{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:17:05Z","timestamp":1771262225254,"version":"3.50.1"},"reference-count":14,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great opportunity to regularly control the dynamics of the driver with transfer learning methods while driving, and to warn the driver in case of possible drowsiness and to focus their attention in order to prevent traffic accidents due to drowsiness. A classification study was carried out with the aim of detecting the drowsiness of the driver by the position of the eyelids and the presence of yawning movement using the Convolutional Neural Network (CNN) architecture. The dataset used in the study includes the face shapes of drivers of different genders and different ages while driving. Accuracy and F1-score parameters were used for experimental studies. The results achieved are 91 % accuracy for the VGG16 model and an F1-score of over 90 % for each class.<\/jats:p>","DOI":"10.2478\/acss-2022-0009","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T10:00:31Z","timestamp":1661335231000},"page":"83-88","source":"Crossref","is-referenced-by-count":2,"title":["Detection of Driver Dynamics with VGG16 Model"],"prefix":"10.2478","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1865-5983","authenticated-orcid":false,"given":"Alper","family":"Aytekin","sequence":"first","affiliation":[{"name":"Y\u0131ld\u0131z Technical University , \u0130stanbul , Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3769-0071","authenticated-orcid":false,"given":"Vasfiye","family":"Men\u00e7ik","sequence":"additional","affiliation":[{"name":"Dicle University , Diyarbak\u0131r , Turkey"}]}],"member":"374","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"2024042805530159487_j_acss-2022-0009_ref_001","doi-asserted-by":"crossref","unstructured":"[1] O. 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Zisserman, \u201cVery deep convolutional networks for large-scale image recognition\u201d, arXiv, preprint arXiv:1409.1556, 2014."},{"key":"2024042805530159487_j_acss-2022-0009_ref_011","doi-asserted-by":"crossref","unstructured":"[11] J. Gwak, A. Hirao, and M. Shino, \u201cAn investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing\u201d, Appl. Sci., vol. 10, no. 8, Apr. 2020, Art no. 2890. https:\/\/doi.org\/10.3390\/app10082890","DOI":"10.3390\/app10082890"},{"key":"2024042805530159487_j_acss-2022-0009_ref_012","doi-asserted-by":"crossref","unstructured":"[12] S. Mehta, S. Dadhich, S. Gumber, and A. J. Bhatt, \u201cReal-time driver drowsiness detection system using eye aspect ratio and eye closure ratio\u201d, in Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), Jaipur, India, Feb. 2019. https:\/\/doi.org\/10.2139\/ssrn.3356401","DOI":"10.2139\/ssrn.3356401"},{"key":"2024042805530159487_j_acss-2022-0009_ref_013","doi-asserted-by":"crossref","unstructured":"[13] Z. Kepesiova, J. Ciganek, and S. Kozak, \u201cDriver drowsiness detection using convolutional neural networks\u201d, in 2020 Cybernetics & Informatics (K&I), Velke Karlovice, Czech Republic, Mar. 2020, pp. 1\u20136. https:\/\/doi.org\/10.1109\/KI48306.2020.9039851","DOI":"10.1109\/KI48306.2020.9039851"},{"key":"2024042805530159487_j_acss-2022-0009_ref_014","doi-asserted-by":"crossref","unstructured":"[14] R. Jabbar, M. Shinoy, M. Kharbeche, K. Al-Khalifa, M. Krichen, and K. Barkaoui, \u201cDriver drowsiness detection model using convolutional neural networks techniques for android application\u201d, in Proceedings of the 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, Doha, Qatar, May 2020, pp. 2\u20135. https:\/\/doi.org\/10.1109\/ICIoT48696.2020.9089484","DOI":"10.1109\/ICIoT48696.2020.9089484"}],"container-title":["Applied Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.sciendo.com\/pdf\/10.2478\/acss-2022-0009","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,28]],"date-time":"2024-04-28T05:53:14Z","timestamp":1714283594000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciendo.com\/article\/10.2478\/acss-2022-0009"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,1]]},"references-count":14,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,8,23]]},"published-print":{"date-parts":[[2022,6,1]]}},"alternative-id":["10.2478\/acss-2022-0009"],"URL":"https:\/\/doi.org\/10.2478\/acss-2022-0009","relation":{},"ISSN":["2255-8691"],"issn-type":[{"value":"2255-8691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,1]]}}}