Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Balamuralidhar, B.Has files: No

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    Covid-19 Fake News Detector using Hybrid Convolutional and Bi-LSTM Model
    (Institute of Electrical and Electronics Engineers Inc., 2021) Surendran, P.; Balamuralidhar, B.; Kambham, H.; Anand Kumar, M.
    Fake news is essentially incorrect and deceiving information presented to the public as news with the motive of tarnishing the reputations of individuals and organizations. In today's world, where we are so closely connected due to the internet, we see a boom in the development of social networking platforms and, thus, the amount of news circulated over the internet. We must keep in mind that fake news circulated on social media and other platforms can cause problems and false alarms in society. In some cases, false information can cause panic and have a dangerous effect on society and the people who believe it to be true. Along with the virus, the Covid-19 pandemic has also brought on distribution and spreading of misinformation. Claims of fake cures, wrong interpretations of government policies, false statistics, etc., bring about a need for a fact-checking system that keeps the circulating news in control. This work examines multiple models and builds an Artificial Intelligence system to detect Covid-19 fake news using a deep neural network. © 2021 IEEE.
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    Unsupervised Abstractive Text Summarization with Length Controlled Autoencoder
    (Institute of Electrical and Electronics Engineers Inc., 2022) Dugar, A.; Singh, G.; Balamuralidhar, B.; Anand Kumar, A.M.
    This work deals with taking an unsupervised approach to abstractive text summarization where a large set of sentences is converted into a concise summary highlighting the essential details. This is achieved with the use of an adversarial autoencoder model. The model encodes the input to a smaller latent vector and the decoder decodes this latent code to generate the higher dimensional output with some loss. Unlike variational autoencoders, AAE's use discriminators to learn using adversarial loss. K-Means clustering and language models are used to get the final summary. This model has been tested with different datasets like the Amazon, Rotten Tomatoes and Yelp reviews dataset to essentially do an opinion summarization task and this is finally evaluated using ROGUE-1, ROGUE-2,ROGUE-L and BLEU scores. The same task is also conducted on a dataset in Hindi. We obtain a ROGUE-1 score of around 24% for Amazon, Yelp and CNN/Daily Mail dataset and a score of 12% for Rotten Tomatoes while the score obtained for the Hindi news articles dataset is only 8%. © 2022 IEEE.