Repozitorij Univerze v Novi Gorici

Izpis gradiva
A+ | A- | SLO | ENG

Naslov:Machine learning models for government to predict COVID-19 outbreak
Avtorji:Gupta, Rajan (Avtor)
Pandey, Gaurav (Avtor)
Chaudhary, Poonam (Avtor)
Pal, Saibal K. (Avtor)
Datoteke:URL https://dl.acm.org/doi/10.1145/3411761
 
Jezik:Angleški jezik
Vrsta gradiva:Neznano ()
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:UNG - Univerza v Novi Gorici
Opis:The COVID-19 pandemic has become a major threat to the whole world. Analysis of this disease requires major attention by the government in all countries to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed and trained for Indian region till 10th May, 2020, and testing has been done for the number of cases for the next three weeks. Machine learning models such as SEIR model and Regression model have been used for predictions based on the data collected from the official portal of the Government of India in the time period of 30th January, 2020, to 10th May, 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0, which is the spread of the disease, was calculated to be 2.84. Expected cases are predicted around 175K--200K in the three-week time period of test data, which is very close to the actual numbers. This study will help the government and doctors in preparing their plans for the future.
Ključne besede:COVID-19, India, spread exposed infected recovered model, regression model, machine learning, predictions, forecasting
Leto izida:2020
Št. strani:str. 1-6
Številčenje:no. 4, Vol. 1
COBISS_ID:57958147 Povezava se odpre v novem oknu
UDK:004
ISSN pri članku:2639-0175
URN:URN:SI:UNG:REP:O5X78UXT
DOI:10.1145/3411761 Povezava se odpre v novem oknu
Število ogledov:385
Število prenosov:9
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
Področja:Gradivo ni uvrščeno v področja.
:
  
Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.

Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše podrobnosti ali sproži prenos.

Gradivo je del revije

Naslov:Digital government
Skrajšan naslov:Digit. gov.
Založnik:Association for Computing Machinery
ISSN:2639-0175
COBISS.SI-ID:57957379 Novo okno

Nazaj