Conference Papers

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

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Author: search.filters.author.Surendran, P.

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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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    Prediction of Drug Interactions Using Graph-Topological Features and GNN
    (Springer Science and Business Media Deutschland GmbH, 2023) Balamuralidhar, N.; Surendran, P.; Singh, G.; Bhattacharjee, S.; Shetty, R.D.
    The risk of side effects is sometimes inevitable every time two or more drugs are prescribed together, and these side effects of varying adversity levels can be referred to as drug-drug interactions (DDI). Massive amounts of data and the constraints of experimental circumstances result in clinical trials for medication compatibility being time-consuming, risky, expensive, and impractical. Recent research has demonstrated that DDI can be modelled as graphs and experimentally shown that deep learning on graphs can be a practical choice for determining the correlation and side effects of taking multiple medications simultaneously. We propose a novel approach to use inductive graph learning with GraphSAGE, along with topological features, to leverage the structural information of a graph along with the node attributes. An experimental study of the approach is done on a publicly available subset of the DrugBank dataset. We achieve our best results that are comparable with state-of-the-art works using degree, closeness and PageRank centrality measures as additional features with less computational complexity. This study can provide a reliable and cost-effective alternative to clinical trials to predict dangerous side effects, ensuring the safety of patients. © 2023, IFIP International Federation for Information Processing.