@inproceedings{f7be6c3e5d15447ba8512b5deeb8dac9,
title = "Maithi-Net: A Customized Convolution Approach for Fake News Detection using Maithili Language",
abstract = "Online news consumers now face serious difficulties due to the widespread distribution of fake news on social media platforms. To distinguish fake news from real, the paper suggested a customized CNN model named {"}Maithi-Net{"}. The model being suggested is composed of five convolution layers which are capable of automatically acquiring the distinguishing features essential for identifying fake news. Both the CGU-Maithili and ISOT fake news datasets have been used to successfully validate the proposed model. The efficacy of the model is verified with several evolution metrics like accuracy, specificity, sensitivity and F1 score. The model provides the detection accuracy 96.85 \% for CGU-Maithili and 97.28\% for ISOT fake news datasets. The experimental results show substantial gains over prior state-of-the-art results in the area of fake news detection and validate the potential of our method for categorising misinformation spread via social media.",
keywords = "CNN, Fake News, Logistic Regression, Naive Bayes, Recurrent Neural Network",
author = "Debendra Muduli and Sharma, \{Santosh Kumar\} and Dinesh Kumar and Akshat Singh and Srivastav, \{Shubham Kumar\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023 ; Conference date: 08-06-2023 Through 09-06-2023",
year = "2023",
doi = "10.1109/IC2E357697.2023.10262664",
language = "English",
series = "2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023",
}