A deep learning framework using CNN and stacked Bi-GRU for COVID-19 predictions in India

Ahuja, Sahil; Shelke, Nitin Arvind; Singh, Pawan Kumar

doi:10.1007/s11760-021-01988-1

Cited by 15 publications

(20 citation statements)

References 13 publications

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“…Bi-GRU is a composite of two GRUs that operate in opposing directions, with one moving forward and the other moving backward. Similarly to Bi-LSTM, the outputs of these two GRUs are merged to obtain the final result [49]. Using Bi-RNN, the model is capable of understanding the intricate connections between these parameters, which allows for more precise and reliable predictions [50].…”

Section: Bidirectional Rnn (Bi-lstm and Bi-gru)mentioning

confidence: 99%

Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

Wahyuzi,

Ahmad Luthfi,

Dhomas Hatta Fudholi

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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This study investigates the intricate relationship between electricity consumption in smart office environments, temporal elements such as time, and external factors such as weather conditions. Leveraging a dataset that includes electrical consumption statistics, temporal data, and weather conditions, the research employs preprocessing, visualization, and feature engineering techniques. The predictive model for electric energy usage is constructed using deep learning architectures, including long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), and bidirectional gated recurrent unit (Bi-GRU). Evaluation metrics reveal that the LSTM model outperforms others, achieving minimal Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The study acknowledges the limitations of the dataset, particularly in comparing electricity usage during work hours and outside work hours in a residential context. Future research aims to address these limitations, considering detailed meteorological data, missing data imputation, and real-time applications for greater applicability. The ultimate goal is to develop a predictive model that serves as a valuable tool to improve energy management in smart office settings, optimize electricity usage, and contribute to long-term company profitability.

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Section: Bidirectional Rnn (Bi-lstm and Bi-gru)mentioning

confidence: 99%

Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

Wahyuzi,

Ahmad Luthfi,

Dhomas Hatta Fudholi

2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…For example, Chen, Qi, et al (2021) applied Bi-GRU to predict wind power, and the results proved its superior prediction performance compared with LSTM and GRU. Ahuja et al (2022) used CNN and stacked Bi-GRU to predict the COVID-19 cases. The experimental result showed that the proposed model was highly reliable over the gaussian process regression model (Schulz et al, 2018).…”

Section: Recurrent Neural Network Variantsmentioning

confidence: 99%

Traffic flow prediction using bi-directional gated recurrent unit method

et al. 2022

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Traffic flow prediction plays an important role in intelligent transportation systems. To accurately capture the complex non-linear temporal characteristics of traffic flow, this paper adopts a Bi-directional Gated Recurrent Unit (Bi-GRU) model in traffic flow prediction. Compared to Gated Recurrent Unit (GRU), which can memorize information from the previous sequence, this model can memorize the traffic flow information in both previous and subsequent sequence. To demonstrate the model’s performance, a set of real case data at 1-hour intervals from 5 working days was used, wherein the dataset was separated into training and validation. To improve data quality, an augmented dickey-fuller unit root test and differential processing were performed before model training. Four benchmark models were used, including the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and GRU. The prediction results show the superior performance of Bi-GRU. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of the Bi-GRU model are 30.38, 9.88%, and 23.35, respectively. The prediction accuracy of LSTM, Bi-LSTM, GRU, and Bi-GRU, which belong to deep learning methods, is significantly higher than that of the traditional ARIMA model. The MAPE difference of Bi-GRU and GRU is 0.48% which is a small prediction error value. The results show that the prediction accuracy of the peak period is higher than that of the low peak. The Bi-GRU model has a certain lag on traffic flow prediction.

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“…Recently, deep learning models have been applied to the prediction of time series problems, such as temperature prediction [ 22 ] and stock prediction [ 23 ], which have achieved good results. Given the strong correlation between COVID-19 and time, many researchers have used deep learning models such as RNN, LSTM, BILSTM, CNN, GRU, and some hybrid models [ 24 , 25 , 26 , 27 , 28 , 29 ] to predict COVID-19 cases. For example, Xu et al [ 29 ] used CNN, LSTM, and CNN-LSTM models to predict COVID-19 cases in Brazil, India, and Russia and found that the LSTM model performed the best among the three models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 Using a WOA-BILSTM Model

Yang

2023

Bioengineering

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The COVID-19 pandemic has had a significant impact on the world, highlighting the importance of the accurate prediction of infection numbers. Given that the transmission of SARS-CoV-2 is influenced by temporal and spatial factors, numerous researchers have employed neural networks to address this issue. Accordingly, we propose a whale optimization algorithm–bidirectional long short-term memory (WOA-BILSTM) model for predicting cumulative confirmed cases. In the model, we initially input regional epidemic data, including cumulative confirmed, cured, and death cases, as well as existing cases and daily confirmed, cured, and death cases. Subsequently, we utilized the BILSTM as the base model and incorporated WOA to optimize the specific parameters. Our experiments employed epidemic data from Beijing, Guangdong, and Chongqing in China. We then compared our model with LSTM, BILSTM, GRU, CNN, CNN-LSTM, RNN-GRU, DES, ARIMA, linear, Lasso, and SVM models. The outcomes demonstrated that our model outperformed these alternatives and retained the highest accuracy in complex scenarios. In addition, we also used Bayesian and grid search algorithms to optimize the BILSTM model. The results showed that the WOA model converged fast and found the optimal solution more easily. Thus, our model can assist governments in developing more effective control measures.

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A deep learning framework using CNN and stacked Bi-GRU for COVID-19 predictions in India

Cited by 15 publications

References 13 publications

Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

Traffic flow prediction using bi-directional gated recurrent unit method

Prediction of COVID-19 Using a WOA-BILSTM Model

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