ABSTRACT
Coronavirus is a quickly spreading viral sickness that infects people, yet in addition creatures as well. Clinical research of COVID-19-tainted patients revealed that these individuals are frequently infected by a lung infection as a result of their interactions with this Corona Virus Disease. Chest CT-scans images are used for diagnosing lungs related problems. Deep learning is the best method of AI, which gives valuable examination to consider a lot of chest CT-Scan pictures that can fundamentally effect on screening of Covid-19. Image and statistical data were used for the evaluation of accuracy and mean value analysis. The DenseNet method is one of the Convolutional Neural Network methods, which achieves better performance during image pre-processing and prediction. The accuracy of our proposed system is up to 95 to 97%, respectively. This kind of system helps to analyze the COVID-19 infection in its early stages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
During the recent outbreak of coronavirus, the concern about proliferation of misleading information, rumours and myths has caused governments across the world to institute various interventionist steps to stem their flow. Each government has had to balance the dichotomy between freedom of expression and people?s right to be safe from the adverse impact of inaccurate information. Governments across the world have implemented a number of strategies to manage COVID-19 including issuing public advisories, advertising campaigns, holding press conferences and instituting punitive regulations to combat the distribution of false and misleading information. We examine the two most populous countries? governments? response to the scourge of fake news during COVID-19. China and India are the most challenging nations to govern in terms of their sheer size and diversity of their population. Each country?s government has taken several steps to minimise the impact of fake news during COVID, within its own political system.