COVID-19 Outbreak Prediction and Analysis of E-Healthcare Data Using Random Forest Algorithms

The forecasting model used random forest algorithm. From the outcomes, it has been found that the regression models utilize basic linkage works and are exceptionally solid for forecast of COVID-19 cases in different countries as well as India. Current shared of worldwide COVID-19 confirmed case has been predicted by taking the world population and a comparatives study has been done on COVID-19 total cases growth for top 10 worst affected countries including US and excluding US. The ratio between confirmed cases vs. fatalities of COVID-19 is predicted and in the end a special study has been done on India where we have forecasted all the age groups affected by COVID-19 then we have extended our study to forecast the active, death and recovered cases especially in India and compared the situation with other countries.

Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models.

One of the most difficult research areas in today's healthcare industry to combat the coronavirus pandemic is accurate COVID-19 detection. Because of its low infection miss rate and high sensitivity, chest computed tomography (CT) imaging has been recommended as a viable technique for COVID-19 diagnosis in a number of recent clinical investigations. This article presents an Internet of Medical Things (IoMT)-based platform for improving and speeding up COVID-19 identification. Clinical devices are connected to network resources in the suggested IoMT platform using cloud computing. The method enables patients and healthcare experts to work together in real time to diagnose and treat COVID-19, potentially saving time and effort for both patients and physicians. In this paper, we introduce a technique for classifying chest CT scan images into COVID, pneumonia, and normal classes that use a Sugeno fuzzy integral ensemble across three transfer learning models, namely SqueezeNet, DenseNet-201, and MobileNetV2. The suggested fuzzy ensemble techniques outperform each individual transfer learning methodology as well as trainable ensemble strategies in terms of accuracy. The suggested MobileNetV2 fused with Sugeno fuzzy integral ensemble model has a 99.15% accuracy rate. In the present research, this framework was utilized to identify COVID-19, but it may also be implemented and used for medical imaging analyses of other disorders.

COVID-19 detection with x-ray images by using transfer learning

Coronavirus is an infectious disease induced by extreme acute respiratory syndrome coronavirus 2. Novel coronaviruses can lead to mild to serious symptoms, like tiredness, nausea, fever, dry cough and breathlessness. Coronavirus symptoms are close to influenza, pneumonia and common cold. So Coronavirus can only be confirmed with a diagnostic test. 218 countries and territories worldwide have reported a total of 59.6 million active cases of the COVID-19 and 1.4 million deaths as of November 24, 2020. Rapid, accurate and early medical diagnosis of the disease is vital at this stage. Researchers analyzed the CT and X-ray findings from a large number of patients with coronavirus pneumonia to draw their conclusions. In this paper, we applied Support Vector Machine (SVM) classifier. After that we moved on to deep transfer learning models such as VGG16 and Xception which are implemented using Keras and Tensor flow to detect positive coronavirus patient using X-ray images. VGG16 and Xception show better performances as compared to SVM. In our work, Xception gained an accuracy of 97.46% with 98% f-score. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 Outbreak Prediction and Analysis of E-Healthcare Data Using Random Forest Algorithms

The forecasting model used random forest algorithm. From the outcomes, it has been found that the regression models utilize basic linkage works and are exceptionally solid for forecast of COVID-19 cases in different countries as well as India. Current shared of worldwide COVID-19 confirmed case has been predicted by taking the world population and a comparatives study has been done on COVID-19 total cases growth for top 10 worst affected countries including US and excluding US. The ratio between confirmed cases vs. fatalities of COVID-19 is predicted and in the end a special study has been done on India where we have forecasted all the age groups affected by COVID-19 then we have extended our study to forecast the active, death and recovered cases especially in India and compared the situation with other countries.

COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.

Prediction of COVID-19 active cases using exponential and non-linear growth models

World Health Organization recognized COVID-19 as a pandemic on March 11, 2020. A total of 213 countries and territories around the world have reported a total of 27,948,441 confirmed cases as on September 9, 2020. This article adopted two non-linear growth models (Gompertz, Verhulst) and exponential model (SIR) to analyse the coronavirus pandemic across the world. All the models have been used for active COVID-19 patients predictions based on the data collected from John Hopkins University repository in the time period of January 30, 2020 to June 4, 2020. Outbreak of COVID-19 disease has been analysed for India, Pakistan, Myanmar (Burma), Brazil, Italy and Germany till June 4, 2020 and predictions have been made for the number of positive cases for the next 28 days. Verhulst model fitting effect is better than Gompertz and SIR model with R-score 0.9973. The proposed model perform better as compare to other three existing models with R-score 0.9981.These above models can be adapted to forecast in long term intervals, based on the predictions for a short interval as of June 5, 2020 and June 30, 2020, active COVID-19 patients for India, Pakistan, Italy, Germany, Brazil and Myanmar predicted as (236,170, 88,998, 234,066, 184,922, 645,057 and 235) and (486,357, 218,864, 240,545, 193,727, 1,211,567 and 309).

Analyses the effects of COVID-19 outbreak on human sexual behaviour using ordinary least-squares based multivariate logistic regression.

This study aimed to evaluate the impact of COVID-19 on sexual, mental and physical health. There were 262 respondents included in this study (38% female and 62% male) above 18 years of age from India. Statistical analysis was performed using Ordinary Least Squares (OLS) based on multivariate logistic regression analysis. The numerical tests were performed by using Python 3 engine and R-squared (coefficient of multiple determinations for multiple regressions) for prediction and P value > 0.5 is considered to be statistically significant. The study outcomes were obtained using a study-specific questionnaire to assess the quality of sex life, changes in sexual behavior and mental health. Frequency of sexual intercourse, frequency of watching porn, sexual hygiene, frequency of physical activity, depression, desire for parenthood in female respondents have more significant R 2 (0.903, 0.976, 0.973, 0.989, 0.985, 0.862) value respectively as compared to male respondents. Financial anxiety, Smoking and drinking habits in male respondents have more significant R 2 (0.917, 0.964) value respectively as compared to female respondents. The aim of this study is to understand quality of sex life, sexual behavior, reproductive planning, mental health, physical health and adult coping during the COVID-19 pandemic, as well as how past experiences have affected. Many respondents had a broad variety of problems concerning their sexual and reproductive well being. Measures should be set in order to safeguard the mental and sexual health of people during the pandemic.