A Comparison: Prediction of Death and Infected COVID-19 Cases in Indonesia Using Time Series Smoothing and LSTM Neural Network

Procedia Computer Science, Год журнала: 2021, Номер 179, С. 982 - 988

Опубликована: Янв. 1, 2021

COVID-19 is a virus causing pneumonia, also known as Corona Virus Disease. The first outbreak was found in Wuhan, China, the province of Hubei on December 2019. objective this paper to predict death and infected Indonesia using Savitzky Golay Smoothing Long Short Term Memory Neural Network model (LSTM-NN). dataset obtained from Humanitarian Data Exchange (HDX), containing daily information due COVID-19. In Indonesia, total data collected ranges 2 March 2020 by 26 July 2020, with 147 records. results these two models are compared determine best fitted model. curve LSTM-NN shows an increase cases Time Series increases, however smoothing tendency decrease. conclusion, prediction produce better result than Smoothing. distinct rise align actual data.

Язык: Английский

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Intelligent system for COVID-19 prognosis: a state-of-the-art survey

Applied Intelligence, Год журнала: 2021, Номер 51(5), С. 2908 - 2938

Опубликована: Янв. 6, 2021

This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 (COVID-19) has been treated as a Public Health crisis global Concern by World Organization (WHO). Various models COVID-19 are being utilized researchers throughout world to get well-versed decisions impose significant control measures. Amid standard methods worldwide epidemic prediction, easy statistical, well epidemiological have got more consideration authorities. One main difficulty controlling spreading inadequacy lack medical tests detecting identifying solution. To solve this problem, few statistical-based advances enhanced turn into partial resolution up-to some level. deal with challenges field, broad range intelligent based methods, frameworks, equipment recommended Machine Learning (ML) Deep Learning. As ML DL ability predicting patterns complex large datasets, they recognized suitable procedure producing effective solutions diagnosis COVID-19. In paper, perspective research conducted applicability systems such ML, others solving related issues. intention behind study (i) understand importance approaches pandemic, (ii) discussing efficiency impact these prognosis COVID-19, (iii) growth development type advanced prognosis,(iv) analyzing data types nature along processing COVID-19,(v) focus on future inspire innovating enhancing their knowledge other impacted sectors due

Язык: Английский

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Covid-19 detection via deep neural network and occlusion sensitivity maps

Alexandria Engineering Journal, Год журнала: 2021, Номер 60(5), С. 4829 - 4855

Опубликована: Март 30, 2021

Deep learning approaches have attracted a lot of attention in the automatic detection Covid-19 and transfer is most common approach. However, majority pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning images often grayscale. To address this issue, we propose deep architecture called CovidNet requires relatively smaller number parameters. accepts grayscale as inputs suitable for training with limited dataset. Experimental results show that outperforms other state-of-the-art detection.

Язык: Английский

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Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

International Journal of Environmental Research and Public Health, Год журнала: 2021, Номер 18(8), С. 4287 - 4287

Опубликована: Апрель 18, 2021

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in daily lives billions people worldwide. Therefore, many efforts have been made by researchers across globe attempt determining models spread. The objectives this review are to analyze some open-access datasets mostly used research field regression modeling as well present current literature based on Artificial Intelligence (AI) methods for tasks, like disease Moreover, we discuss applicability Machine Learning (ML) and Evolutionary Computing (EC) that focused regressing epidemiology curves COVID-19, provide an overview usefulness existing specific areas. An electronic search various databases was conducted develop comprehensive latest AI-based approaches spread COVID-19. Finally, conclusion drawn from observation reviewed papers algorithms clear application epidemiological may be crucial tool combat against coming pandemics.

Язык: Английский

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Robust modelling and prediction of the COVID-19 pandemic in Canada

International Journal of Production Research, Год журнала: 2021, Номер 61(24), С. 8367 - 8383

Опубликована: Июнь 14, 2021

Since the beginning of COVID-19, more than 13,036,550 people have been infected, and 571,574 died because disease by July 13, 2020. Developing new methodologies to predict COVID-19 pandemic will help policymakers plan contain spread virus. In this research, we develop a Stochastic Fractal Search algorithm combined with mathematical model forecast pandemic. To enhance algorithm, employed design experiments approach for tuning. We applied our public datasets in Canada upcoming months. Our predicts number symptomatic, asymptomatic, life-threatening, recovered, death cases. The outcomes reveal that asymptomatic cases play main role transmission also show increasing testing capacity would detection limit community transmission. Moreover, performed sensitivity analyses discover effects changes rates on growth. provide realistic overview future if change due emergence variants or social measures. Considering outcomes, several managerial insights minimize

Язык: Английский

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