A review on extreme learning machine
Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward
neural network (SLFN), which converges much faster than traditional methods and yields …
neural network (SLFN), which converges much faster than traditional methods and yields …
A systematic review on machine learning and deep learning models for electronic information security in mobile networks
Today's advancements in wireless communication technologies have resulted in a
tremendous volume of data being generated. Most of our information is part of a widespread …
tremendous volume of data being generated. Most of our information is part of a widespread …
A review of the role of machine learning techniques towards brain–computer interface applications
S Rasheed - Machine Learning and Knowledge Extraction, 2021 - mdpi.com
This review article provides a deep insight into the Brain–Computer Interface (BCI) and the
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
application of Machine Learning (ML) technology in BCIs. It investigates the various types of …
A comprehensive review of extreme learning machine on medical imaging
The feedforward neural network based on randomization has been of great interest in the
scientific community, particularly extreme learning machines, due to its simplicity, training …
scientific community, particularly extreme learning machines, due to its simplicity, training …
A review on semi-supervised learning for EEG-based emotion recognition
Semisupervised learning holds significant academic and practical importance in the realm of
EEG-based emotion recognition. Currently, a multitude of research endeavors are dedicated …
EEG-based emotion recognition. Currently, a multitude of research endeavors are dedicated …
OGSSL: A semi-supervised classification model coupled with optimal graph learning for EEG emotion recognition
Electroencephalogram (EEG) signals are generated from central nervous system which are
difficult to disguise, leading to its popularity in emotion recognition. Recently, semi …
difficult to disguise, leading to its popularity in emotion recognition. Recently, semi …
EEG-based driving fatigue detection using a two-level learning hierarchy radial basis function
Z Ren, R Li, B Chen, H Zhang, Y Ma, C Wang… - Frontiers in …, 2021 - frontiersin.org
Electroencephalography (EEG)-based driving fatigue detection has gained increasing
attention recently due to the non-invasive, low-cost, and potable nature of the EEG …
attention recently due to the non-invasive, low-cost, and potable nature of the EEG …
A semi-supervised Laplacian extreme learning machine and feature fusion with CNN for industrial superheat identification
Y Lei, X Chen, M Min, Y Xie - Neurocomputing, 2020 - Elsevier
The superheat degree (SD) in industrial aluminum electrolysis cell is a critical index that can
maintain the energy balance, improve the current efficiency and improve production …
maintain the energy balance, improve the current efficiency and improve production …
IBoNN: Intelligent agent-based internet of medical things framework for detecting brain response from electroencephalography signal using bag-of-neural network
The inability of a patient to talk, hear, or both for any specific reason can create a worrisome
scenario because any form of reaction can deem the brain activity. In such a scenario …
scenario because any form of reaction can deem the brain activity. In such a scenario …
Robust adaptive semi-supervised classification method based on dynamic graph and self-paced learning
Despite the computers have developed rapidly in recent years, there are still many
difficulties to obtain a large number of labelled data in many practical problems, for example …
difficulties to obtain a large number of labelled data in many practical problems, for example …