Ruilin Li

Orcid: 0000-0002-1979-8767

Affiliations:
  • Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore
  • National University of Singapore


According to our database1, Ruilin Li authored at least 24 papers between 2020 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2025
Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting.
IEEE Trans. Neural Networks Learn. Syst., June, 2025

EEG-Based Cross-Dataset Driver Drowsiness Recognition With an Entropy Optimization Network.
IEEE J. Biomed. Health Informatics, March, 2025

2024
SPARK: A High-Efficiency Black-Box Domain Adaptation Framework for Source Privacy-Preserving Drowsiness Detection.
IEEE J. Biomed. Health Informatics, June, 2024

Entropy-guided robust feature domain adaptation for electroencephalogram-based cross-dataset drowsiness recognition.
Eng. Appl. Artif. Intell., 2024

A benchmarking framework for eye-tracking-based vigilance prediction of vessel traffic controllers.
Eng. Appl. Artif. Intell., 2024

TFormer: A time-frequency Transformer with batch normalization for driver fatigue recognition.
Adv. Eng. Informatics, 2024

Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Noise Elimination in Deep Random Vector Functional Link Network for Tabular Classification.
Proceedings of the International Joint Conference on Neural Networks, 2024

Heart Rate based Fatigue Recognition for Human Factors Evaluation.
Proceedings of the International Conference on Cyberworlds, 2024

2023
A spectral-ensemble deep random vector functional link network for passive brain-computer interface.
Expert Syst. Appl., October, 2023

Online dynamic ensemble deep random vector functional link neural network for forecasting.
Neural Networks, September, 2023

A decomposition-based hybrid ensemble CNN framework for driver fatigue recognition.
Inf. Sci., May, 2023

Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces.
Frontiers Comput. Neurosci., February, 2023

An enhanced ensemble deep random vector functional link network for driver fatigue recognition.
Eng. Appl. Artif. Intell., 2023

Significant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning.
Eng. Appl. Artif. Intell., 2023

Ensemble of Randomized Neural Network and Boosted Trees for Eye-Tracking-Based Driver Situation Awareness Recognition and Interpretation.
Proceedings of the Neural Information Processing - 30th International Conference, 2023

Benchmarking EEG-based Cross-dataset Driver Drowsiness Recognition with Deep Transfer Learning.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

Heart Rate Based Cross-subject Stress Recognition.
Proceedings of the International Conference on Cyberworlds, 2023

2022
Sample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics.
IEEE J. Biomed. Health Informatics, 2022

A Decomposition-Based Hybrid Ensemble CNN Framework for Improving Cross-Subject EEG Decoding Performance.
CoRR, 2022

Advanced Ensemble Deep Random Vector Functional Link for Eye-Tracking-based Situation Awareness Recognition.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022

Situation Awareness Recognition Using EEG and Eye-Tracking data: a pilot study.
Proceedings of the International Conference on Cyberworlds, 2022

2021
A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG.
CoRR, 2021

2020
EEG-based Recognition of Driver State Related to Situation Awareness Using Graph Convolutional Networks.
Proceedings of the International Conference on Cyberworlds, 2020


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