The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

Ma, Ruifang; Zheng, Xinqi; Wang, Peipei; Liu, Haiyan; Zhang, Chunxiao

doi:10.1038/s41598-021-97037-5

Cited by 55 publications

(30 citation statements)

References 42 publications

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“…In general, the tanh function is chosen as the activation function for the input and output of the memory cell. The sigmoid function is used as the activation function of the gate structure [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model

Zhao

2022

Computational Intelligence and Neuroscience

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The purpose is to automatically and quickly analyze whether the rope skipping actions conform to the standards and give correct guidance and training plans. Firstly, aiming at the problem of motion analysis, a deep learning (DL) framework is proposed to obtain the coordinates of key points in rope skipping. The framework is based on the OpenPose method and uses the lightweight MobileNetV2 instead of the Visual Geometry Group (VGG) 19. Secondly, a multi-label classification model is proposed: attention long short-term memory-long short-term memory (ALSTM-LSTM), according to the algorithm adaptive method in the multi-label learning method. Finally, the validity of the model is verified. Through the analysis and comparison of simulation results, the results show that the average accuracy of the improved OpenPose method is 77.8%, an increase of 3.3%. The proposed ALSTM-LSTM model achieves 96.1% accuracy and 96.5% precision. After the feature extraction model VGG19 in the initial stage of OpenPose is replaced by the lightweight MobileNetV2, the pose estimation accuracy is improved, and the number of model parameters is reduced. Additionally, compared with other models, the performance of the ALSTM-LSTM model is improved in all aspects. This work effectively solves the problems of real-time and accurate analysis in human pose estimation (HPE). The simulation results show that the proposed DL model can effectively improve students’ high school entrance examination performance.

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Section: Methodsmentioning

confidence: 99%

Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model

Zhao

2022

Computational Intelligence and Neuroscience

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“…Stationarity analysis was performed using Augmented Dickey Fuller test (ADF). The ADF is a standardized unit root test for determining the effect of trends on data, and its results are analyzed by the probability of the test [35]. The following hypotheses were assumed:…”

Section: Resultsmentioning

confidence: 99%

“…LSTMs are a special type of RNN capable of learning long-term relationships. They were introduced by Hochreiter and Schmidhuber in 1997 [34], and then refined and popularized in subsequent works [35][36][37][38]. They work extremely well with long-term dependency problems.…”

Section: Literature Review Of Forecasting Natural Gas Consumptionmentioning

confidence: 99%

Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model

et al. 2021

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Natural gas is one of the main energy sources in Poland and accounts for about 15% of the primary energy consumed in the country. Poland covers only 1/5 of its demand from domestic deposits. The rest is imported from Russia, Germany, Norway, the Czech Republic, Ukraine, and Central Asia. An important issue concerning the market of energy resources is the question of the direct impact of the prices of energy resources on the income of exporting and importing countries. It should also be remembered that unexpected and large fluctuations are detrimental to both exporters and importers of primary fuels. The article analyzes natural gas deposits in Poland, raw material trade and proposes a model for forecasting the volume of its consumption, which takes into account historical consumption, prices of energy resources and assumptions of Poland’s energy policy until 2040. A hybrid model was built by combining ARIMA with LSTM artificial neural networks. The validity of the constructed model was assessed using ME, MAE, RMSE and MSE. The average percentage error is 2%, which means that the model largely represents the real gas consumption course. The obtained forecasts indicate an increase in consumption by 2040.

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“…It selectively allows information to pass through Gate structure, so as to update or retain historical information. The LSTM model can be expressed as [ 42 , 43 ]: Here, f t , i t , and o t represent the Forget Gate, Input Gate and Output Gate, respectively. Besides, σ represents sigmoid function, C t represents the cell state update value at time t, represents the candidate state value of the input cell at time t. W f , W i , W C represent the weight of Forget Gate, Input Gate and Output Gate.…”

Section: Methodsmentioning

confidence: 99%

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China

Zhao¹,

Zhang²,

Cao

et al. 2022

PLoS ONE

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Background and objective Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population’s health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China. Methods The data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People’s Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy. Results There were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models. Conclusions Our findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.

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The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method

Cited by 55 publications

References 42 publications

Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model

Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model

Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China

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