Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

Alotaibi, Saud S.; Al-Rasheed, Amal; Althahabi, Sami; Hamza, Manar Ahmed; Mohamed, Abdullah; Zamani, Abu Sarwar; Motwakel, Abdelwahed; Eldesouki, Mohamed I.

doi:10.32604/cmc.2022.029385

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…In preparation for COVID-19's eventual cross-country accessibility, machine learning (ML) and deep learning (DL) techniques can be utilized to track typical behavior using open data sources from real-time applications. These techniques can forecast the immediate future and aid in minimizing the negative impacts of COVID-19 [9][10][11]. Around the world, concerns have been raised about the COVID-19 pandemic strategy's capabilities and delivery, quick response, linked information, and evaluation [6].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

Tarek,

Shams,

Towfek

et al. 2023

Biomimetics

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The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

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Section: Introductionmentioning

confidence: 99%

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

Tarek,

Shams,

Towfek

et al. 2023

Biomimetics

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Introduction to the special issue on Data Science for COVID-19

Cao

Chacón

2022

Journal of Nonparametric Statistics

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COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine

Han

Wang

et al. 2022

International Journal of Patient-Centered Healthcare

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COVID-19 has swept the world and has had great impact on us. Rapid and accurate diagnosis of COVID-19 is essential. Analysis of chest CT images is an effective means. In this paper, an automatic diagnosis algorithm based on chest CT images is proposed. It extracts image features by stationary wavelet entropy (SWE), classifies and trains the input dataset by extreme learning machine (LEM), and finally determines the model through k-fold cross-validation (k-fold CV). By detecting 296 chest CT images of healthy individuals and COVID-19 patients, the algorithm outperforms state-of-the-art methods in sensitivity, specificity, precision, accuracy, F1, MCC, and FMI.

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Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

Cited by 3 publications

References 15 publications

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

Introduction to the special issue on Data Science for COVID-19

COVID-19 Diagnosis by Stationary Wavelet Entropy and Extreme Learning Machine

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