Characterizing the electric energy curve can improve the energy efficiency of existing
buildings without any structural change and is the basis for controlling and optimizing …

Applying computational statistics or machine learning methods to data is a key component of
many scientific studies, in any field, but alone might not be sufficient to generate robust and …

Self-driving laboratories (SDLs) represent a cutting-edge convergence of machine learning
with laboratory automation. SDLs operate in active learning loops, in which a machine …

Extreme value theory is the state-of-the-art modelling technique for estimating crash risk
from traffic conflicts, with two different sampling techniques, ie block maxima and peak-over …

Pedestrians represent a group of vulnerable road users who are at a higher risk of
sustaining severe injuries than other road users. As such, proactively assessing pedestrian …

Nowadays there is a large amount of information at our disposal, which is increasing day by
day, and right now the question is not whether we have a method to process it, but which …

Outlier detection is crucial for improving the performance of machine learning algorithms
and is particularly vital in data sets possessing a small number of points. While the existing …

Owing to the advancement in the technologies, the vision of smart manufacturing is not
implausible. Development of sophisticated measuring tools, modelling approaches …

Abstract Machine learning (ML) models are increasingly popular in environmental and
hydrologic modeling, but they typically contain uncertainties resulting from noisy data …

This work is driven by a practical question: corrections of Artificial Intelligence (AI) errors.
These corrections should be quick and non-iterative. To solve this problem without …