@Article{cmc.2020.011155,
AUTHOR = {Muhammad Adnan Khan, Sagheer Abbas, Khalid Masood Khan, Mohammad A. Al Ghamdi, Abdur Rehman},
TITLE = {Intelligent Forecasting Model of COVID-19 Novel Coronavirus  Outbreak Empowered with Deep Extreme Learning Machine},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1329--1342},
URL = {http://www.techscience.com/cmc/v64n3/39432},
ISSN = {1546-2226},
ABSTRACT = {An epidemic is a quick and widespread disease that threatens many lives and 
damages the economy. The epidemic lifetime should be accurate so that timely and 
remedial steps are determined. These include the closing of borders schools, suspension 
of community and commuting services. The forecast of an outbreak effectively is a very 
necessary but difficult task. A predictive model that provides the best possible forecast is 
a great challenge for machine learning with only a few samples of training available. This 
work proposes and examines a prediction model based on a deep extreme learning 
machine (DELM). This methodology is used to carry out an experiment based on the 
recent Wuhan coronavirus outbreak. An optimized prediction model that has been 
developed, namely DELM, is demonstrated to be able to make a prediction that is fairly 
best. The results show that the new methodology is useful in developing an appropriate 
forecast when the samples are far from abundant during the critical period of the disease.
During the investigation, it is shown that the proposed approach has the highest accuracy 
rate of 97.59% with 70% of training, 30% of test and validation. Simulation results 
validate the prediction effectiveness of the proposed scheme.},
DOI = {10.32604/cmc.2020.011155}
}