[HTML][HTML] A spectral-ensemble deep random vector functional link network for passive brain–computer interface

R Li, R Gao, PN Suganthan, J Cui, O Sourina… - Expert Systems with …, 2023 - Elsevier
Randomized neural networks (RNNs) have shown outstanding performance in many
different fields. The superiority of having fewer training parameters and closed-form …

Benchmarking feed-forward randomized neural networks for vessel trajectory prediction

R Cheng, M Liang, H Li, KF Yuen - Computers and Electrical Engineering, 2024 - Elsevier
The burgeoning scale and speed of maritime vessels present escalating challenges to
navigational safety. Perceiving the motions of vessels, identifying anomalies, and risk …

Ship order book forecasting by an ensemble deep parsimonious random vector functional link network

R Cheng, R Gao, KF Yuen - Engineering Applications of Artificial …, 2024 - Elsevier
Efficient forecasting of ship order books holds immense significance in the maritime industry,
enabling companies to optimize their operations, allocate resources effectively, and make …

A benchmarking framework for eye-tracking-based vigilance prediction of vessel traffic controllers

Z Li, R Li, L Yuan, J Cui, F Li - Engineering Applications of Artificial …, 2024 - Elsevier
Abstract Vessel Traffic Controllers (VTCs) play a crucial role in ensuring safe navigation by
maintaining a high level of vigilance. Eye-tracking has been identified as one of the most …

[HTML][HTML] Bayesian learning of feature spaces for multitask regression

C Sevilla-Salcedo, A Gallardo-Antolín… - Neural Networks, 2024 - Elsevier
This paper introduces a novel approach to learn multi-task regression models with
constrained architecture complexity. The proposed model, named RFF-BLR, consists of a …

Real-time EEG-based Driver Drowsiness Detection Based on Convolutional Neural Network With Gumbel-Softmax Trick

W Feng, X Wang, J Xie, W Liu, Y Qiao… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
Nowadays, severe traffic accidents attributed to driver drowsiness have become increasingly
frequent, prompting a widespread concern among researchers in electroencephalogram …

Local core expanding-based label diffusion and local deep embedding for fast community detection algorithm in social networks

A Bouyer, P Shahgholi, B Arasteh… - Computers and Electrical …, 2024 - Elsevier
Community detection is a key task in social network analysis, as it reveals the underlying
structure and function of the network. Various global and local techniques exist for …

Parallel ensemble of a randomization-based online sequential neural network for classification problems using a frequency criterion

E Gelvez-Almeida, RJ Barrientos, K Vilches-Ponce… - Scientific Reports, 2024 - nature.com
Randomization-based neural networks have gained wide acceptance in the scientific
community owing to the simplicity of their algorithm and generalization capabilities. Random …

MSSTNet: A multi-stream time-distributed spatio-temporal deep learning model to detect mind wandering from electroencephalogram signals

S Pain, S Chatterjee, M Sarma, D Samanta - Computers and Electrical …, 2025 - Elsevier
The automated detection of mind-wandering (MW) and associated attention lapses through
Electroencephalogram (EEG) signals holds significant potential for practical applications …

TFAC-Net: A Temporal-Frequential Attentional Convolutional Network for Driver Drowsiness Recognition With Single-Channel EEG

P Gong, P Wang, Y Zhou, X Wen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Fatigue driving is a significant cause of road traffic accidents and associated casualties.
Automatic assessment of driver drowsiness by monitoring electroencephalography (EEG) …