Browsing by Author "Vasudev, R."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Epidemic Outbreak Prediction with Ensemble of Deep Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2022) Vasudev, R.; Dahikar, P.; Jain, A.; Patil, N.
    The ability of today's technology has proved it's significance and dire need in the world yet again, with COVID-19 being a global pandemic. Various techniques are being incorporated and researches being conducted everyday in order to mitigate this pandemic. Forecasting of COVID-19 cases is one such task in machine learning which is being researched intensively to develop reliable forecasting models.In the proposed work, we have forecasted the number of COVID-19 confirmed,recovered and death cases globally using time series data with machine learning and deep learning ensemble models. The purpose of this study is to prove that ensemble of several week learners that we have developed can result in a better performing model. Deep learning models always tend to perform better than machine learning and traditional linear models due to their non-linearity. Our study concludes that deep learning ensemble model achieves better performance than the machine learning ensemble (Random forest) and the individual base learners used in ensemble model itself in COVID-19 forecasting. © 2022 IEEE.
  • No Thumbnail Available
    Item
    Sentiment Analysis on Worldwide COVID-19 Outbreak
    (Springer Science and Business Media Deutschland GmbH, 2024) Vasudev, R.; Dahikar, P.; Jain, A.; Patil, N.
    Sentiment analysis has proved to be an effective way to easily mine public opinions on issues, products, policies, etc. One of the ways this is achieved is by extracting social media content. Data extracted from the social media has proven time and again to be the most powerful source material for sentiment analysis tasks. Twitter, which is widely used by the general public to express their concerns over daily affairs, can be the strongest tool to provide data for such analysis. In this paper, we intend to use the tweets posted regarding the COVID-19 pandemic for a sentiment analysis study and sentiment classification using BERT model. Due to its transformer architecture and bidirectional approach, this deep learning model can be easily preferred as the best choice for our study. As expected, the model performed very well in all the considered classification metrics and achieved an overall accuracy of 92%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.