[HTML][HTML] A spectral-ensemble deep random vector functional link network for passive brain–computer interface
Randomized neural networks (RNNs) have shown outstanding performance in many
different fields. The superiority of having fewer training parameters and closed-form …
different fields. The superiority of having fewer training parameters and closed-form …
Benchmarking feed-forward randomized neural networks for vessel trajectory prediction
The burgeoning scale and speed of maritime vessels present escalating challenges to
navigational safety. Perceiving the motions of vessels, identifying anomalies, and risk …
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
Efficient forecasting of ship order books holds immense significance in the maritime industry,
enabling companies to optimize their operations, allocate resources effectively, and make …
enabling companies to optimize their operations, allocate resources effectively, and make …
A benchmarking framework for eye-tracking-based vigilance prediction of vessel traffic controllers
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 …
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 …
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 …
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
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 …
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
Randomization-based neural networks have gained wide acceptance in the scientific
community owing to the simplicity of their algorithm and generalization capabilities. Random …
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
The automated detection of mind-wandering (MW) and associated attention lapses through
Electroencephalogram (EEG) signals holds significant potential for practical applications …
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
Fatigue driving is a significant cause of road traffic accidents and associated casualties.
Automatic assessment of driver drowsiness by monitoring electroencephalography (EEG) …
Automatic assessment of driver drowsiness by monitoring electroencephalography (EEG) …