Outbreak prediction of COVID-19 using Recurrent neural network with Gated Recurrent Units

Natarajan, Sathish; Kumar, Mohit; Gadde, Sai Kiran Kumar; Venugopal, Vijay

doi:10.1016/j.matpr.2021.07.266

Cited by 5 publications

(4 citation statements)

References 30 publications

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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%

“…The results showed that even in the face of different intervention policies, the model still achieved good prediction accuracy. Considering that the LSTM-RNN model was not accurate enough for prediction, Natarajan et al [ 24 ] proposed an RNN-GRU model to predict infections, recoveries, and deaths in four countries (the Czech Republic, the United States, India, and Russia).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 Using a WOA-BILSTM Model

Yang

2023

Bioengineering

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“…Recent experiments have been conducted to screen for COVID-19 using acoustic features extracted from these respiratory sounds. In [ 10 ], a recurrent neural network (RNN), specifically the long short-term memory (LSTM) architecture, is used to extract six speech features from a collected dataset. They used 70% of the data for training and 30% for testing.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics

Rodríguez-Cobo,

Reyes-Gonzalez,

Algorri

et al. 2023

Sensors

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This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system’s effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.

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“…As a result, blockchain records the whole provenance of data. A blockchain digital record may be used to keep track of sample test results, patient information, discharge instructions, and immunization schedules [17].Traditionally, various methods are utilized in this application such as combined deep learning methods [18], Machine learning approach [19], Long short term memory [20], Recurrent Neural Network [21], Artificial Neural Network [22] and recent optimization methods like Particle swarm optimization [23] and Grey wolf Optimization (GWO) [24] and so on.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Based Method in Covid 19 Health System for Data Extraction with Blockchain Technology

Robin¹,

Moses

Babu

et al. 2023

IJRITCC

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Millions of people have been afflicted by the COVID-19 epidemic, which has resulted in hundreds of thousands of fatalities throughout the world. Extracting correct data on patients and facilities with and without COVID-19 with high confidence for medical specialists or the government is extremely difficult. As a result, utilizing blockchain technology, a reliable data extraction methodology for the COVID-19 database is constructed. In this accurate data extraction model development and validation study in blockchain technology for COVID analysis, here a novel Hybrid Deep Belief Lionized Optimization (HDBLO) approach is proposed. The weights of the deep model are optimized by the fitness of lion optimization. The implementation of this work is executed using MATLAB software. The simulation outcomes shows the effective performance of proposed model in blockchain technology in COVID paradigm in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), accuracy, F-measure, Processing time, precision and error. Consequently, the proposed approach is compared with the conventional strategies for significant validation.

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Outbreak prediction of COVID-19 using Recurrent neural network with Gated Recurrent Units

Cited by 5 publications

References 30 publications

Prediction of COVID-19 Using a WOA-BILSTM Model

Prediction of COVID-19 Using a WOA-BILSTM Model

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics

A Novel Hybrid Based Method in Covid 19 Health System for Data Extraction with Blockchain Technology

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