scholarly journals Utilization of Grid Neural Network Model and RT-PCR test to detect the COVID-19 Patients and to avoid the Spreading of SARS-CoV-2

In December 2019, a new virus, also named a novel coronavirus, started as an emerging pathogen for humans and resulted in a pandemic. World Health Organization (WHO) called this novel coronavirus as COVID-19 on 11 February 2020, and the virus responsible for causing COVID-19 is SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), which is a positive-stranded RNA virus. This paper proposed an artificial neural network model in a grid computing system to identify COVID-19 patients. It can help us to identify the suspected patients and shortlist those patients who need to check by the RT-PCR test kit. The purpose of this research is to increase the time efficiency to test those patients, which has a higher chance of getting affected by COVID-19. Increasing the time efficiency in this type of pandemic situation can make a huge impact on reducing the fatality rate. This is because, according to ICMR, 1,191,946 samples have been tested as of 5 May, and 46,433 individuals have been confirmed positive. It means that only 3.85% of persons get positive results and 96.15% persons with a negative result. It implies that the time to test this 96.15% of cases is wasted. Hence we aim to detect the COVID-19 patients in less time and utilize this large amount of time to test those at higher risk of being affected by this epidemic (COVID-19). This model will also help those countries to overcome the problem of the shortage of this type of test kits such as - RT-PCR.

Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.


2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.


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