عنوان مقاله :
Long Short-Term Memory Approach for Coronavirus Disease Predicti
عنوان به زبان ديگر :
Long Short-Term Memory Approach for Coronavirus Disease Predicti
پديد آورندگان :
Ibrahim Obaid، Omar Department of Computer Science - College of Education - AL-Iraqia University, Baghdad, Iraq , Abed Mohammed، Mazin College of Computer Science and Information Technology - University of Anbar, Iraq , Mostafa، Salama A Faculty of Computer Science and Information Technology - Universiti Tun Hussein Onn Malaysia, Malaysia
كليدواژه :
Deep learning , Recurrent Neural Network (RNN) , LSTM , COVID-19
چكيده لاتين :
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.
عنوان نشريه :
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