{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:51:15Z","timestamp":1743130275191,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030461393"},{"type":"electronic","value":"9783030461409"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-46140-9_16","type":"book-chapter","created":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T07:03:10Z","timestamp":1587538990000},"page":"165-172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Peruvian Sign Language Recognition Using a Hybrid Deep Neural Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4108-2631","authenticated-orcid":false,"given":"Yuri Vladimir Huallpa","family":"Vargas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1202-474X","authenticated-orcid":false,"given":"Naysha Naydu Diaz","family":"Ccasa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-0838","authenticated-orcid":false,"given":"Lauro Enciso","family":"Rodas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,23]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","unstructured":"Akilan, T., Wu, Q.M.J., Yang, Y., Safaei, A.: Fusion of transfer learning features and its application in image classification. In: 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1\u20135, April 2017. https:\/\/doi.org\/10.1109\/CCECE.2017.7946733","DOI":"10.1109\/CCECE.2017.7946733"},{"key":"16_CR2","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv e-prints abs\/1409.0473, September 2014. https:\/\/arxiv.org\/abs\/1409.0473"},{"key":"16_CR3","doi-asserted-by":"publisher","unstructured":"Cai, M., Liu, J.: Maxout neurons for deep convolutional and LSTM neural networks in speech recognition. Speech Commun. 77, December 2015. https:\/\/doi.org\/10.1016\/j.specom.2015.12.003","DOI":"10.1016\/j.specom.2015.12.003"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. CoRR abs\/1411.4389 (2014). http:\/\/arxiv.org\/abs\/1411.4389","DOI":"10.21236\/ADA623249"},{"key":"16_CR5","unstructured":"Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 1019\u20131027. Curran Associates, Inc. (2016). http:\/\/papers.nips.cc\/paper\/6241-a-theoretically-grounded-application-of-dropout-in-recurrent-neural-networks.pdf"},{"key":"16_CR6","unstructured":"Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. In: Dasgupta, S., McAllester, D. (eds.) Proceedings of the 30th International Conference on Machine Learning, No. 3 in Proceedings of Machine Learning Research, PMLR, Atlanta, Georgia, USA, pp. 1319\u20131327, 17\u201319 June 2013. http:\/\/proceedings.mlr.press\/v28\/goodfellow13.html"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., Hinton, G.E.: Speech recognition with deep recurrent neural networks. CoRR abs\/1303.5778 (2013). http:\/\/arxiv.org\/abs\/1303.5778","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"16_CR8","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs\/1512.03385 (2015). http:\/\/arxiv.org\/abs\/1512.03385"},{"key":"16_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u201380 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"issue":"3","key":"16_CR10","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1109\/LSP.2018.2797228","volume":"25","author":"S Huang","year":"2018","unstructured":"Huang, S., Mao, C., Tao, J., Ye, Z.: A novel chinese sign language recognition method based on keyframe-centered clips. IEEE Signal Process. Lett. 25(3), 442\u2013446 (2018). https:\/\/doi.org\/10.1109\/LSP.2018.2797228","journal-title":"IEEE Signal Process. Lett."},{"key":"16_CR11","doi-asserted-by":"publisher","unstructured":"Laraba, S., Brahimi, M., Tilmanne, J., Dutoit, T.: 3D skeleton-based action recognition by representing motion capture sequences as 2D-RGB images. Comput. Animat. Virtual Worlds 28, May 2017. https:\/\/doi.org\/10.1002\/cav.1782","DOI":"10.1002\/cav.1782"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Luong, M., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. CoRR abs\/1508.04025 (2015). http:\/\/arxiv.org\/abs\/1508.04025","DOI":"10.18653\/v1\/D15-1166"},{"key":"16_CR13","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1007\/978-981-10-7299-4_15","volume-title":"Computer Vision","author":"C Mao","year":"2017","unstructured":"Mao, C., Huang, S., Li, X., Ye, Z.: Chinese sign language recognition with\u00a0sequence to sequence learning. In: Yang, J., Hu, Q., Cheng, M.-M., Wang, L., Liu, Q., Bai, X., Meng, D. (eds.) CCCV 2017, Part I. CCIS, vol. 771, pp. 180\u2013191. Springer, Singapore (2017). https:\/\/doi.org\/10.1007\/978-981-10-7299-4_15"},{"key":"16_CR14","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/978-981-10-7566-7_63","volume-title":"Intelligent Engineering Informatics","author":"S Masood","year":"2018","unstructured":"Masood, S., Srivastava, A., Thuwal, H.C., Ahmad, M.: Real-time sign language gesture (word) recognition from video sequences using CNN and RNN. In: Bhateja, V., Coello Coello, C.A., Satapathy, S.C., Pattnaik, P.K. (eds.) Intelligent Engineering Informatics. AISC, vol. 695, pp. 623\u2013632. Springer, Singapore (2018). https:\/\/doi.org\/10.1007\/978-981-10-7566-7_63"},{"key":"16_CR15","unstructured":"Smith, N., Van der Walt, S.: MPL Colormaps (2015). https:\/\/bids.github.io\/colormap\/"},{"key":"16_CR16","unstructured":"Raffel, C., Ellis, D.P.W.: Feed-forward networks with attention can solve some long-term memory problems. CoRR abs\/1512.08756 (2015). http:\/\/arxiv.org\/abs\/1512.08756"},{"key":"16_CR17","unstructured":"Ronchetti, F., Quiroga, F., Estrebou, C., Lanzarini, L., Rosete, A.: Lsa64: A dataset of argentinian sign language. In: XX II Congreso Argentino de Ciencias de la Computaci\u00f3n (CACIC) (2016)"},{"key":"16_CR18","doi-asserted-by":"publisher","unstructured":"Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, pp. 3384\u20133391, September 2008. https:\/\/doi.org\/10.1109\/IROS.2008.4650967","DOI":"10.1109\/IROS.2008.4650967"},{"key":"16_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-72038-8_3","volume-title":"Intelligent Human Computer Interaction","author":"A Sarkar","year":"2017","unstructured":"Sarkar, A., Gepperth, A., Handmann, U., Kopinski, T.: Dynamic hand gesture recognition for mobile systems using deep LSTM. In: Horain, P., Achard, C., Mallem, M. (eds.) IHCI 2017. LNCS, vol. 10688, pp. 19\u201331. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-72038-8_3"},{"key":"16_CR20","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Machi. Learn. Rese. 15, 1929\u20131958 (2014). http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html","journal-title":"J. Machi. Learn. Rese."},{"key":"16_CR21","unstructured":"Su, P., Ding, X., Zhang, Y., Miao, F., Zhao, N.: Learning to predict blood pressure with deep bidirectional LSTM network. CoRR abs\/1705.04524 (2017). http:\/\/arxiv.org\/abs\/1705.04524"},{"key":"16_CR22","unstructured":"Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M.: C3D: generic features for video analysis. CoRR abs\/1412.0767 (2014). http:\/\/arxiv.org\/abs\/1412.0767"},{"key":"16_CR23","unstructured":"Vargas, Y.V.H.: Peruvian sign language videolsp10 (2019). https:\/\/github.com\/videoLSP\/VideoLSP10"},{"key":"16_CR24","unstructured":"Xu, K., et al.: Show, attend and tell: Neural image caption generation with visual attention. CoRR abs\/1502.03044 (2015). http:\/\/arxiv.org\/abs\/1502.03044"}],"container-title":["Communications in Computer and Information Science","Information Management and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46140-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,16]],"date-time":"2021-10-16T18:26:21Z","timestamp":1634408781000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46140-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461393","9783030461409"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46140-9_16","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SIMBig","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual International Symposium on Information Management and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"simbig2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/simbig.org\/SIMBig2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"104","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}