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" Long Short-Term Memory Approach for Coronavirus Disease Predicti "
Omar Ibrahim Obaid؛ Mazin Abed Mohammed؛ Salama A. Mostafa
2021/09/25
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1502371
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| Language of Document
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English
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| Main Entry
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Omar Ibrahim Obaid
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| Title & Author
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Long Short-Term Memory Approach for Coronavirus Disease Predicti [electronic resources: essay]/ Omar Ibrahim Obaid؛ Mazin Abed Mohammed؛ Salama A. Mostafa
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| Piece Level
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Journal of Information Technology Management
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| Notes Pertaining to Publication, Distribution, Etc.
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March 2020
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Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications
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| Access Link
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https://jitm.ut.ac.ir/article_79187.html
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https://jitm.ut.ac.ir/article_79187_610e84342be9afc08de825da1a72d188.pdf
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| Summary or Abstract
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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.
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| Topical Name Used as Subject
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Covid-19
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LSTM
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Prediction
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Recurrent Neural Network (RNN)
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Deep learning
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| Personal Name - Alternative Intelectual Responsibility
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Mazin Abed Mohammed
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Salama A. Mostafa
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| Originating Source
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University of Tehran. Central Library and Documentation Center
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