ABSTRACT
Corona -virus disease commonly known as COVID-19 that outbreak in late December 2019 is continuously spreading worldwide and infecting people due to which it's required to analysis research on the expansion of CODID-19.In this research, a more improved model. HYBRID ARTIFICAL MODEL (AI) is suggested for prediction. In conventional model, it treats similar infection rate for all people, an improvised ISI (improved susceptible-infected) is suggested to gauge the infection rate to calculate the development mode. We have build the hybrid AI model by using natural language processing(NLP) model and long short-term memory(LSTM) network modules inside ISI module.According to the attentive results, it represents more infections from three to eight days.In comparison to both the models , our developed new AI model can remarkably reduces the prediction result's error and prevail the mean percentage errors with different percentage for the six consecutive days in different countries.For example-China , Italy, France, etc. © 2022 IEEE.
ABSTRACT
Based on several pre-defined standard symptoms, a model that can determine the coronavirus illness as positive is developed. Guidelines for these symptoms have been issued by the World Health Organization (WHO) and India's Ministry of Health and Family Welfare. In this model the various symptoms of the illnesses is given to the system. It allows users to discuss their symptoms, with the algorithm predicting a condition based on factual information. This factual information is then evaluated using the ARM based Apriori algorithm to get the most accurate results. Other conventional models such as Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forests (RF) are considered and have analyzed the predictions and have found that the proposed algorithm predicts a higher accuracy score. © 2022 IEEE.