Abstract: Gaussian process (GP) regression has been widely used in supervised machine learning due to its flexibility and inherent ability to describe uncertainty in function estimation. In the ...

Predictive models turn historical data into reliable forecasts that support accurate planning across industries. Different modeling types solve different problems, from forecasting numbers to ...

Background and Aims: Carotid plaque is an early manifestation of atherosclerosis and is closely associated with the risk of myocardial ischemia, ischemic stroke, and other atherosclerotic ...

Predictive analytics has emerged as a formidable tool in the decision-making arsenal of modern businesses, particularly in the financial sector where it plays a crucial role in predicting SME loan ...

Tableau, TIBCO Data Science, IBM and Sisense are among the best software for predictive analytics. Explore their features, pricing, pros and cons to find the best option for your organization.

Objective The aim of this study was to construct a predictive model in order to develop an intervention study to reduce the prevalence of stunting among children aged 12–23 months. Design The study ...

Objectives The primary objective of this observational study was to assess the status of public place and workplace compliance with smoke-free provisions in Ethiopia. Methods This study was conducted ...

Abstract: Model predictive control is a promising technique for electric drives as it enables optimization for multiple parameters and offers reliable operation with nonlinear systems. In this article ...

Background Configurational methods are increasingly being used in health services research. Objectives To use configurational analysis and logistic regression within a single data set to compare ...