Complex networks and deep learning for EEG signal analysis
Electroencephalogram (EEG) signals acquired from brain can provide an effective
representation of the human's physiological and pathological states. Up to now, much work …
representation of the human's physiological and pathological states. Up to now, much work …
Financial market prediction under deep learning framework using auto encoder and kernel extreme learning machine
The technical indicators are highly uncertain therefore possess greater influence on the
stock market prediction. Among different techniques developed for effective prediction of the …
stock market prediction. Among different techniques developed for effective prediction of the …
UbeHealth: A personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities
Smart city advancements are driving massive transformations of healthcare, the largest
global industry. The drivers include increasing demands for ubiquitous, preventive, and …
global industry. The drivers include increasing demands for ubiquitous, preventive, and …
Stock market trend prediction using high-order information of time series
Given a financial time series such as, or any historical data in stock markets, how can we
obtain useful information from recent transaction data to predict the ups and downs at the …
obtain useful information from recent transaction data to predict the ups and downs at the …
A novel convolutional neural network framework based solar irradiance prediction method
N Dong, JF Chang, AG Wu, ZK Gao - … Journal of Electrical Power & Energy …, 2020 - Elsevier
As an important part of solar power system, photovoltaic grid-connected system and solar
thermal system, solar irradiance has the inherent characteristics of variability and …
thermal system, solar irradiance has the inherent characteristics of variability and …
Which artificial intelligence algorithm better predicts the Chinese stock market?
Unpredictable stock market factors make it difficult to predict stock index futures. Although
efforts to develop an effective prediction method have a long history, recent developments in …
efforts to develop an effective prediction method have a long history, recent developments in …
A complex network-based broad learning system for detecting driver fatigue from EEG signals
Driver fatigue detection is of great significance for guaranteeing traffic safety and further
reducing economic as well as societal loss. In this article, a novel complex network (CN) …
reducing economic as well as societal loss. In this article, a novel complex network (CN) …
Core-brain-network-based multilayer convolutional neural network for emotion recognition
Z Gao, R Li, C Ma, L Rui, X Sun - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this article, we propose a method for emotion classification based on multilayer
convolutional neural network (MCNN) and combining differential entropy (DE) and brain …
convolutional neural network (MCNN) and combining differential entropy (DE) and brain …
Relative wavelet entropy complex network for improving EEG-based fatigue driving classification
Detecting fatigue driving from electroencephalogram (EEG) signals constitutes a
challenging problem of continuing interest since fatigue driving has caused the majority of …
challenging problem of continuing interest since fatigue driving has caused the majority of …
A novel method for forecasting time series based on directed visibility graph and improved random walk
Y Hu, F Xiao - Physica A: Statistical Mechanics and its Applications, 2022 - Elsevier
Recently network-based method for forecasting time series has become a hot research
topic. Although some proposed network-based methods achieve good performance in …
topic. Although some proposed network-based methods achieve good performance in …