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Proceedings of the 2021 International Conference on Research in Management & Technovation

Annals of Computer Science and Information Systems, Volume 28

Comparing the effectiveness of Convolutional Neural Network and Long Short-Term Memory Network for Disaster Based Social Media Messages — Using Thunderstorm and Cyclone as Case Studies

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DOI: http://dx.doi.org/10.15439/2021KM66

Citation: Proceedings of the 2021 International Conference on Research in Management & Technovation, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 28, pages 273275 ()

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Abstract. We present a framework to ameliorate the classifi- cation of disaster-related social media messages. In the present work, we have incorporated the Convolutional Neural Network, and Long Short-Term Memory Network. To demonstrate the applicability and effectiveness of the proposed approach, it is applied to the thunderstorm and cyclone Fani dataset. The results indicate that CNN is better than the LSTM model with an accuracy score of 0.9999 (99.99\%) and loss score of 0.0410. The output from the research study is helpful for disaster managers to make effective decisions on time


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