An Efficient COVID-19 Disease Outbreak Prediction Using BI-SSOA-TMLPNN and ARIMA
International Journal of Image and Graphics
; 2023.
Article
in English
| Scopus | ID: covidwho-2244934
ABSTRACT
Globally, people's health and wealth are affected by the outbreak of the corona virus. It is a virus, which infects from common fever to severe acute respiratory syndrome. It has the potency to transmit from one person to another. It is established that this virus spread is augmenting speedily devoid of any symptoms. Therefore, the prediction of this outbreak situation with mathematical modelling is highly significant along with necessary. To produce informed decisions along with to adopt pertinent control measures, a number of outbreak prediction methodologies for COVID-19 are being utilized by officials worldwide. An effectual COVID-19 outbreaks' prediction by employing Squirrel Search Optimization Algorithm centric Tanh Multi-Layer Perceptron Neural Network (MLPNN) (SSOA-TMLPNN) along with Auto-Regressive Integrated Moving Average (ARIMA) methodologies is proposed here. Initially, from the openly accessible sources, the input time series COVID-19 data are amassed. Then, pre-processing is performed for better classification outcomes after collecting the data. Next, by utilizing Sine-centered Empirical Mode Decomposition (S-EMD) methodology, the data decomposition is executed. Subsequently, the data are input to the Brownian motion Intense (BI) - SSOA-TMLPNN classifier. In this, the diseased, recovered, and death cases in the country are classified. After that, regarding the time-series data, the corona-virus's future outbreak is predicted by employing ARIMA. Afterwards, data visualization is conducted. Lastly, to evaluate the proposed model's efficacy, its outcomes are analogized with certain prevailing methodologies. The obtained outcomes revealed that the proposed methodology surpassed the other existing methodologies. © 2023 World Scientific Publishing Company.
Brownian movement; Data visualization; Forecasting; Machine learning; Multilayer neural networks; Network layers; Time series; Viruses; Machine-learning; Multi-layer perceptron neural network; Multilayer perceptrons neural networks (MLPs); Optimization algorithms; Outbreak prediction; Search Algorithms; Search optimization; Squirrel search algorithm; Time-series data; COVID-19; decomposition; machine learning (ML); multi-layer perceptron neural network (MLPNN); squirrel search algorithm (SSA); time series data
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
International Journal of Image and Graphics
Year:
2023
Document Type:
Article
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