{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:43:54Z","timestamp":1780512234107,"version":"3.54.1"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T00:00:00Z","timestamp":1680825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Grant Scheme of the Ministry of Higher Education","award":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"]}]},{"name":"Fundamental Research Grant Scheme of the Ministry of Higher Education","award":["MMUI\/220021"],"award-info":[{"award-number":["MMUI\/220021"]}]},{"name":"Multimedia University Internal Research Grant","award":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"],"award-info":[{"award-number":["FRGS\/1\/2021\/ICT02\/MMU\/02\/4"]}]},{"name":"Multimedia University Internal Research Grant","award":["MMUI\/220021"],"award-info":[{"award-number":["MMUI\/220021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>With the growing trend in autonomous vehicles, accurate recognition of traffic signs has become crucial. This research focuses on the use of convolutional neural networks for traffic sign classification, specifically utilizing pre-trained models of ResNet50, DenseNet121, and VGG16. To enhance the accuracy and robustness of the model, the authors implement an ensemble learning technique with majority voting, to combine the predictions of multiple CNNs. The proposed approach was evaluated on three different traffic sign datasets: the German Traffic Sign Recognition Benchmark (GTSRB), the Belgium Traffic Sign Dataset (BTSD), and the Chinese Traffic Sign Database (TSRD). The results demonstrate the efficacy of the ensemble approach, with recognition rates of 98.84% on the GTSRB dataset, 98.33% on the BTSD dataset, and 94.55% on the TSRD dataset.<\/jats:p>","DOI":"10.3390\/jsan12020033","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:24:18Z","timestamp":1681097058000},"page":"33","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Enhanced Traffic Sign Recognition with Ensemble Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8391-8457","authenticated-orcid":false,"given":"Xin Roy","family":"Lim","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3679-8977","authenticated-orcid":false,"given":"Chin Poo","family":"Lee","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1929-7978","authenticated-orcid":false,"given":"Kian Ming","family":"Lim","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5867-9517","authenticated-orcid":false,"given":"Thian Song","family":"Ong","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17779","DOI":"10.1007\/s11042-022-12163-0","article-title":"Traffic sign recognition based on deep learning","volume":"81","author":"Zhu","year":"2022","journal-title":"Multimed. 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Appl."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/12\/2\/33\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:11:55Z","timestamp":1760123515000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/12\/2\/33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,7]]},"references-count":24,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["jsan12020033"],"URL":"https:\/\/doi.org\/10.3390\/jsan12020033","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,7]]}}}