Department of Electrical and Electronics Engineering

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Author: search.filters.author.Chamola, VinaySubject: search.filters.subject.Neural networks

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    Understanding the Use and Abuse of Social Media: Generalized Fake News Detection With a Multichannel Deep Neural Network
    (IEEE, 2022-11) Chamola, Vinay
    Fake news has spread across social media platforms and with the ease of access, negative consequences have come with it on individuals and society. This issue has become a focus of interest among various research communities, including artificial intelligence (AI) researchers. Existing AI-based fake news detection techniques primarily make use of a 1-D convolutional neural network (1D-CNN) with unidirectional word embedding. We propose a multichannel deep convolutional neural network (CNN) with different kernel sizes and filters as an AI technique. Multiple embedding of the same dimension with different kernel sizes technically allows the news article to be processed at different resolutions of different n-grams at the same time. Different kernel sizes increase the learning ability of the proposed classification model. The proposed model determines how to integrate these interpretations (different n-grams) most suitably. Three real-world fake news datasets were used in experiments to validate the classification performance. The classification results showed that the proposed model has high accuracy in detecting fake news. Regardless of the dataset, the proposed model can be used for fake news detection in binary classification problems
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    Traffic Jam Probability Estimation Based on Blockchain and Deep Neural Networks
    (IEEE, 2021-07) Chamola, Vinay
    The exponential surge in the number of vehicles on the road has aggravated the traffic congestion problem across the globe. Several attempts have been made over the years to predict the traffic scenario accurately and consequently avoiding further congestion. Crowdsourcing has come forward as one of the most adopted methods for predicting traffic intensity using live data. However, the privacy concerns and the lack of motivation for the live users to help in the traffic prediction process have rendered existing crowdsourcing models inefficient. Towards this end, we present an advanced blockchain-based secure crowdsourcing model. Not only does our model ensure privacy preservation of the users, but by incorporating a revenue model, it also provides them with an incentive to participate in the traffic prediction process willingly. For accurate and efficient traffic jam probability estimation, our work proposes a neural network-based smart contract to be deployed onto the blockchain network. The results reveal that the proposed model is highly efficient in terms of attaining high participation and consequently obtaining highly accurate predictions.