In the community of computational materials science, one of the challenges in hierarchical
multiscale modeling is information-passing from one scale to another, especially from the …

This paper presents a new regularization for Extreme Learning Machines (ELMs). ELMs are
Randomized Neural Networks (RNNs) that are known for their fast training speed and good …

This paper presents a novel dimensionality reduction technique based on ELM and SOM:
ELM-SOM+. This technique preserves the intrinsic quality of Self-Organizing Map (SOM): it is …

With the onset of easy access to supercomputers with high amounts of memory available,
machine learning algorithms have continued to increase the resources necessary to perform …

A variety of multidimensional visualization methods are applied for the qualitative analysis of
multidimensional data. One of the multidimensional data visualization methods is a method …

This paper presents a novel methodology for time series prediction. It is based on Extreme
Learning Machines and an adaptive ensemble techniques. It is tested successfully on the …

This paper presents a novel methodology to analyze nurses' willingness to report
medication errors. Parallel Extreme Learning Machines were applied to identify the top …

Abstract This dissertation explores Random Neural Networks (RNNs) in several aspects and
their applications. First, Novel RNNs have been proposed for dimensionality reduction and …

This paper presents a novel methodology to analyze nursing surveys. It is based on ELM
and SOM. The goal is to identify which variables lead to the likelihood to report the …

Data visualization plays an important role in the analysis and processing of Big Data and
this becomes more important with the explosive growth in the need to analyze and use data …