A COVID-19 forecasting system using adaptive neuro-fuzzy inference

Ly, Kim Tien

doi:10.1016/j.frl.2020.101844

Cited by 31 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to prove the advancement and superiority of the proposed TCN-GRU-DBN-Q-SVM algorithm, this paper compares it with two classic models (LSTM [ 73 ] and ANFIS [ 74 ]), three state-of-the-art models (VMD-BP [ 75 ], N-Beats [ 76 ], and WT-RVFL [ 77 ]), and time series analysis methods (ARIMA). In addition, in order to prove that residual prediction can effectively improve the model's comprehensive prediction and data analysis accuracy, the proposed TCN-GRU-DBN-Q-SVM was compared with TCN-GRU-DBN-Q.…”

Section: Resultsmentioning

confidence: 99%

“…From the results, it can be found that for the number of infections in the UK, India, and the US, model 1 (TCN-GRU-DBN-Q-SVM) has a higher prediction accuracy than model 2 (TCN-GRU-DBN-Q) (MRSE 1 ＜MRSE 2 , MAE 1 ＜MAE 2 , MAPE% 1 ＜MAPE% 2 , ||PCC1|-1|＜||PCC2|-1|), which proves that the establishment of an error prediction model is meaningful for improving the prediction accuracy. Furthermore, in order to verify the high forecast accuracy of the proposed hybrid model quantitatively, we propose the prediction performance indices improvement percentages P MAPE% (%), P MAE (%), P RMSE (%) and P pcc (%) to compare and analyze the improvement of the prediction accuracy of the proposed model (TCN-GRU-DBN-Q-SVM) compared with the TCN-GRU-DBN-Q (proposed model without SVM error predictor), LSTM [ 73 ], ANFIS [ 74 ], VMD-BP [ 75 ], N-Beats [ 76 ], WT-RVFL [ 77 ] and ARIMA models. The specific calculation method is as follows:

where MAPE% 1 , MAE 1 , RMSE 1 and PCC 1 are forecasting performance indices of the proposed model, while MAPE% 2 , MAE 2 , RMSE 2 and PCC 2 are the indices of the comparison model.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

Jin¹,

Dong²,

Yu³

et al. 2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

where MAPE% 1 , MAE 1 , RMSE 1 and PCC 1 are forecasting performance indices of the proposed model, while MAPE% 2 , MAE 2 , RMSE 2 and PCC 2 are the indices of the comparison model.…”

Section: Resultsmentioning

confidence: 99%

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

Jin¹,

Dong²,

Yu³

et al. 2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…These models are NARANN, ANFIS, HFFA, LSTM, BNN, VAE and SSA. ANFIS has been applied to predict the future number of COVID-19 cases in the United Kingdom [ 56 ] and Malaysia [ 57 ]. In the first study, a parametric comparison has been carried out to obtain the optimal ANFIS model based on model accuracy considering different model parameters.…”

Section: Comparative Study and Discussionmentioning

confidence: 99%

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

Elsheikh

Saba

Panchal³

et al. 2021

Healthcare

View full text Add to dashboard Cite

Since the discovery of COVID-19 at the end of 2019, a significant surge in forecasting publications has been recorded. Both statistical and artificial intelligence (AI) approaches have been reported; however, the AI approaches showed a better accuracy compared with the statistical approaches. This study presents a review on the applications of different AI approaches used in forecasting the spread of this pandemic. The fundamentals of the commonly used AI approaches in this context are briefly explained. Evaluation of the forecasting accuracy using different statistical measures is introduced. This review may assist researchers, experts and policy makers involved in managing the COVID-19 pandemic to develop more accurate forecasting models and enhanced strategies to control the spread of this pandemic. Additionally, this review study is highly significant as it provides more important information of AI applications in forecasting the prevalence of this pandemic.

show abstract

“…The model was also statistically efficient because to the adoption of a Robust Weibull model based on iterative weighting. Kim Tien Ly [23] predicted the number of COVID-19 cases in the United Kingdom using Adaptive Neuro-Fuzzy Inference System (ANFIS) model to train the data collected. He worked on various factors of ANFIS to build a successful time-series prediction model.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Techniques for Predicting the Outbreak of Covid19 Cases

Santra¹,

Dutta²

2022

IJISA

View full text Add to dashboard Cite

At present, the whole world is experiencing a huge disturbance in social, economic, and political levels which may mostly attributed to sudden outbreak of Covid-19. The World Health Organization (WHO) declared it as Public Health crisis and global pandemic. Researchers across the globe have already proposed different outbreak models to impose various control measures fight against the novel corona virus. In order to overcome various challenges for the prediction of Covid-19 outbreaks, different mathematical and statistical approaches have been recommended by the researchers. The approaches used machine learning and deep learning based techniques which are capable of prediction of hidden patterns from large and complex datasets. The purpose of the present paper is to study different machine learning and deep learning based techniques used to identify and predict the pattern and performs some comparative analysis on the techniques. This paper contains a detailed summary of 40 paper based on this issue along with the use of method they applied to obtain the purpose. After the review it has been found that no model is fully capable of predicting it with accuracy. So, a hybrid model with better training should be employed for better result. This paper also studies different performance measures that researchers have used to show the efficiency of their proposed model.

show abstract

A COVID-19 forecasting system using adaptive neuro-fuzzy inference

Cited by 31 publications

References 14 publications

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

A Comprehensive Review of Machine Learning Techniques for Predicting the Outbreak of Covid19 Cases

Contact Info

Product

Resources

About