Nanobioengineering: A promising approach for early detection of COVID-19

Gill, Atal A.S.; Nate, Zondi; Chauhan, Ruchika; Faya, Mbuso; Karpoormath, Rajshekhar; Omolo, Calvin A.

doi:10.1016/b978-0-323-90280-9.00004-3

Cited by 3 publications

(1 citation statement)

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“…Artificial intelligence (AI) examines computer code for particular characteristics and creates high-performing algorithms [14,15]. Healthcare approaches based on AI perform better for broad-level diagnostics and control.…”

Section: Introductionmentioning

confidence: 99%

A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique

Alam

Mirza

et al. 2023

Diagnostics

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COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.

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Section: Introductionmentioning

confidence: 99%