ABSTRACT
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.
ABSTRACT
BACKGROUND: SARS-CoV-2, an emerged strain of corona virus family became almost serious health concern worldwide. Despite vaccines availability, reports suggest the occurrence of SARS-CoV-2 infection even in a vaccinated population. With frequent evolution and expected multiple COVID-19 waves, improved preventive, diagnostic, and treatment measures are required. In recent times, phytochemicals have gained attention due to their therapeutic characteristics and are suggested as alternative and complementary treatments for infectious diseases. This present study aimed to identify potential inhibitors against reported protein targets of SARS-CoV-2. METHODOLOGY: We computationally investigated potential SARS-CoV-2 protein targets from the literature and collected druggable phytochemicals from Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database. Further, we implemented a systematic workflow of molecular docking, dynamic simulations and generalized born surface area free-energy calculations (MM-GBSA). RESULTS: Extensive literature search and assessment of 1508 articles identifies 13 potential SARS-CoV-2 protein targets. We screened 501 druggable phytochemicals with proven biological activities. Analysis of 6513(501 *13) docked phytochemicals complex, 26 were efficient against SARS-CoV-2. Amongst, 4,8-dihydroxysesamin and arboreal from Gmelina arborea were ranked potential against most of the targets with binding energy ranging between - 10.7 to - 8.2 kcal/mol. Additionally, comparative docking with known drugs such as arbidol (-6.6 to -5.1 kcal/mol), favipiravir (-5.5 to -4.5 kcal/mol), hydroxychloroquine (-6.5 to -5.1 kcal/mol), and remedesivir (-8.0 to -5.3 kcal/mol) revealed equal/less affinity than 4,8-dihydroxysesamin and arboreal. Interestingly, the nucleocapsid target was found commonly inhibited by 4,8-dihydroxysesamin and arboreal. Molecular dynamic simulation and Molecular mechanics generalized born surface area (MM-GBSA)calculations reflect that both the compounds possess high inhibiting potential against SARS-CoV-2 including the recently emerged Omicron variant (B.1.1.529). CONCLUSION: Overall our study imparts the usage of phytochemicals as antiviral agents for SARS-CoV-2 infection. Additional in vitro and in vivo testing of these phytochemicals is required to confirm their potency.
ABSTRACT
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.
ABSTRACT
The main focus of this Research is to see how one can easily predict if he/she has been infected by COVID-19. Another aspect is deciding which COVID-19 symptom is more likely to show positive result of virus contamination. The virus has been declared as a pandemic and has affected more than 66,729,375 people across 220 countries and has also cost the lives of 1,535,982 people [source : who.int] as of the time this paper is being written. Research still predicts that another second wave is to hit soon. The required objective is obtained using complex machine learning algorithms that are able to predict, up to an extent the probability that the person has covid-19 and also if the related covid-19 symptoms provided are relevant to the condition or not. The algorithm used is a LOGISTIC REGRESSION algorithm that is used as a classification tool to separate the data into binary results, which in our case is if the person has covid-19 or not (YES OR NO). The dataset used to train this machine learning algorithm is obtained from online resources and a public survey.The machine learning model as of now has been able to predict the probability of virus contamination by 66.89% accuracy, further the model is able to relate if a given symptom is valid or not. With this we are able to conclude that the model is working fine and only fine tuning of the model is required in order to improve and enhance the accuracy and probability of exact results. The result were then improved using ANN( Artificial Neural Networks) but as it is computational expensive and due to lack of resources the expected performance cannot be met. The maximum accuracy achieved with ANNs was about 71%. © 2021 IEEE.
ABSTRACT
Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days.