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Analysis of the Impacts of COVID-19 using Deep Convolutional Neural Network
2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023 ; : 173-179, 2023.
Article in English | Scopus | ID: covidwho-2325769
ABSTRACT
COVID-19 is the transmittable disease that emerged as a recent epidemic and threatened the lives of various people. The emerged pandemic initiated a change in the people's routine and impacted a serious financial crisis. This initiated a necessity for developing a deeper insight of the COVID-19 disease and multiple researches are performed based on the COVID-19 epidemic, which possess the challenges of basic analysis of information about the disease, lack of data, lack of knowledge about the parameters that cause disease and to overcome this a deep COVID-19 analysis epidemic via the deep CNN classifier is accomplished in the research. The impact of the disease is examined based on the gender, age group, symptoms and outbreak of the disease. This analysis provides comprehensive information about the disease and helps in making the preventive measures, which will greatly reduce the impacts of the disease. The accomplishment of deep CNN instinctively analyzes the essential features needed for the classification that helps in reducing the effort and time of the individuals. The performance is analyzed with the metrics specificity, accuracy and sensitivity, which obtained values of 0.48 %, 0.27 %, 2.82 % corresponding to and 2.88 %, 1.5 %, 0.36% considering training percentage, which is more efficient. © 2023 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Language: English Journal: 2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023 Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies Language: English Journal: 2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023 Year: 2023 Document Type: Article