The scalp electroencephalogram (EEG)-based epileptic seizure/nonseizure detection has
been comprehensively studied, and fruitful achievements have been reported in the past …

Kernel-based extreme learning machine (KELM), as a natural extension of ELM to kernel
learning, has achieved outstanding performance in addressing various regression and …

The scalp electroencephalogram (EEG) has been extensively studied for epileptic signal
classification in the past, but little attention has been paid to the data imbalance among …

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for
epileptic electroencephalogram (EEG) signal classification. The goal is to develop a …

Abnormal electricity consumption (AEC) caused huge economic losses to power supply
enterprises in the past years, and also posed severe threats to the safety of peoples' daily …

Extreme learning machine (ELM) has received considerable attention due to its rapid
learning speed and powerful fitting capabilities. One of its important variants, the elastic-net …

Autoencoding is a vital branch of representation learning in deep neural networks (DNNs).
The extreme learning machine-based autoencoder (ELM-AE) has been recently developed …

Along with the explosive growing of data, semi-supervised learning attracts increasing
attention in the past years due to its powerful capability in labeling unlabeled data and …

The rapidity of ore grade identification is key to speeding up the beneficiation process in the
mining process. Traditional ore grade detection mostly relies on chemical methods …

One of the significant features of extreme learning machines (ELMs) is their fast
convergence. However, in the big data environment, the ELM based on the Moore–Penrose …