With increasing demand for energy, the penetration of alternative sources such as
renewable energy in power grids has increased. Solar energy is one of the most common …

Probabilistic forecasting is receiving growing attention nowadays in a variety of applied
fields, including hydrology. Several machine learning concepts and methods are notably …

Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …

Machine learning (ML), a subdiscipline of artificial intelligence studies, has gained
importance in predicting or suggesting efficient thermoelectric materials. Previous ML …

Background and Contexts. Autism spectrum disorder (ASD) is difficult to diagnose,
prompting researchers to increase their efforts to find the best diagnosis by introducing …

Background Fetal alcohol syndrome (FAS) is a lifelong developmental disability that occurs
among individuals with prenatal alcohol exposure (PAE). With improved prediction models …

Gridded satellite precipitation datasets are useful in hydrological applications as they cover
large regions with high density. However, they are not accurate in the sense that they do not …

The task of forecasting consumers' energy consumption is currently a trend in energy supply
companies. An accurate prediction of energy consumption is a powerful tool to check for …

Merging satellite and gauge data with machine learning produces high-resolution
precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of …

Clustering has grown to be a research focus in recent years owing to the challenges of
labeling massive collected data. Recent advances such as the emotional preference and …