Artificial neural networks and statistical models for optimization studying COVID-19
نویسندگان
چکیده
منابع مشابه
Neural Networks and Statistical Models
There has been much publicity about the ability of artificial neural networks to learn and generalize. In fact, the most commonly used artificial neural networks, called multilayer perceptrons, are nothing more than nonlinear regression and discriminant models that can be implemented with standard statistical software. This paper explains what neural networks are, translates neural network jarg...
متن کاملComparison of Artificial Neural Networks and Cox Regression Models in Prediction of Kidney Transplant Survival
Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...
متن کاملComparison of Artificial Neural Networks and Cox Regression Models in Prediction of Kidney Transplant Survival
Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...
متن کاملStatistical downscaling with artificial neural networks
Statistical downscaling methods seek to model the relationship between large scale atmospheric circulation, on say a European scale, and climatic variables, such as temperature and precipitation, on a regional or subregional scale. Downscaling is an important area of research as it bridges the gap between predictions of future circulation generated by General Circulation Models (GCMs) and the e...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Results in Physics
سال: 2021
ISSN: 2211-3797
DOI: 10.1016/j.rinp.2021.104274