Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases.
TLDR
In this paper, a Nonlinear Autoregressive Neural Network Time Series (NAR-NNTS) model is proposed for predicting confirmed, recovered and death cases of COVID-19 outbreak.Abstract:
The recent COVID-19 outbreak has severely affected people around the world. There is a need of an efficient decision making tool to improve awareness about the spread of COVID-19 infections among the common public. An accurate and reliable neural network based tool for predicting confirmed, recovered and death cases of COVID-19 can be very helpful to the health consultants for taking appropriate actions to control the outbreak. This paper proposes a novel Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model for forecasting COVID-19 cases. This NAR-NNTS model is trained with Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR) training algorithms. The performance of the proposed model has been compared by using Root Mean Square Error (RMSE), Mean Square Error (MSE) and correlation co-efficient i.e. R-value. The results show that NAR-NNTS model trained with LM training algorithm performs better than other models for COVID-19 epidemiological data prediction.read more
Citations
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Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook
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References
More filters
Journal ArticleDOI
COVID-19 and Italy: what next?
Andrea Remuzzi,Giuseppe Remuzzi +1 more
TL;DR: Analysis of the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Italy might help political leaders and health authorities to allocate enough resources, including personnel, beds, and intensive care facilities, to manage the situation in the next few days and weeks.
Journal ArticleDOI
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks.
TL;DR: The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively.
Journal ArticleDOI
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
TL;DR: In this article, a dataset of X-ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus.
Journal ArticleDOI
Application of the ARIMA model on the COVID-2019 epidemic dataset
TL;DR: A simple econometric model that could be useful to predict the spread of COVID-2019 is proposed that was performed on the Johns Hopkins epidemiological data and performed Auto Regressive Integrated Moving Average model prediction.
Journal ArticleDOI
Achieving Secure Role-Based Access Control on Encrypted Data in Cloud Storage
TL;DR: This paper proposes a role-based encryption (RBE) scheme that integrates the cryptographic techniques with RBAC, and presents a secure RBE-based hybrid cloud storage architecture that allows an organization to store data securely in a public cloud, while maintaining the sensitive information related to the organization's structure in a private cloud.