Intelligent Forecasting Model of COVID-19 Novel Coronavirus Outbreak Empowered with Deep Extreme Learning Machine
作者机构：Department of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer ScienceNational College of Business Administration&EconomicsLahore54000Pakistan Department of Computer ScienceUmm Al-Qura UniversityMakkah23500Saudi Arabia
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2020年第64卷第9期
页 面：1329-1342页
核心收录：
学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 1001[医学-基础医学(可授医学、理学学位)] 0809[工学-电子科学与技术（可授工学、理学学位）] 100103[医学-病原生物学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 10[医学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Coronavirus nCoV DELM Mis rate SERS-CoV WHO COVID-19
摘 要：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.
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