COVID19: INTELLIGENT CHATBOT AND FINANCIAL AID APPLICATION

Covid19 has devastated all continents causing disasters not only on the health sector but also at social, economic, and at political levels. The world is still trying to eradicate the virus. One of the measures taken is to inform citizens about the virus in order to avoid contamination as much as possible. Several people lost their jobs, and found themselves without any income. The whole world is confined, and the poor can no longer endure this critical situation. Financial assistance is therefore necessary in order to reduce the impact. This paper aims to propose an intelligent financial support application that computes the eligibility for a citizen to get a support during the pandemic;and to explain steps for chatbot using DialogFlow. The training realized using a machine learning algorithm was chosen after making a comparison between some other algorithms. Gradient Boosting Classifier algorithm was the accurate and most efficient for the application. It is possible to train the system again using other data set to make any adaptive results or computations. Copyright © (2022) by International Association for Development of the Information Society (IADIS). All rights reserved.

Prediction of Happiness Score of Countries by Considering Maximum Infection Rate of People by COVID-19 using Random Forest Algorithm

In this paper, the relationship between COVID-19 Maximum Infection Rate (MIR) and the happiness indicators has been investigated for the prediction of Happiness Score of Countries using Random Forest (RF) algorithm. The per-formance of the proposed algorithm is also compared against five other algorithms such as Linear Regression (LR), Ada Boost Classifier (ABC), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (NB) and Logistic Regression. The comparison of performance includes parameters like training accuracy, testing accuracy and computation time. It is clear from the observation that the proposed approach is superior to others. Then the parameters like MAE, MSE, RMSE, R2 Score, Adjusted R2 Score is calculated. This proposed algorithm can be used for other classification and regression work involving large amount of data with missing values like COVID- 19 datasets. © 2022 IEEE.

Mini Medical Assistant: A Handheld Emergency Medical Support Program During COVID19 Pandemic

Android phones are one of the most common accessories used all over the world. Although once a luxury, it has now become a basic need for all generations. It is a multipurpose tool that can be used for all sorts of necessities and entertainment. Through our android app corona care, a mobile phone can be a helping hand for health care. This app can help prevent the deadly virus known as COVID-19 through plasma donation, consultation with doctors, setting up appointments, predicting corona risk assessment from symptoms using the Gaussian Naive Bayes method of predicting the risk percentage, providing emergency health services and updating users about the safety instructions about Covid-19. Our application consists of most features needed in a mHealth application that can provide necessary medical assistance to each and every household. © 2021 IEEE.

Twitter Sentiment Analysis on Approval of Covaxin for Children in India

Covid-19 and its different variants are still a big issue the whole world is facing right now. At present different SARS-CoV-2 vaccines are playing vital role to combat the coronavirus. The objective of this paper is to perform sentiment analysis on approval of Bharat Biotech covaxin for emergency use for children. The presented paper emphasizes on the sentiment analysis of tweets of the microblogging site Twitter. Python programming language with Natural Language processing toolkit (NLTK), TextBlob library and tweepy twitter API are used for the process. Machine learning algorithms are used for the classification of tweeets. Graphical representation has been used for the representation of the data after sentiment analysis based on hashtags. © 2022 IEEE.

Performance Comparison of Multiple Supervised Machine Learning Algorithms for COVID-19 Mortality Prediction

The coronavirus disease (COVID-19) has wreaked havoc on populations around the world. Every day, thousands of people are dying as a result of this lethal virus. Patients with pre- existing conditions, as well as the elderly, are more susceptible to the disease. Artificial intelligence can play a vital role to track patient health conditions using various parameters. It assists in determining how to best handle certain patients in order to save their lives. The various parameters of a patient's health condition may have a significant impact on the outcome. Various artificial intelligence strategies are a blessing in minimizing the loss from COVID-19. This paper focuses on predicting the potential outcome of a patient using the COVID-19 dataset obtained from John Hopkins University of infected patients, which will help minimizing the death toll of COVID-19 disease. In this study, the performance of various machine learning models is compared for predicting COVID-19-affected patient's mortality using Logistic Regression, Support Vector Machine, K Nearest Neighbor, Decision Tree and Gaussian Naive Bayes. Finally, the best model for hyper parameter tuning was chosen from the comparative section. After hyper parameter optimization, a maximum accuracy of 95 percent and an F1 score of 89 percent using the K Nearest Neighbor algorithm was achieved. © 2022 IEEE.