Detecting fake news for COVID-19 using deep learning: a review

Zaheer, Hamza; Bashir, Maryam

doi:10.1007/s11042-024-18564-7

Cited by 5 publications

(1 citation statement)

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“…Through the use of a sigmoid activation function in the input gate, the network is able to control the amount of new data that is injected, ensuring that only relevant data is added to the cell state and irrelevant data is removed. Finding fresh information that the cell state ought to comprise is its primary duty [15]. The input gate is realized by the cooperation of the hyperbolic tangent (tanh) layer and the sigmoid layer.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Word Vector Integrated With Enhanced Lstm For Classification Of Fake News

T V Divya

2024

jes

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Social-media has great impact on society by spreading news which can be fake or real without analysis. Before confessing the news predictions has to be considered. To analyse a text through machines only NLP was best method. This focuses on five stages of NLP for detection of fake or real news. For creating vector to words a neural network technique word2vec was utilized for accurate prediction of word. For identifying meaning full words tokenization was utilized. By creating the vectors, the words have to be embedded for inputting the data to train and feature learning. In dense and dropout, the neural network method LSTM is initialized for the extraction of features or words. Dropout layer involves LSTM method for co-adopt the internal data. These layers mitigate overfitting by stochastically disabling neurons during training, hence fostering resilient feature acquisition. Dense layers, by use of non-linear mappings, allow the network to comprehend intricate patterns in data. This extensive investigation offers valuable insights into the development and fine-tuning of NLP models, which in turn enhances the accuracy of false news detection. To evaluate the performances epochs are utilized were accuracy and loss was validated. Five epochs are calculated were accuracy was gradually increased and loss had been decreased. The highest accuracy achieved was 99.8% and low loss value 0.01.

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Section: Introductionmentioning

confidence: 99%