{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:14:35Z","timestamp":1793132075147,"version":"4.5.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s00521-020-05209-7","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T09:03:41Z","timestamp":1595235821000},"page":"3155-3168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":192,"title":["Deep CNN models-based ensemble approach to driver drowsiness detection"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7071-8323","authenticated-orcid":false,"given":"Mohit","family":"Dua","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Shakshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ritu","family":"Singla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saumya","family":"Raj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arti","family":"Jangra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,20]]},"reference":[{"key":"5209_CR1","unstructured":"National Safety Council (2020) Drivers are falling asleep behind the wheel https:\/\/www.nsc.org\/road-safety\/safety-topics\/fatigued-driving"},{"key":"5209_CR2","doi-asserted-by":"crossref","unstructured":"Grace R, Byrne VE, Bierman DM, Legrand JM, Gricourt D, Davis BK, Staszewski JJ, Carnahan B (1998) A drowsy driver detection system for heavy vehicles. In: 17th DASC. AIAA\/IEEE\/SAE. Digital avionics systems conference. Proceedings (Cat. No. 98CH36267) vol 2, pp I36\u20131. IEEE","DOI":"10.1109\/DASC.1998.739878"},{"key":"5209_CR3","doi-asserted-by":"crossref","unstructured":"Ursulescu O, Ilie B, Simion G (2018). Driver drowsiness detection based on eye analysis. In: 2018 International symposium on electronics and telecommunications (ISETC) pp 1\u20134. IEEE","DOI":"10.1109\/ISETC.2018.8583852"},{"key":"5209_CR4","doi-asserted-by":"crossref","unstructured":"Tabrizi PR, Zoroofi RA (2008). Open\/closed eye analysis for drowsiness detection. In: 2008 First workshops on image processing theory, tools and applications pp 1\u20137. IEEE","DOI":"10.1109\/IPTA.2008.4743785"},{"key":"5209_CR5","unstructured":"Dehnavi M, Attarzadeh N, Eshghi M (2011) Real time eye state recognition. In: 2011 19th Iranian conference on electrical engineering pp. 1\u20134. IEEE"},{"key":"5209_CR6","doi-asserted-by":"crossref","unstructured":"Rahman A, Sirshar M, Khan A (2015) Real time drowsiness detection using eye blink monitoring. In: 2015 National software engineering conference (NSEC) (pp 1\u20137). IEEE","DOI":"10.1109\/NSEC.2015.7396336"},{"key":"5209_CR7","doi-asserted-by":"crossref","unstructured":"Viola P, Jones M (2001). Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, vol. 1, pp I\u2013I. IEEE","DOI":"10.1109\/CVPR.2001.990517"},{"key":"5209_CR8","unstructured":"Lu H, Zhang W, Yang D (2007) Eye detection based on rectangle features and pixel-pattern-based texture features. In: 2007 International symposium on intelligent signal processing and communication systems. pp 746\u2013749. IEEE"},{"key":"5209_CR9","doi-asserted-by":"crossref","unstructured":"Bhone RA (2019). Computer vision based drowsiness detection for motorized vehicles with web push notifications. In: 2019 4th International conference on internet of things: smart innovation and usages (IoT-SIU). pp 1\u20134. IEEE","DOI":"10.1109\/IoT-SIU.2019.8777652"},{"issue":"13","key":"5209_CR10","doi-asserted-by":"publisher","first-page":"5444","DOI":"10.1109\/JSEN.2016.2566667","volume":"16","author":"Boon-Leng Lee","year":"2016","unstructured":"Lee Boon-Leng, Lee Boon-Giin, Chung Wan-Young (2016) Standalone wearable driver drowsiness detection system in a smartwatch. IEEE Sens J 16(13):5444\u20135451","journal-title":"IEEE Sens J"},{"issue":"9","key":"5209_CR11","doi-asserted-by":"publisher","first-page":"155014771773339","DOI":"10.1177\/1550147717733391","volume":"13","author":"Zhenlong Li","year":"2017","unstructured":"Li Zhenlong, Zhang Qingzhou, Zhao Xiaohua (2017) Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries. Int J Distrib Sens Netw 13(9):1550147717733391","journal-title":"Int J Distrib Sens Netw"},{"issue":"4","key":"5209_CR12","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1177\/0954411919831313","volume":"233","author":"Mohammad Mahmoodi","year":"2019","unstructured":"Mahmoodi Mohammad, Nahvi Ali (2019) Driver drowsiness detection based on classification of surface electromyography features in a driving simulator. Proc Inst Mech Engineers Part H J Eng Med 233(4):395\u2013406","journal-title":"Proc Inst Mech Engineers Part H J Eng Med"},{"issue":"4","key":"5209_CR13","doi-asserted-by":"publisher","first-page":"943","DOI":"10.3390\/s19040943","volume":"19","author":"S Arefnezhad","year":"2019","unstructured":"Arefnezhad S et al (2019) Driver drowsiness detection based on steering wheel data applying adaptive neurofuzzy feature selection. Sensors 19(4):943","journal-title":"Sensors"},{"issue":"17","key":"5209_CR14","doi-asserted-by":"publisher","first-page":"3555","DOI":"10.3390\/app9173555","volume":"9","author":"Jasper Gielen","year":"2019","unstructured":"Gielen Jasper, Aerts Jean-Marie (2019) Feature extraction and evaluation for driver drowsiness detection based on thermoregulation. Appl Sci 9(17):3555","journal-title":"Appl Sci"},{"key":"5209_CR15","unstructured":"Choi IH, Hong SK, Kim YG (2016) Real-time categorization of driver\u2019s gaze zone using the deep learning techniques. In: 2016 International conference on big data and smart computing (BigComp), pp. 143\u2013148. IEEE"},{"key":"5209_CR16","doi-asserted-by":"crossref","unstructured":"Dwivedi K, Biswaranjan K, Sethi A (2014) Drowsy driver detection using representation learning. In: 2014 IEEE International advance computing conference (IACC) pp 995\u2013999. IEEE","DOI":"10.1109\/IAdCC.2014.6779459"},{"key":"5209_CR17","doi-asserted-by":"crossref","unstructured":"Park S, Pan F, Kang S, Yoo CD (2016) Driver drowsiness detection system based on feature representation learning using various deep networks. In: Asian conference on computer vision, Springer, Cham. pp 154\u2013164","DOI":"10.1007\/978-3-319-54526-4_12"},{"key":"5209_CR18","doi-asserted-by":"crossref","unstructured":"Weng CH, Lai YH, Lai SH (2016) Driver drowsiness detection via a hierarchical temporal deep belief network. In: Asian conference on computer vision Springer, Cham, pp 117\u2013133","DOI":"10.1007\/978-3-319-54526-4_9"},{"issue":"1\u20132","key":"5209_CR19","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/S0004-3702(02)00190-X","volume":"137","author":"ZH Zhou","year":"2002","unstructured":"Zhou ZH, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137(1\u20132):239\u2013263","journal-title":"Artif Intell"},{"key":"5209_CR20","unstructured":"Alif MAR, Ahmed S, Hasan MA (2017) Isolated Bangla handwritten character recognition with convolutional neural network. In: 2017 20th International conference of computer and information technology (ICCIT), pp 1\u20136. IEEE"},{"key":"5209_CR21","doi-asserted-by":"crossref","unstructured":"Agrawal S, Das S, Valmiki M, Wandhekar S, Moona R (2017) A case for PARAM Shavak: ready-to-use and affordable supercomputing solution. In: 2017 International conference on high performance computing & simulation (HPCS), pp 396\u2013401. IEEE","DOI":"10.1109\/HPCS.2017.66"},{"key":"5209_CR22","doi-asserted-by":"publisher","unstructured":"Tian X, Chen C (2019) Modulation pattern recognition based on Resnet50 neural network. In: IEEE 2nd International conference on information communication and signal processing (ICICSP), Weihai, China, 2019, pp 34\u201338, https:\/\/doi.org\/10.1109\/icicsp48821.2019.8958555","DOI":"10.1109\/icicsp48821.2019.8958555"},{"key":"5209_CR23","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5209_CR24","doi-asserted-by":"crossref","unstructured":"Shakeel MF, Bajwa NA, Anwaar AM, Sohail A, Khan A (2019) Detecting driver drowsiness in real time through deep learning based object detection. In: International work-conference on artificial neural networks, Springer, Cham pp 283\u2013296","DOI":"10.1007\/978-3-030-20521-8_24"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05209-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05209-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05209-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T06:51:19Z","timestamp":1723272679000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05209-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,20]]},"references-count":24,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5209"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05209-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,20]]},"assertion":[{"value":"29 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The manuscript does not have any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}