Analysis and Prediction of COVID-19 by using Recurrent LSTM Neural Network Model in Machine Learning

As we all know that corona virus is announced as pandemic in the world by WHO. It is spreaded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self preventive measures are the best strategies. As of now many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the corona virus disease behaves in exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To do this prediction of active cases, we need database. The database of COVID-19 is downloaded from KAGGLE website and is analyzed by applying recurrent LSTM neural network with univariant features to predict for the number of active cases of patients suffering from corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with testing dataset to predict the number of active cases in a particular state here we have concentrated on Andhra Pradesh state.

Development of medical assistance systems by cloud computing networks with Covid-19 datasets

COVID-19 Disease has been declared a pandemic by the World Health Organization (WHO) within 10 million cases and 503862 neglected lands as indicated by the WHO test on 30 June 2020. Coronavirus is caused by a major respiratory problem called Corona 2 (SARS-CoV). -2) and was reported by the WHO on March 11, 2020. So, for medical assistance, we have chosen cloud computing networks rather than IoT and Distributed parallel networks. Because of the present scenario Corona Virus that causes Covid'19 is mainly transmitted through droplets generated when an infected person coughs, sneezes, or exhales. The cough droplets are very much high and they were too heavy to droop in the atmosphere and quickly fall on floors or surfaces. We are using Google's cloud platform because it is more scalable and reliable. The services promote the clients to compute, store data, to test their respective applications. So, we are collecting covid-19 datasets from Github data sources and we are going to use Conventional Neural networks (CNN) and models to predict and analyze covid-19 cases around the world. © 2022 Author(s).