<scp>COVID</scp>‐19 vs influenza viruses: A cockroach optimized deep neural network classification approach

El-Dosuky, M. A.; Soliman, Mona; Hassanien, Aboul Ella

doi:10.1002/ima.22562

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…Coronavirus 2019, which is highly contagious and severe, is considered a common respiratory illness after in uenza ( u). Infections with the in uenza A virus (IAV) account for approximately 75% of infections [1][2][3][4]. Adults have been infected with the IAV at a rate of 2.3%.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Optimal Selection of Features Related to Flu

Rao¹,

Kumar

Padmapriya³

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.

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Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Optimal Selection of Features Related to Flu

Rao¹,

Kumar

Padmapriya³

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…El‐dosuky et al 52 present a cockroach‐optimized DNN to detect COVID‐19 and differentiate between COVID‐19, and influenza types A, B, and C. Gupta et al 53 propose a hybrid CNN‐based model for accurate detection of COVID‐19, pneumonia, and normal patients using CXR images. Sanghvi et al 54 propose a DL method for the classification of COVID and pneumonia by DenseNet‐201.…”

Section: Other Respiratory Diseasesmentioning

confidence: 99%

Fighting against COVID‐19: Innovations and applications

Zhang

2023

Int J Imaging Syst Tech

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“…Based on our findings, in this research, designers review studies based on machine learning approaches to predict the number of cases of infectious diseases. Mohamed et al (2021) recommended a cockroach optimized deep learning model (CODNN) to classify the Covid-19 and influenza virusbased infection disease. The system aims to increase infection disease identification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Analysing biosensor clinical pathogen information using mayfly optimized convolute neural network approach

et al. 2022

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Viral and bacterial infection diseases are the most common things caused by microbes. Infection diseases are serious issues because of the growth of COVID‐19. Because of the current living situation, clinical pathogens are difficult to identify. Therefore, biosensors have been widely utilized to sense the biomolecules relevant to viruses and bacteria. The biosensors observe the nanoparticles from the pathogens and help improve the infection analysis. The sensor information is processed using machine learning techniques because it consists of several learning patterns. However, the existing methods have multi‐objective optimization problems while analysing the changes in the nanoparticles. This work utilizes a mayfly optimized convoluted neural network (MOCNN) to overcome this research issue. The grid uses the fully convolution layer that processes the extracted biosensor features to determine the infections. The network performance is optimized by applying the exploitation and exploration properties of nuptial dance that help to escape from the local optima solutions. The effective utilization of the optimized training patterns improves the convergence speed and convergence rate compared to traditional methods. From the results, MOCNN ensures 98.97% accuracy, 0.388 error rate, and 0.322833 convergence rate on various iterations with different learning rates.

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COVID‐19 vs influenza viruses: A cockroach optimized deep neural network classification approach

Cited by 12 publications

References 27 publications

Deep Neural Networks for Optimal Selection of Features Related to Flu

Deep Neural Networks for Optimal Selection of Features Related to Flu

Fighting against COVID‐19: Innovations and applications

Analysing biosensor clinical pathogen information using mayfly optimized convolute neural network approach

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