ABSTRACT
The Covid-19 Corona Virus, also known as SARS-CoV-2, has wreaked havoc around the world, and the condition is only getting worse.It is a pandemic disease spreading from person-to-person every day. Therefore, it is important to keep track the number of patients being affected. The current system gives the computerized data in a collective way which is very difficult to analyze and predict the growth of disease in a particular area and in the world. Machine learning algorithms can be used to successfully map the disease and its progression to solve this problem. Machine Learning, a branch of computer science, is critical in correctly distinguishing patients with the condition by analyzing their chest X-ray photographs. Supervised Machine learning models with associated algorithms (like LR, SVR and Time series algorithms) to analyze data for regression and classification helps in training the model to predict the number of total number of global confirmed cases who will be prone to the disease in the upcoming days. In this proposed work, the overall dataset of the world is being collected, preprocessed and the number of confirmed cases up to a particular date are extracted which is given as the training set to the model. The model is being trained by supervised machine learning algorithms to predict the growth of cases in the upcoming days. The experimental setup with the above mentioned algorithms shows that Time series Holt's model outperforms Linear Regression and Support Vector Regression algorithms.
ABSTRACT
The disposal of waste generated by the COVID-19 pandemic is still a challenge to the government in most countries. The present study shines its light on the catalytic effect of metal oxide on converting COVID-19 waste i.e. used face masks into valuable products through co-pyrolysis. The co-pyrolysis trial was carried out for a mixture of waste face mask (WFM) and Moringa oleifera (MO) biomass at a constant temperature of 450°C for 15â min of resident time. This investigation focuses on studying the catalytic effect of calcium oxide (CaO) on the by-products of the pyrolysis process. From the FT-IR studies, it is observed that the CaO catalyst assisted to reduce oxygen as well as sulphur and carboxylic acids in the bio-oil due to its strong basic nature. The FE-SEM images suggest the increase in porous structure with catalytic pyrolysis (CP) char compared to non-catalytic pyrolysis (NCP) char. The catalytic activity of CaO increased the alcoholic content with a reduction in aldehydes and ketones in the bio-oil. The addition of WFM to the biomass with CaO catalyst pyrolysis (CP) delivered a higher oil yield of 52% compared to non-catalytic pyrolysis (NCP).
ABSTRACT
Covid-19 is a corona virus pandemic disease affected by a new corona virus. Maximum people infected by covid-19 will experience symptoms namely mild to moderate respiratory illness and recover without requiring any special treatment. However elderly people and those having underlying medical diseases such as diabetes, cardiovascular diseases, cancer and chronic respiratory disease are more prone to develop serious illness. Reliability analysis for medical test for covid-19 is performed using a Bayesian network. A Bayesian network (BN) is a probabilistic graphical model that represents knowledge about an uncertain domain where each node corresponds to a random variable and each edge represents the corresponding conditional probability. The BN is used to prioritize the factors that influence virus symptoms of covid-19. The BN model is constructed based on a list of general symptoms of covid-19. The marginal probabilities for all states are computed. The comparison of prior and conditional probabilities is determined. Using BN the reliability of medical test for covid-19 is obtained. © 2022 American Institute of Physics Inc.. All rights reserved.