An enhanced ensemble deep random vector functional link network for driver fatigue recognition
<p>This work investigated the use of an ensemble deep random vector functional link (edRVFL) network for electroencephalogram (EEG)-based driver fatigue recognition. Against the low feature learning capability of the edRVFL network from raw EEG signals, two strategies were exploited in this wo...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
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| _version_ | 1864513529422282752 |
|---|---|
| author | Ruilin Li (5627456) |
| author2 | Ruobin Gao (16003195) Liqiang Yuan (11253801) P.N. Suganthan (16518528) Lipo Wang (17185534) Olga Sourina (17823674) |
| author2_role | author author author author author |
| author_facet | Ruilin Li (5627456) Ruobin Gao (16003195) Liqiang Yuan (11253801) P.N. Suganthan (16518528) Lipo Wang (17185534) Olga Sourina (17823674) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ruilin Li (5627456) Ruobin Gao (16003195) Liqiang Yuan (11253801) P.N. Suganthan (16518528) Lipo Wang (17185534) Olga Sourina (17823674) |
| dc.date.none.fl_str_mv | 2023-08-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.engappai.2023.106237 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_enhanced_ensemble_deep_random_vector_functional_link_network_for_driver_fatigue_recognition/25038503 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Electroencephalogram (EEG) Ensemble deep random vector functional link (edRVFL) Feature selection Dynamic ensemble Cross-subject driver fatigue recognition |
| dc.title.none.fl_str_mv | An enhanced ensemble deep random vector functional link network for driver fatigue recognition |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>This work investigated the use of an ensemble deep random vector functional link (edRVFL) network for electroencephalogram (EEG)-based driver fatigue recognition. Against the low feature learning capability of the edRVFL network from raw EEG signals, two strategies were exploited in this work. Specifically, the first one was to exploit the advantages of the feature extractor module in CNNs, i.e., use CNN features as the input of the edRVFL network. The second one was to improve the feature learning capability of the edRVFL network. An enhanced edRFVL network named FGloWD-edRVFL was proposed, in which four enhancements were implemented, including random forest-based Feature selection, Global output layer, Weighting and entropy-based Dynamic ensemble. The proposed FGloWD-edRVFL network was evaluated on the challenging cross-subject driver fatigue recognition tasks. The results indicated that the proposed model could boost the recognition performance, significantly outperforming all strong baselines. The step-wise analysis further demonstrated the effectiveness of the proposed enhancements in the edRVFL network.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2023.106237" target="_blank">https://dx.doi.org/10.1016/j.engappai.2023.106237</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f7bc2bd61a92506dcb5dca2214e97fd0 |
| identifier_str_mv | 10.1016/j.engappai.2023.106237 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25038503 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An enhanced ensemble deep random vector functional link network for driver fatigue recognitionRuilin Li (5627456)Ruobin Gao (16003195)Liqiang Yuan (11253801)P.N. Suganthan (16518528)Lipo Wang (17185534)Olga Sourina (17823674)EngineeringBiomedical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceMachine learningElectroencephalogram (EEG)Ensemble deep random vector functional link (edRVFL)Feature selectionDynamic ensembleCross-subject driver fatigue recognition<p>This work investigated the use of an ensemble deep random vector functional link (edRVFL) network for electroencephalogram (EEG)-based driver fatigue recognition. Against the low feature learning capability of the edRVFL network from raw EEG signals, two strategies were exploited in this work. Specifically, the first one was to exploit the advantages of the feature extractor module in CNNs, i.e., use CNN features as the input of the edRVFL network. The second one was to improve the feature learning capability of the edRVFL network. An enhanced edRFVL network named FGloWD-edRVFL was proposed, in which four enhancements were implemented, including random forest-based Feature selection, Global output layer, Weighting and entropy-based Dynamic ensemble. The proposed FGloWD-edRVFL network was evaluated on the challenging cross-subject driver fatigue recognition tasks. The results indicated that the proposed model could boost the recognition performance, significantly outperforming all strong baselines. The step-wise analysis further demonstrated the effectiveness of the proposed enhancements in the edRVFL network.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2023.106237" target="_blank">https://dx.doi.org/10.1016/j.engappai.2023.106237</a></p>2023-08-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.engappai.2023.106237https://figshare.com/articles/journal_contribution/An_enhanced_ensemble_deep_random_vector_functional_link_network_for_driver_fatigue_recognition/25038503CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/250385032023-08-01T00:00:00Z |
| spellingShingle | An enhanced ensemble deep random vector functional link network for driver fatigue recognition Ruilin Li (5627456) Engineering Biomedical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Electroencephalogram (EEG) Ensemble deep random vector functional link (edRVFL) Feature selection Dynamic ensemble Cross-subject driver fatigue recognition |
| status_str | publishedVersion |
| title | An enhanced ensemble deep random vector functional link network for driver fatigue recognition |
| title_full | An enhanced ensemble deep random vector functional link network for driver fatigue recognition |
| title_fullStr | An enhanced ensemble deep random vector functional link network for driver fatigue recognition |
| title_full_unstemmed | An enhanced ensemble deep random vector functional link network for driver fatigue recognition |
| title_short | An enhanced ensemble deep random vector functional link network for driver fatigue recognition |
| title_sort | An enhanced ensemble deep random vector functional link network for driver fatigue recognition |
| topic | Engineering Biomedical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Electroencephalogram (EEG) Ensemble deep random vector functional link (edRVFL) Feature selection Dynamic ensemble Cross-subject driver fatigue recognition |