguese language using audio and introduces a novel methodology that integrates audio-to-text and self-image technologies to effectively tackle this problem. We utilize Machine Learning and Deep Learning models to differentiate between hate speech and normal speech. The research utilized a total of 200 datasets, which were categorized into hate speech and normal speech. These datasets were collected by me personally for this project. Four distinct models are presented in the analysis: LSTM, SVM, CNN, and Random Forest. The findings highlight the superior performance of the CNN model when applied to spectrogram data, achieving an accuracy rate of 90%. Conversely, the Random Forest model outperforms others when dealing with text data, achieving an impressive accuracy rate of 73.1%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.