BACKGROUND The Institute of Medicine 2011 Report on Dietary Reference Intakes for Calcium and Vitamin D specified higher intakes for all age groups compared to the 1997 report, but also cautioned against spurious claims about an epidemic of vitamin D deficiency and against advocates of higher intake requirements. Over 40 years, we have noted marked improvement in vitamin D status but we are con...

The integration of renewable energy resources into smart grids has become increasingly important to address the challenges managing and forecasting production in fourth revolution. To this end, artificial intelligence (AI) emerged as a powerful tool for improving control management. This study investigates application machine learning techniques, specifically ARIMA (auto-regressive integrated m...

BACKGROUND In Latin America, there are 13 geographically isolated endemic foci distributed among Mexico, Guatemala, Colombia, Venezuela, Brazil and Ecuador. The communities of the three endemic foci found within Mexico have been receiving ivermectin treatment since 1989. In this study, we predicted the trend of occurrence of cases in Mexico by applying time series analysis to monthly onchocerci...

This paper provides a Bayesian analysis of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. We discuss in detail inference on impulse responses, and show how Bayesian methods can be used to (i) test ARFIMA models against ARIMA alternatives, and (ii) take model uncertainty into account when making inferences on quantities of interest. Our methods are then used to investigat...

Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel m...

Forecasting electricity demand requires accurate and sustainable data acquisition systems which rely on smart grid systems. To predict the expected by grid, many meters are required to collect sufficient data. However, problem is multi-dimensional simple power aggregation techniques may fail capture relational similarities between various types of users. Therefore, forecasting energy plays a ke...

During the COVID-19 outbreak, governments, scientists, health workers, and numerous people worked on strategies or solutions for halting disease propagation. Unfortunately, need monitoring is steeply increasing, taking necessary restrictive actions currently unavoidable. Due to lack of epidemiological data constantly changing numbers, constructing less error-prone predictive models reliable mat...

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...