Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization

Abbasimehr, Hossein; Paki, Reza

doi:10.1016/j.chaos.2020.110511

Cited by 141 publications

(97 citation statements)

References 21 publications

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“…On the other hand, to optimize the model's performance and reduce computational time, the hyperparameter screening process must be optimized-this is the main purpose of using BO. e Bayesian optimization framework utilizes historical data to optimize the search domain and constantly predict the posterior piece of information [65]. In particular, suppose that we have a functional relation between the hyperparameters and loss function:…”

Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

“…In the proposed model, the hyperparameters are maximum tree depth (D), number of nodes in each tree (c), number of trees (K), learning rate (η), regularization parameter (λ), and number of samples (N), as introduced in equations ( 1)-( 4). e loss function is defined by the RMSE as [65] loss p j �…”

Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

“…where p j is the j th hyperparameter combination, y is the true value, and y i (p j ) is the model output results obtained using the j th hyperparameter combination p j . e next step of BO is to construct the dataset D � (x i , y i ) , where x i is the i th set of hyperparameters and y i is the corresponding error of the model output [65]:…”

Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

“…e posterior probability P(y|x, D) is denoted as D. A Gaussian distribution is applied to the alternative model M whose mean and variance are denoted as µ and K, respectively. e specific functional expression M is obtained by fitting the dataset D [65]:…”

Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

“…Based on M, the next observation is calculated using the acquisition function α(p) [65]: p * � arg max α(P, p(y|x)).…”

Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

See 4 more Smart Citations

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Nguyen

Truong

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the prediction of surface roughness in manufacturing polycarbonate (PC) by applying Bayesian optimization for machine learning models. The input variables of ultraprecision turning—namely, feed rate, depth of cut, spindle speed, and vibration of the X-, Y-, and Z-axis—are the main factors affecting surface quality. In this research, six machine learning- (ML-) based models—artificial neural network (ANN), Cat Boost Regression (CAT), Support Vector Machine (SVR), Gradient Boosting Regression (GBR), Decision Tree Regression (DTR), and Extreme Gradient Boosting Regression (XGB)—were applied to predict the surface roughness (Ra). The predictive performance of the baseline models was quantitatively assessed through error metrics: root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The overall results indicate that the XGB and CAT models predict Ra with the greatest accuracy. In improving baseline models such as XGB and CAT, the Bayesian optimization (BO) is next used to determine their best hyperparameters, and the results indicate that XGB is the best model according to the evaluation metrics. Results have shown that the performance of the models has been improved significantly with BO. For example, the values of RMSE and MAE of XGB have decreased from 0.0076 to 0.0047 and from 0.0063 to 0.0027, respectively, for the training dataset. Using the testing dataset, the values of RMSE and MAE of XGB have decreased from 0.4033 to 0.2512 and from 0.2845 to 0.2225, respectively. Moreover, the vibrations of the X, Y, and Z axes and feed rate are the most significant feature in predicting the results, which is in high accordance with the literature. We find that, in a specified value domain, the vibration of the axes has a greater influence on the surface quality than does the cutting condition.

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Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

“…Based on M, the next observation is calculated using the acquisition function α(p) [65]: p * � arg max α(P, p(y|x)).…”

Section: Bayesian Algorithm Optimizationmentioning

confidence: 99%

See 3 more Smart Citations

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Nguyen

Truong

et al. 2021

Mathematical Problems in Engineering

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State‐of‐health estimation and remaining useful life for lithium‐ion battery based on deep learning with Bayesian hyperparameter optimization

Kong

Wang

Ping

2021

Intl J of Energy Research

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Improving the performance of deep learning models using statistical features: The case study of COVID‐19 forecasting

Abbasimehr

Paki

Bahrini

2021

Math Methods in App Sciences

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COVID‐19 pandemic has affected all aspects of people's lives and disrupted the economy. Forecasting the number of cases infected with this virus can help authorities make accurate decisions on the interventions that must be implemented to control the pandemic. Investigation of the studies on COVID‐19 forecasting indicates that various techniques such as statistical, mathematical, and machine and deep learning have been utilized. Although deep learning models have shown promising results in this context, their performance can be improved using auxiliary features. Therefore, in this study, we propose two hybrid deep learning methods that utilize the statistical features as auxiliary inputs and associate them with their main input. Specifically, we design a hybrid method of the multihead attention mechanism and the statistical features (ATT_FE) and a combined method of convolutional neural network and the statistical features (CNN_FE) and apply them to COVID‐19 data of 10 countries with the highest number of confirmed cases. The results of experiments indicate that the hybrid models outperform their conventional counterparts in terms of performance measures. The experiments also demonstrate the superiority of the hybrid ATT_FE method over the long short‐term memory model.

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Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization

Cited by 141 publications

References 21 publications

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

State‐of‐health estimation and remaining useful life for lithium‐ion battery based on deep learning with Bayesian hyperparameter optimization

Improving the performance of deep learning models using statistical features: The case study of COVID‐19 forecasting

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