FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3033986236> ?p ?o ?g. }

• W3033986236 abstract "Abstract According to WHO, more than one million individuals are infected with COVID-19, and around 20000 people have died because of this infectious disease around the world. In addition, COVID-19 epidemic poses serious public health threat to the world where people with little or no pre-existing human immunity can be more vulnerable to the effects of the effects of the coronavirus. Thus, developing surveillance systems for predicting COVID-19 pandemic in an early stage saves millions of lives. In this study, the deep learning algorithm and Holt-trend model is proposed to predict coronavirus. The Long-Short Term Memory (LSTM) algorithm and Holt-trend were applied to predict confirmed numbers and death cases. The real time data have been collected from the World Health Organization (WHO). In the proposed research, we have considered three countries to test the proposed model namely Saudi Arabia, Spain and Italy. The results suggest that the LSTM models showed better performance in predicting the cases of coronavirus patients. Standard measure performance MSE, RMSE, Mean error and correlation are employed to estimate the results of the proposed models. The empirical results of the LSTM by using correlation metric are 99.94%, 99.94% and 99.91 to predict number of confirmed cases on COVID-19 in three countries. Regarding the prediction results of LSTM model to predict the number of death on COVID-19 are 99.86%, 98.876% and 99.16 with respect to the Saudi Arabia, Italy and Spain respectively. Similarly the experimented results of Holt-Trend to predict the number of confirmed cases on COVID-19 by using correlation metrics are 99.06%, 99.96% and 99.94, whereas the results of Holt-Trend to predict the number of death cases are 99.80%, 99.96 and 99.94 with respect to the Saudi Arabia, Italy and Spain respectively. The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of COVID-19 in these countries. Such findings provide better insights about the future of COVID-19 in general. The results were obtained by applying the time series models which needs to be considered for the sake of saving the lives of many people." @default.
• W3033986236 created "2020-06-12" @default.
• W3033986236 creator A5027089688 @default.
• W3033986236 creator A5033065748 @default.
• W3033986236 creator A5051355986 @default.
• W3033986236 creator A5079932319 @default.
• W3033986236 creator A5085168837 @default.
• W3033986236 date "2020-06-05" @default.
• W3033986236 modified "2023-10-16" @default.
• W3033986236 title "Deep Learning and Holt-Trend Algorithms for predicting COVID-19 pandemic" @default.
• W3033986236 cites W1071368427 @default.
• W3033986236 cites W1717229843 @default.
• W3033986236 cites W1861518448 @default.
• W3033986236 cites W1930324736 @default.
• W3033986236 cites W1988706664 @default.
• W3033986236 cites W1998956117 @default.
• W3033986236 cites W2016674662 @default.
• W3033986236 cites W2017539781 @default.
• W3033986236 cites W2038752134 @default.
• W3033986236 cites W2109902257 @default.
• W3033986236 cites W2117239687 @default.
• W3033986236 cites W2118023920 @default.
• W3033986236 cites W2151485425 @default.
• W3033986236 cites W2151932005 @default.
• W3033986236 cites W2166792446 @default.
• W3033986236 cites W2171998706 @default.
• W3033986236 cites W2344349485 @default.
• W3033986236 cites W2477032014 @default.
• W3033986236 cites W2504150216 @default.
• W3033986236 cites W2531252297 @default.
• W3033986236 cites W2553292362 @default.
• W3033986236 cites W2557517619 @default.
• W3033986236 cites W2565629978 @default.
• W3033986236 cites W2581082771 @default.
• W3033986236 cites W2610490986 @default.
• W3033986236 cites W2610984530 @default.
• W3033986236 cites W2741582347 @default.
• W3033986236 cites W2749157153 @default.
• W3033986236 cites W2753292014 @default.
• W3033986236 cites W2753641467 @default.
• W3033986236 cites W2772780441 @default.
• W3033986236 cites W2774804416 @default.
• W3033986236 cites W2794803511 @default.
• W3033986236 cites W2951946244 @default.
• W3033986236 cites W2962848499 @default.
• W3033986236 cites W3008556730 @default.
• W3033986236 cites W3009359219 @default.
• W3033986236 cites W4238047442 @default.
• W3033986236 doi "https://doi.org/10.1101/2020.06.03.20121590" @default.
• W3033986236 hasPublicationYear "2020" @default.
• W3033986236 type Work @default.
• W3033986236 sameAs 3033986236 @default.
• W3033986236 citedByCount "6" @default.
• W3033986236 countsByYear W30339862362020 @default.
• W3033986236 countsByYear W30339862362021 @default.
• W3033986236 countsByYear W30339862362022 @default.
• W3033986236 crossrefType "posted-content" @default.
• W3033986236 hasAuthorship W3033986236A5027089688 @default.
• W3033986236 hasAuthorship W3033986236A5033065748 @default.
• W3033986236 hasAuthorship W3033986236A5051355986 @default.
• W3033986236 hasAuthorship W3033986236A5079932319 @default.
• W3033986236 hasAuthorship W3033986236A5085168837 @default.
• W3033986236 hasBestOaLocation W30339862361 @default.
• W3033986236 hasConcept C105795698 @default.
• W3033986236 hasConcept C11413529 @default.
• W3033986236 hasConcept C117220453 @default.
• W3033986236 hasConcept C119857082 @default.
• W3033986236 hasConcept C127413603 @default.
• W3033986236 hasConcept C139945424 @default.
• W3033986236 hasConcept C142724271 @default.
• W3033986236 hasConcept C154945302 @default.
• W3033986236 hasConcept C176217482 @default.
• W3033986236 hasConcept C21547014 @default.
• W3033986236 hasConcept C2524010 @default.
• W3033986236 hasConcept C2777648638 @default.
• W3033986236 hasConcept C2779134260 @default.
• W3033986236 hasConcept C3007834351 @default.
• W3033986236 hasConcept C3008058167 @default.
• W3033986236 hasConcept C33923547 @default.
• W3033986236 hasConcept C41008148 @default.
• W3033986236 hasConcept C524204448 @default.
• W3033986236 hasConcept C71924100 @default.
• W3033986236 hasConcept C89623803 @default.
• W3033986236 hasConceptScore W3033986236C105795698 @default.
• W3033986236 hasConceptScore W3033986236C11413529 @default.
• W3033986236 hasConceptScore W3033986236C117220453 @default.
• W3033986236 hasConceptScore W3033986236C119857082 @default.
• W3033986236 hasConceptScore W3033986236C127413603 @default.
• W3033986236 hasConceptScore W3033986236C139945424 @default.
• W3033986236 hasConceptScore W3033986236C142724271 @default.
• W3033986236 hasConceptScore W3033986236C154945302 @default.
• W3033986236 hasConceptScore W3033986236C176217482 @default.
• W3033986236 hasConceptScore W3033986236C21547014 @default.
• W3033986236 hasConceptScore W3033986236C2524010 @default.
• W3033986236 hasConceptScore W3033986236C2777648638 @default.
• W3033986236 hasConceptScore W3033986236C2779134260 @default.
• W3033986236 hasConceptScore W3033986236C3007834351 @default.
• W3033986236 hasConceptScore W3033986236C3008058167 @default.
• W3033986236 hasConceptScore W3033986236C33923547 @default.
• W3033986236 hasConceptScore W3033986236C41008148 @default.

• next