Prediction of COVID-19 Using Recurrent Neural Network Model

Alamsyah, Alamsyah; Prasetiyo, Budi; Hakim, M. Faris Al; Pradana, Fadli Dony

doi:10.15294/sji.v8i1.30070

Cited by 6 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results received from RNNCON-Res are compared with other methods, such as Recurrent Neural Model [30] and Stacked-LSTM [31], as discussed in Table 2. Stacked-LSTM is advanced by using LSTM units instead of simple recurrent units used in the Recurrent Neural Model for better results.…”

Section: Discussion Of Performancementioning

confidence: 99%

Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network

et al. 2023

View full text Add to dashboard Cite

The outbreak of COVID-19 has engulfed the entire world since the end of 2019, causing tremendous loss of lives. It has also taken a toll on the healthcare sector due to the inability to accurately predict the spread of disease as the arrangements for the essential supply of medical items largely depend on prior predictions. The objective of the study is to train a reliable model for predicting the spread of Coronavirus. The prediction capabilities of various powerful models such as the Autoregression Model (AR), Global Autoregression (GAR), Stacked-LSTM (Long Short-Term Memory), ARIMA (Autoregressive Integrated Moving Average), Facebook Prophet (FBProphet), and Residual Recurrent Neural Network (Res-RNN) were taken into consideration for predicting COVID-19 using the historical data of daily confirmed cases along with Twitter data. The COVID-19 prediction results attained from these models were not up to the mark. To enhance the prediction results, a novel model is proposed that utilizes the power of Res-RNN with some modifications. Gated Recurrent Unit (GRU) and LSTM units are also introduced in the model to handle the long-term dependencies. Neural Networks being data-hungry, a merged layer was added before the linear layer to combine tweet volume as additional features to reach data augmentation. The residual links are used to handle the overfitting problem. The proposed model RNN Convolutional Residual Network (RNNCON-Res) showcases dominating capability in country-level prediction 20 days ahead with respect to existing State-Of-The-Art (SOTA) methods. Sufficient experimentation was performed to analyze the prediction capability of different models. It was found that the proposed model RNNCON-Res has achieved 91% accuracy, which is better than all other existing models.

show abstract

Section: Discussion Of Performancementioning

confidence: 99%

Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network

et al. 2023

View full text Add to dashboard Cite

show abstract

“…While the Elman architecture, the network has feedback that comes from the output on the hidden layer to the input on the hidden layer. From the application of the two RNN architectures above, the Elman architecture is best used in forecasting foreign exchange [13]- [15].…”

Section: Recurrent Neural Network Architecturementioning

confidence: 99%

“…where 𝜀 𝑗 (𝑛) is hidden neuron error (i th ) at the time (n); and 𝛿 𝑗 (𝑛) is delta input neuron hidden (j th ) at the time (n). − Calculation of the weight correction at the time (n) shown in ( 14), (15), and ( 16):…”

Section: Backpropagation Through Timementioning

confidence: 99%

Implementation of recurrent neural network for the forecasting of USD buy rate against IDR

Moonlight

Trilaksono

Harianto

et al. 2023

IJECE

View full text Add to dashboard Cite

This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the most appropriate activation function for the hidden layer, while the linear transfer function is the most appropriate activation function for the output layer. Based on the results of training and forecasting the USD against IDR currency, the Elman BPTT method is better than the Jordan BPTT method, with the best iteration being the 4000th iteration for both. The lowest root mean square error (RMSE) values for training and forecasting produced by Elman BPTT were 0.073477 and 122.15 the following day, while the Jordan backpropagation RNN method yielded 0.130317 and 222.96 also the following day.

show abstract

“…In March 2020, World Health Organization (WHO) declared the coronavirus or COVID-19 as a pandemic outbreak [1]- [3]. In December 2019, the first start of this coronavirus was found in the Wuhan area of Hubei Province, China.…”

Section: Introductionmentioning

confidence: 99%

Combination of gray level co-occurrence matrix and artificial neural networks for classification of COVID-19 based on chest X-ray images

Imran,

Delsi Samsumar,

Subki

et al. 2024

IJ-AI

View full text Add to dashboard Cite

This research uses the gray level co-occurrence matrix (GLCM) and artificial neural networks to classify COVID-19 images based on chest X-ray images. According to previous studies, there has never been a researcher who has integrated GLCM with artificial neural networks. Epochs 10, 30, 50, 70, 100, and 120 were used in this research. The total number of data points used in this investigation was 600, divided into 300 normal chests and 300 COVID-19 data points. Epoch 10 had 91% accuracy, epoch 30 had 91% accuracy, epoch 50 had 92% accuracy, epoch 70 had 91% accuracy, epoch 100 had 92% accuracy, and epoch 120 had 90% accuracy in categorization. As indicated by the results of the classification tests, combining GLCM and artificial neural networks can produce good results; a combination of these methods can yield a classification for COVID-19.

show abstract

Prediction of COVID-19 Using Recurrent Neural Network Model

Cited by 6 publications

References 14 publications

Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network

Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network

Implementation of recurrent neural network for the forecasting of USD buy rate against IDR

Combination of gray level co-occurrence matrix and artificial neural networks for classification of COVID-19 based on chest X-ray images

Contact Info

Product

Resources

About