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.

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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 nonlinear 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

Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis.

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.