Implementation of SimpleRNN and LSTMs based prediction model for coronavirus disease (Covid-19)

Choudhary, Priyanka; Kumari, Ankita; Sood, Manu

doi:10.1088/1757-899x/1022/1/012015

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…This technique can recognize the association between death and infection rates, and is implemented in cloud technology, making it useful for governments, regulatory agencies, and healthcare systems [138]. [36,56,71,72,107,147,148] used deep learning frameworks with CT images to track the progression of the pandemic, generating a corneal score for patients in 3D volume. The primary objective of this study was to track the rise of the pandemic, and the dataset included more than 150 images from the US and China [35].…”

Section: Plos Onementioning

confidence: 99%

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Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron’s, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

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Section: Plos Onementioning

confidence: 99%

“…This software was used to predict the variants of COVID-19 and has been shown to be more accurate than the regular hybrid and deep learning models. The results of quantumbased algorithms suggest that hybrid and deep learning models are efficient at forecasting mutations [29,36,56,71,72,107,115,127,[145][146][147][148].…”

Section: Plos Onementioning

confidence: 99%

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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“…According to Priyanka et al (2021), RNN simple layer is used to model the full connection between units in the network. RNN simple layer accepts the following parameters: 1.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Comparison Methods of Machine Learning and Deep Learning to Forecast The GDP of Indonesia

Subian,

Mulkan,

Ahmady

et al. 2024

SISTEMASI

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The success of an economy can bring significant benefits to a country and its society. One way to measure economic growth is by looking at the value of gross domestic product (GDP). The value of a country's GDP is influenced by many factors, including inflation, exports, and imports. Therefore, predicting future economic growth requires forecasting the value of GDP. GDP forecasts are crucial as they provide information about the economic development of a country over a specific period of time. By forecasting GDP, governments and investors can make informed decisions to optimize profits or minimize risks when investing or doing business in a country. This research aims to forecast Indonesia's GDP for the second, third, and fourth quarters of 2023 using the best models from machine learning or deep learning methods. Forecasts are made for each method with and without additional variables. The results indicate that the SimpleRNN algorithm from deep learning without additional variables is the method with the smallest RMSE and MAPE for GDP forecasting. Therefore, the best method used to forecast GDP is the SimpleRNN algorithm, and the forecasted GDP values for Indonesia's second, third, and fourth quarters are 5.350.840,00, 5.483.895,00, and 5.610.077,50 billion rupiah.

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“…On mathematical predictions of epidemics. In the last few years, researchers and government officials have used computer-based models to try to forecast the course of the coronavirus pandemic (see, for example, [4], [13], [6], [2]). To predict the future of the coronavirus disease 2019 (COVID-19) outbreaks globally, several mathematical models have been developed.…”

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confidence: 99%

Differential neural networks prediction using slow and fast hybrid learning: application to prognosis of infections and deaths of covid-19 dynamics

Chaírez

Poznyak²,

Aniutin³

2022

Preprint

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This essay discusses a potential method for predicting the behavior of various physical processes and uses the COVID-19 outbreak to demonstrate its applicability. This study assumes that the current data set reflects the output of a dynamic system that is governed by a nonlinear ordinary differential equation. This dynamic system may be described by a Differential Neural Network (DNN) with time-varying weights matrix parameters. A new hybrid learning scheme based on the decomposition of the signal to be predicted. The decomposition considers the slow and fast components of the signal which is more natural to signals such as the ones corresponding to the number of infected and deceased patients who suffered of COVID 2019 sickness. The paper results demonstrate the recommended method offers competitive performance (70 days of COVID prediction) in comparison to similar studies.

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Implementation of SimpleRNN and LSTMs based prediction model for coronavirus disease (Covid-19)

Cited by 6 publications

References 5 publications

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Comparison Methods of Machine Learning and Deep Learning to Forecast The GDP of Indonesia

Differential neural networks prediction using slow and fast hybrid learning: application to prognosis of infections and deaths of covid-19 dynamics

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