{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:34:18Z","timestamp":1743096858550,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031774317"},{"type":"electronic","value":"9783031774324"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-77432-4_5","type":"book-chapter","created":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T08:24:09Z","timestamp":1735115049000},"page":"67-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Computer Vision for\u00a0Detecting Attentional Behaviors"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3302-2721","authenticated-orcid":false,"given":"Caio","family":"Piza","sequence":"first","affiliation":[]},{"given":"Marcos Roberto","family":"Bombacini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7902-1207","authenticated-orcid":false,"given":"Jos\u00e9","family":"Lima","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,26]]},"reference":[{"issue":"6","key":"5_CR1","doi-asserted-by":"publisher","first-page":"3017","DOI":"10.1109\/TITS.2015.2462084","volume":"16","author":"S Kaplan","year":"2015","unstructured":"Kaplan, S., Guvensan, M.A., Yavuz, A.G., Karalurt, Y.: Driver behavior analysis for safe driving: a survey. IEEE Trans. Intell. Transp. Syst. 16(6), 3017\u20133032 (2015). https:\/\/doi.org\/10.1109\/TITS.2015.2462084","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"5_CR2","unstructured":"IBGE. 161,6 milh\u00f5es de pessoas com 10 anos ou mais de idade utilizaram a internet no pa\u00eds, em 2022 (2023). https:\/\/encurtador.com.br\/nEQS1"},{"key":"5_CR3","unstructured":"Global Status Report on Road Safety 2018. World Health Organization, Geneva, Switzerland (2018). https:\/\/www.who.int\/violence_injury_prevention\/road_safety_status\/2018\/en\/. Accessed 7 Jan 2024"},{"key":"5_CR4","unstructured":"World\u00a0Health Organization. Mobile phone use: a growing problem of driver distraction. World Health Organization, Geneva, Switzerland (2011). https:\/\/www.who.int\/publications\/i\/item\/mobile-phone-use-a-growing-problem-of-driver-distraction. Accessed 7 Jan 2024"},{"key":"5_CR5","unstructured":"Organiza\u00e7\u00e3o das Na\u00e7\u00f5es Unidas (ONU). D\u00e9cada de a\u00e7\u00f5es para a seguran\u00e7a no tr\u00e2nsito 2011-2020. [s.l.]: ONU, (Nota T\u00e9cnica) (2011)"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"de\u00a0Carvalho, C.H.R., Guedes, E.P.: Balan\u00e7o da primeira d\u00e9cada de a\u00e7\u00e3o pela seguran\u00e7a no tr\u00e2nsito no Brasil e perspectivas para a segunda d\u00e9cada. Administra\u00e7\u00e3o P\u00fablica. Governo. Estado (2023). Publicado em Novembro de 2023","DOI":"10.38116\/ntdirur42-port"},{"key":"5_CR7","unstructured":"Oms - plano global - d\u00e9cada de a\u00e7\u00e3o pela seguran\u00e7a no tr\u00e2nsito 2021-2030. Technical report, Organiza\u00e7\u00e3o Mundial da Sa\u00fade (OMS) (2021). Documento T\u00e9cnico"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Masello, L., Castignani, G., Sheehan, B., Murphy, F., McDonnell, K.: On the road safety benefits of advanced driver assistance systems in different driving contexts. Transp. Res. Interdisc. Perspect. 15, 100670 (2022). ISSN 2590-1982. https:\/\/doi.org\/10.1016\/j.trip.2022.100670. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2590198222001300","DOI":"10.1016\/j.trip.2022.100670"},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1109\/ACCESS.2017.2779939","volume":"6","author":"F Karim","year":"2017","unstructured":"Karim, F.: LSTM fully convolutional networks for time series classification. IEEE Access 6, 1662\u20131669 (2017)","journal-title":"IEEE Access"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Xiao, Z., Zhiqiang, H., Geng, L., Zhang, F., Jun, W., Li, Y.: Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units. IET Intel. Transport Syst. 13(9), 1410\u20131416 (2019) https:\/\/doi.org\/10.1049\/iet-its.2018.5392. https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/abs\/10.1049\/iet-its.2018.5392","DOI":"10.1049\/iet-its.2018.5392"},{"key":"5_CR11","unstructured":"Geng, L., Hu, Z., Xiao, Z., et al.: Real-time fatigue driving recognition system based on deep learning and embedded platform. Am. Sci. Res. J. Eng. Technol. Sci. (ASRJETS) 53(1), 164\u2013175 (2019). https:\/\/asrjetsjournal.org\/index.php\/American_Scientific_Journal\/article\/view\/4735\/1665"},{"key":"5_CR12","unstructured":"Frangoul, A.: Bosch develops A.I. system in cars to detect distracted, tired drivers. CNBC (2019). https:\/\/www.cnbc.com\/2019\/12\/17\/bosch-develops-ai-system-in-cars-to-detect-distracted-tired-drivers.html"},{"key":"5_CR13","unstructured":"Keras Documentation. Mobilenet application - keras documentation (2023). https:\/\/keras.io\/api\/applications\/mobilenet\/"},{"key":"5_CR14","unstructured":"Krishnamurthy, B.: An introduction to the relu activation function. Built In (2024). https:\/\/builtin.com\/machine-learning\/relu-activation-function. Written by Bharath Krishnamurthy"},{"key":"5_CR15","unstructured":"Keras Team. Adam optimizer, 2024. URL https:\/\/keras.io\/api\/optimizers\/adam\/. Optimizer that implements the Adam algorithm. Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments. According to Kingma et al., 2014, the method is \u201ccomputationally efficient, has little memory requirement, invariant to diagonal rescaling of gradients, and is well suited for problems that are large in terms of data\/parameters\u201d"},{"key":"5_CR16","unstructured":"TensorFlow Authors. Tensorflow lite quantization specification (2024). https:\/\/www.tensorflow.org\/lite\/performance\/quantization_spec. Accessed 07 June 2024"}],"container-title":["Communications in Computer and Information Science","Optimization, Learning Algorithms and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77432-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T09:01:56Z","timestamp":1735117316000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77432-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031774317","9783031774324"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77432-4_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"26 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OL2A","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Optimization, Learning Algorithms and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tenerife","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ol2a2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ol2a.ipb.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}