Powiadomienia systemowe
- Sesja wygasła!
Tytuł artykułu
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
We present a framework to ameliorate the classification 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.
Rocznik
Tom
Strony
273--275
Opis fizyczny
Bibliogr. 10 poz., tab., il.
Twórcy
autor
- Centre of Excellence in Disaster Mitigation & Management Indian Institute of Technology Roorkee, India
autor
- Department of Management Studies Indian Institute of Technology Roorkee, India
autor
- Science and Technology Department Hanoi University of Industry, Vietnam
Bibliografia
- 1. J. Kim and M. Hastak, “Social network analysis: Characteristics of online social networks after a disaster,” Int. J. of Info. Mgt., vol. 38, no. 1, pp. 86–96, 2018. [Online]. Available: https://doi.org/10.1016/j.ijinfomgt.2017.08.003
- 2. M. Basu, A. Shandilya, P. Khosla, K. Ghosh, and S. Ghosh, “Extracting Resource Needs and Availabilities From Microblogs for Aiding Post- Disaster Relief Operations,” IEEE Trans. on Comp. Soc. Sys., vol. 6, no. 3, pp. 1–15, 2019.
- 3. J. Zhao, Q. Yan, J. Li, M. Shao, Z. He, and B. Li, “TIMiner: Auto- matically extracting and analyzing categorized cyber threat intelligence from social data,” Comp. & Sec., vol. 95, 2020.
- 4. S. Madisetty and M. S. Desarkar, “A Neural Network-Based Ensemble Approach for Spam Detection in Twitter,” IEEE Trans. on Comp. Soc. Sys., vol. 5, no. 4, pp. 973–984, 2018.
- 5. J. Salminen, M. Hopf, S. A. Chowdhury, S. Jung, H. Almerekhi, and B. J. Jansen, “Developing an online hate classifier for multiple social media platforms,” Hum.-cent. Comp. and Info. Sci., vol. 6, pp. 1–34, 2020. [Online]. Available: https://doi.org/10.1186/s13673-019-0205-6
- 6. E. D. Andrea, P. Ducange, A. Bechini, A. Renda, and F. Marcelloni, “Monitoring the public opinion about the vaccination topic from tweets analysis,” Exp. Sys. With App., vol. 116, pp. 209–226, 2019. [Online]. Available: https://doi.org/10.1016/j.eswa.2018.09.009
- 7. Z. Wu, D. Pi, J. Chen, M. Xie, and J. Cao, “Rumor detection based on propagation graph neural network with attention mechanism,” Exp. Sys. with App., vol. 158, pp. 1–16, 2020.
- 8. A. H. Ombabi, W. Ouarda, and A. M. Alimi, “Deep learning CNN – LSTM framework for Arabic sentiment analysis using textual information shared in social networks,” Soc. Net. Anal. and Min., pp. 1–13, 2020. [Online]. Available: https://doi.org/10.1007/ s13278-020-00668-1
- 9. S. Subramani, H. U. A. Wang, H. U. Y. Q. Vu, G. Li, and S. Member, “Domestic Violence Crisis Identification From Facebook Posts Based on Deep Learning,” IEEE Acc., vol. 6, pp. 54 075–54 085, 2018.
- 10. M. M. A. Monshi, J. Poon, and V. Chung, “Deep learning in generating radiology reports: A survey,” Art.Int. In Med., vol. 106, no. April 2019, p. 101878, 2020. [Online]. Available: https://doi.org/10.1016/j.artmed.2020.101878
Uwagi
1. Preface
2. Session: International Conference on Research in Management and Technovation
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e4b3b122-9cbd-4f6b-be9b-50881546acca