Long Short-Term Memory Approach for Coronavirus Disease Predicti

Document Type : Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications

Authors

1 Department of Computer Science, College of Education, AL-Iraqia University, Baghdad, Iraq.

2 Ph.D., College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Iraq.

3 Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, 86400, Malaysia.

Abstract

Corona Virus (COVID-19) is a major problem among people, and it causes suffering worldwide. Yet, the traditional prediction models are not yet suitably efficient in catching the fundamental expertise as they cannot visualize the difficulty in the health's representation problem areas. This paper states prediction mechanism that uses a model of deep learning called Long Short-Term Memory (LSTM). We have carried this model out on corona virus dataset that obtained from the records of infections, deaths, and recovery cases across the world. Furthermore, producing a dataset which includes features of geographic regions (temperature and humidity) that have experienced severe virus outbreaks, risk factors, spatio-temporal analysis, and social behavior of people, a predictive model can be developed for areas where the virus is likely to spread. However, the outcomes of this study are justifiable to alert the authorities and the people to take precautions.

Keywords

References

Abd Ghani, M. K., Mohammed, M. A., Arunkumar, N., Mostafa, S. A., Ibrahim, D. A., Abdullah, M. K., Jaber, M. M., Abdulhay, E., Ramirez-Gonzalez, G., & Burhanuddin, M. A. (2020). Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Computing and Applications, 32(3), 625–638. https://doi.org/10.1007/s00521-018-3882-6
Abed Mohammed, M., Khanapi Abd Ghani, M., Mostafa, S., Taha Al-Dhief, F., Ibrahim Obaid, O., Mostafa, S. A., & Taha AL-Dhief, F. (2018). Evaluating the Performance of Machine Learning Techniques in the Classification of Wisconsin Breast Cancer Performance evaluation of Wisconsin Breast Cancer. Article in International Journal of Engineering and Technology, 7(4), 160–166. https://doi.org/10.14419/ijet.v7i4.36.23737
Al-Dhief, F. T., Latiff, N. M. azzah A., Malik, N. N. N. A., Salim, N. S., Baki, M. M., Albadr, M. A. A., & Mohammed, M. A. (2020). A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms. IEEE Access, 8, 64514–64533. https://doi.org/10.1109/ACCESS.2020.2984925
Carneiro, T., Da Nobrega, R. V. M., Nepomuceno, T., Bian, G. Bin, De Albuquerque, V. H. C., & Filho, P. P. R. (2018). Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications. IEEE Access, 6, 61677–61685. https://doi.org/10.1109/ACCESS.2018.2874767
Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I. and Mrzljak, V., 2020. Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron. Computational and Mathematical Methods in Medicine, 2020.
CHEN, M., HAO, Y., HWANG, K., WANG, L., & WANG, L. (2019). Disease Prediction by Machine Learning from Healthcare Communities. International Journal of Scientific Research in Science and Technology, 29–35. https://doi.org/10.32628/ijsrst19633
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015). Long-term recurrent convolutional networks for visual recognition and description. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 2625–2634. https://doi.org/10.1109/CVPR.2015.7298878
E.V., S., Konradi, A.O., Arutyunov, G. P., Arutyunov, A. G., Bautin, A. E., Boytsov, S. A., Villevalde, S.V., Grigoryeva, N. Y., Duplyakov, D. V., Zvartau, N. E., & Koziolova, N. A. (2020). Guidelines for the diagnosis and treatment of circulatory diseases in the context of the COVID-19 pandemic. Russian Journal of Cardiology, 25(3), 3801.
Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5–6), 602–610. https://doi.org/10.1016/j.neunet.2005.06.042
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 1735–1780.
Mohammed, M. A., Abdulkareem, K. H., Al-Waisy, A. S., Mostafa, S. A., Al-Fahdawi, S., Dinar, A. M., Alhakami, W., Baz, A., Al-Mhiqani, M. N., Alhakami, H., Arbaiy, N., Maashi, M. S., Mutlag, A. A., Garcia-Zapirain, B., & De La Torre Diez, I. (2020). Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods. IEEE Access, 8, 99115–99131. https://doi.org/10.1109/ACCESS.2020.2995597
Mohammed, M. A., Abdulkareem, K. H., Mostafa, S. A., Ghani, M. K. A., Maashi, M. S., Garcia-Zapirain, B., Oleagordia, I., Alhakami, H., & Al-Dhief, F. T. (2020). Voice pathology detection and classification using convolutional neural network model. Applied Sciences (Switzerland), 10(11), 1–13. https://doi.org/10.3390/app10113723
Muhammad, L.J., Islam, M.M., Sharif, U.S. and Ayon, S.I., 2020. Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients Recovery.
M. Rubaiyat Hossain Mondal, Subrato Bharati, Prajoy Podder, Priya Podder, Data analytics for novel coronavirus disease, Informatics in Medicine Unlocked,Vol 20,100374, 2020.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Mathieu, B., Peter, P., Ron, W., & Vincent, D. (2011). Scikit-learn. GetMobile: Mobile Computing and Communications, 19(1), 29–33. https://doi.org/10.1145/2786984.2786995
Rassem, A., El-Beltagy, M., & Saleh, M. (2017). Cross-Country Skiing Gears Classification using Deep Learning. 1–14. http://arxiv.org/abs/1706.08924
Raw.githubusercontent.com. 2020. [online] Available at:    https://raw.githubusercontent.com/datasets/ covid-19/ master/time-series-19-covid-combined.csv [Accessed 22 March 2020].
Shanmugamani, R. (2018). Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

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