Long Short-Term Memory Approach for Coronavirus Disease Predicti
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 pape...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
University of Tehran
2020-12-01
|
| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_79187_610e84342be9afc08de825da1a72d188.pdf |
| _version_ | 1818402506925932544 |
|---|---|
| author | Omar Ibrahim Obaid Mazin Mohammed Salama A. Mostafa |
| author_facet | Omar Ibrahim Obaid Mazin Mohammed Salama A. Mostafa |
| author_sort | Omar Ibrahim Obaid |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T08:09:27Z |
| format | Article |
| id | doaj.art-7a73f768fa284c6b9d25d0175e6203c3 |
| institution | Directory Open Access Journal |
| issn | 2008-5893 2423-5059 |
| language | fas |
| last_indexed | 2024-12-14T08:09:27Z |
| publishDate | 2020-12-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj.art-7a73f768fa284c6b9d25d0175e6203c32022-12-21T23:10:06ZfasUniversity of TehranJournal of Information Technology Management2008-58932423-50592020-12-0112Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications.112110.22059/jitm.2020.7918779187Long Short-Term Memory Approach for Coronavirus Disease PredictiOmar Ibrahim Obaid0Mazin Mohammed1Salama A. Mostafa2Department of Computer Science, College of Education, AL-Iraqia University, Baghdad, Iraq.Ph.D., College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Iraq.Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, 86400, Malaysia.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.https://jitm.ut.ac.ir/article_79187_610e84342be9afc08de825da1a72d188.pdfdeep learninglstmpredictioncovid-19recurrent neural network (rnn) |
| spellingShingle | Omar Ibrahim Obaid Mazin Mohammed Salama A. Mostafa Long Short-Term Memory Approach for Coronavirus Disease Predicti Journal of Information Technology Management deep learning lstm prediction covid-19 recurrent neural network (rnn) |
| title | Long Short-Term Memory Approach for Coronavirus Disease Predicti |
| title_full | Long Short-Term Memory Approach for Coronavirus Disease Predicti |
| title_fullStr | Long Short-Term Memory Approach for Coronavirus Disease Predicti |
| title_full_unstemmed | Long Short-Term Memory Approach for Coronavirus Disease Predicti |
| title_short | Long Short-Term Memory Approach for Coronavirus Disease Predicti |
| title_sort | long short term memory approach for coronavirus disease predicti |
| topic | deep learning lstm prediction covid-19 recurrent neural network (rnn) |
| url | https://jitm.ut.ac.ir/article_79187_610e84342be9afc08de825da1a72d188.pdf |
| work_keys_str_mv | AT omaribrahimobaid longshorttermmemoryapproachforcoronavirusdiseasepredicti AT mazinmohammed longshorttermmemoryapproachforcoronavirusdiseasepredicti AT salamaamostafa longshorttermmemoryapproachforcoronavirusdiseasepredicti |