The investigation of the accuracy of methods employed to forecast agricultural commodities
prices is an important area of study. In this context, the development of effective models is …

Performance estimation aims at estimating the loss that a predictive model will incur on
unseen data. This process is a fundamental stage in any machine learning project. In this …

Human osteosarcoma (OS) is the most common primary malignant bone tumor occurring
most commonly in adolescents and young adults. Major improvements in disease-free …

Water quality monitoring plays a vital role in the protection of water resources, environmental
management, and decision-making. Artificial intelligence (AI) based on machine learning …

Background The potential to harness the plurality of available data in real time along with
advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks …

With the profusion of web content, researchers have avidly studied and proposed new
approaches to enable the anticipation of its impact on social media, presenting many distinct …

Time series prediction has been widely applied to the finance industry in applications such
as stock market price and commodity price forecasting. Machine learning methods have …

A method of establishing a prediction model of the greenhouse temperature based on time-
series analysis and the boosting tree model is proposed, aiming at the problem that the …

In this paper, noting that the prediction of time series follows the temporal order of data, we
propose a frequentist model averaging method based on forward-validation. Our method …

The increasing use of sensor networks has led to an ever larger number of available
spatiotemporal datasets. Forecasting applications using this type of data are frequently …