No abstract

The human race is under the COVID-19 pandemic menace since beginning of the year 2020. Even though the disease is easily transmissible, a massive fraction of the affected people are recovering. Most of the recovered patients will not experience death due to COVID-19, even if they observed for a long period. They can be treated as long term survivors (cured population) in the context of lifetime data analysis. In this article, we present some statistical methods to estimate the cure fraction of the COVID-19 patients in India. Proportional hazards mixture cure model is used to estimate the cure fraction and the effect of covariates gender and age on lifetime. The data available on website https://api.cvoid19india.org is used in this study. We can see that, the cure fraction of the COVID-19 patients in India is more than 90%, which is indeed an optimistic information.

Optimization algorithms are liable for sinking the losses and to give the most precise outcomes conceivable. Optimizers are utilized to modify the properties of neural network, for example, training rate and weights are used to reduce the losses. Optimization means a procedure of obtaining a global optimal solution for a given problem under given conditions. The real-world problems in the scientific fields, such as engineering design and economic planning, mostly are multimodal, high-dimensional, disconnected, and oscillated optimization problems. These complex problems cannot be solved well within reasonable time using traditional method based on gradient. Nature-inspired algorithms are becoming delightful in resolving mathematical optimization problems, like multiprocessor scheduling problem, vehicle routing and classification problems etc. In this manuscript, Whale Swarm Optimization algorithm on optimizing the neural networks, one of the meta-heuristic algorithms is applied to analysis of the cardiovascular disease dataset and compares the performance with Gradient Descent and RMSprop optimization techniques.

No abstract

No abstract