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Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm
How COVID-19 is Accelerating the Digital Revolution: Challenges and Opportunities ; : 101-114, 2022.
Artículo en Inglés | Scopus | ID: covidwho-20241717
ABSTRACT
As the number of COVID-19 patients grows exponentially, not all cases are likely dealt with by doctors and medical professionals. Researchers will add to the fight against COVID-19 by developing smarter strategies to achieve accelerated control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus that causes disease. Proposed method suggests best ways to optimize protection and avoid COVID-19 spread. Big benefit of the hybrid algorithm is that COVID-19 is diagnosed and treated more rapidly. Pandemic diseases possibilities are handling with help of Computational Intelligence, using cases and applications from current COVID-19 pandemic. This work discusses data that can be analyzed based on optimization algorithm which provides betterCOVID-19 detection and diagnosis. This algorithm uses a machine learning model to decide how the hazard function changes concerning characteristics of potential methods to find parameters in optimization of machine learning model, which has in many cases been shown to be accurate for actual clinical datasets. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Texto completo: Disponible Colección: Bases de datos de organismos internacionales Base de datos: Scopus Tipo de estudio: Estudio experimental / Estudio observacional / Estudio pronóstico Idioma: Inglés Revista: How COVID-19 is Accelerating the Digital Revolution: Challenges and Opportunities Año: 2022 Tipo del documento: Artículo

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Texto completo: Disponible Colección: Bases de datos de organismos internacionales Base de datos: Scopus Tipo de estudio: Estudio experimental / Estudio observacional / Estudio pronóstico Idioma: Inglés Revista: How COVID-19 is Accelerating the Digital Revolution: Challenges and Opportunities Año: 2022 Tipo del documento: Artículo