We present two effective methods for solving high-dimensional partial differential equations
(PDE) based on randomized neural networks. Motivated by the universal approximation …

We consider the use of extreme learning machines (ELM) for computational partial
differential equations (PDE). In ELM the hidden-layer coefficients in the neural network are …

A novel fuzzy-based cascade ensemble of regression models is proposed to address a
problem of extreme wind speed events forecasting, using data from atmospheric reanalysis …

Extreme learning machine (ELM) is a type of randomized neural networks originally
developed for linear classification and regression problems in the mid-2000s, and has …

In extreme learning machines (ELM) the hidden-layer coefficients are randomly set and
fixed, while the output-layer coefficients of the neural network are computed by a least …

ABSTRACT The Extreme Learning Machine (ELM) has sparked a lot of attention since it can
learn fast and be applied to various problems. In this study, a convolutional layer-based …

A new technique is proposed to increase the prediction accuracy of artificial neural networks
(ANNs). This technique applies a stepwise regression (SR) procedure to the input data …

Due to its fast training speed and powerful approximation capabilities, the extreme learning
machine (ELM) has generated a lot of attention in recent years. However, the basic ELM still …

A stock market enables the buying, selling and issuance of the shares of a publicly-held
company. With a large number of transactions in the market happening every second, it is …

Vaccine hesitancy, defined as the delay in accepting or even refusing vaccines despite their
availability, has become a significant public health concern. Researchers in social and …