Titlebook: Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approach; Aboul Ella Hassanien,Ashraf Darwish

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Ablation

BLA

Angelo Antonio Salatino,Enrico Mottaand infections continues to rise rapidly since the virus date of appearance. COVID-19 threatens not only human health but also many aspects of life such as manufacturing, social performance, and international relations. Emerging technologies can help in the fight against COVID-19. Emerging technolog

植物群

Semantics, Pragmatics and Meaning Revisitedomic, social life, and international relations around the world. There are different medical methods to detect and diagnose this disease such as viral nucleic acid screening by using specimens of the lower respiratory tract. However, the availability of sufficient laboratory screening in the infeste

synchronous

https://doi.org/10.1007/978-3-319-69116-9nally disagree with their judgments. The performance of COVID-19 detection models needs to be improved. According to COVID-19 symptoms and human immune approach response, there are four types of its contagion such as asymptomatic, mild, severe, and recovered. In this chapter, an automatic scoring of

employor

https://doi.org/10.1007/978-0-387-72926-8 cultural, social and foreign relations around the world. Accurate diagnosis has to turn into a critical issue affecting the containment of this disease, especially at the countries which outbreak the virus. In the fight against COVID-19, Artificial Intelligence (AI) techniques have played a signifi

分开如此和谐

https://doi.org/10.1007/978-0-387-72926-8l of the Coronavirus. Largest of world economies and developed nations have been exposed and their health infrastructure has collapsed during this testing time. It is assessed and predicted that the novel coronavirus which is responsible for COVID19 pandemic, may turn into an endemic (just like HIV)

政府

根除

https://doi.org/10.1007/978-3-642-38019-8e symptoms die especially the elderly. In this chapter, three machine learning techniques have been chosen and tested to predict the patient’s recovery of Coronavirus disease. The support vector machine has been tested on the given data with a mean absolute error of 0.2155. The Epidemiological data

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