![]() ![]() It has been observed that AE-based deep learning models outperformed some of the existing machine learning and deep learning techniques with a maximum accuracy of 99.66%. Our autoencoder-based logistic regression, decision trees, and support vector machine improves the prediction performance and accuracy towards anticipating the spread of coronavirus. We have considered machine learning models, including logistic regression, support vector machine, and decision trees and recurrent deep neural network model for performance comparison with proposed Autoencoder (AE) based models. Our AI driven prediction model can efficiently identify distinct classes of severity, which includes no-infection, mild, moderate, and severe cases where a patient may require intensive medical care. ![]() Our proposed model is based on certain symptoms, such as fever, dry cough, fatigue, difficulty in breathing, aches, sore throat, nasal congestion, diarrhea, etc. Our research aims on developing an Autoencoder (AE) based multi-class prediction model for detecting the severity of coronavirus. ![]() Though the pace of the pandemic slowed down owing to the vaccination process, still new mutant variants of the virus continue to evolve. The investigation can help in designing appropriate environmental policies for promoting financial development.Ĭoronavirus pandemic has hampered human life all over the world with several serious casualties and numerous deceased cases. Our results reveal that whereas the relationship of financial development with SO2 and SW emissions shows a significant U-shaped pattern, that of economic growth exhibit a significant inverted U-shaped pattern. However, financial development significantly decreases SW emissions of a particular region but does not exert a significant impact on its surrounding regions, implying a weak spillover effect. Moreover, a region’s pollutant emissions can be influenced by the financial development of its surrounding regions, suggesting that financial development reduces SO2 emissions in a particular region, but it significantly increases SO2 emissions in surrounding regions, indicating a strong spillover effect. The results show a positive spatial spillover effect on pollutant emissions across various regions. Industrial sulfur dioxide (SO2) and solid waste (SW) emissions are used to quantify pollutant emissions in China. ![]() Also, the spatial transmission mechanism between financial development and pollutant emissions is analyzed theoretically. This study aims to investigate the nonlinearity between financial development and pollutant emissions while considering the various stages of financial development among regions. Financial development is affected dramatically by the real economy and typically shows nonlinear characteristics. This study expands the understanding of the relationship between COVID-19 cases and deaths by using a global dataset and the instrumental variable generalized method of moment’s model (IV-GMM) for the analysis that addresses endogeneity and omitted variable issues.Īssessing the environmental effects of financial development has an important theoretical and practical reference for the government to achieve the goal of sustainable development. Regionally, a 1% increase in confirmed cases increases attributable deaths by 0.65% in Africa, 0.90% in the Americas, 0.67% in the Eastern Mediterranean, 0.72% in Europe, 0.88% in Southeast Asia, and 0.52% in the Western Pacific. At the global level, a 1% increase in confirmed cases increases attributable deaths by 0.78%. Thus, the confirmed cases significantly increase attributable deaths at the global and regional levels. The results showed that COVID-19 confirmed cases at both the global and regional levels have a strong positive effect on deaths. We used a panel of 232 countries (further disaggregated into Africa-49, Americas-54, Eastern Mediterranean-23, Europe-61, Southeast Asia-10, and Western Pacific-35) from 03 January 2020 to 28 November 2020, and the instrumental variable generalized method of moment’s model (IV-GMM) for analysing the datasets. Motivated by a recent dataset, knowledge gaps, surge in global cases, and the need to combat the virus spread, this study examined the relationship between COVID-19 confirmed cases and attributable deaths at the global and regional levels. At present, the virus has spread across 232 countries worldwide killing 2,409,011 as of 17 February 2021 (9:37 CET). In late December 2019, strange pneumonia was detected in a seafood market in Wuhan, China which was later termed COVID-19 by the World Health Organization. ![]()
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