{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:28:08Z","timestamp":1742945288959,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031505799"},{"type":"electronic","value":"9783031505805"}],"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\/978-3-031-50580-5_8","type":"book-chapter","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T07:02:40Z","timestamp":1708412560000},"page":"84-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A CNN-Based Algorithm with an Optimized Attention Mechanism for Sign Language Gesture Recognition"],"prefix":"10.1007","author":[{"given":"Kai","family":"Yang","sequence":"first","affiliation":[]},{"given":"Zhiwei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yuqi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Naihe","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shengwei","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: Unified, real-time object detection. In: IEEE CVPR2016 Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788. IEEE Computer Society Press, Washington DC (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"8_CR2","volume":"2024","author":"P Wang","year":"2021","unstructured":"Wang, P., Huang, H., Wang, M., et al.: YOLOv5s-FCG: an improved YOLOv5 method for inspecting riders\u2019 helmet wearing. J. Phys: Conf. Ser. 2024, 012059 (2021)","journal-title":"J. Phys: Conf. Ser."},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., et al.: CBAM: convolutional block attention module. In: Proceedings of the 15th European Conference on Computer Vision, Munich, 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"23","key":"8_CR4","doi-asserted-by":"publisher","first-page":"12482","DOI":"10.3390\/app122312482","volume":"12","author":"R Zhu","year":"2022","unstructured":"Zhu, R., Huang, X., Huang, X., Li, D., Yang, Q.: An on-site-based opportunistic routing protocol for scalable and energy-efficient underwater acoustic sensor networks. Appl. Sci. 12(23), 12482 (2022)","journal-title":"Appl. Sci."},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Berman, M., Triki, A.R., Blaschiko, M.B.: The Lovasz-Softmax Loss: a tractable surrogate for optimizing the intersection-over-union measure in neural networks. In: Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4413\u20134421 (2018)","DOI":"10.1109\/CVPR.2018.00464"},{"key":"8_CR6","doi-asserted-by":"publisher","first-page":"2187","DOI":"10.1007\/s13369-021-06167-5","volume":"47","author":"A Boukdir","year":"2022","unstructured":"Boukdir, A., Benaddy, M., Ellahyani, A., et al.: Isolated video-based Arabic sign language recognition using convolutional and recursive neural networks. Arab. J. Sci. Eng. 47, 2187\u20132199 (2022)","journal-title":"Arab. J. Sci. Eng."},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Oz, C., Leu, M.c.: American Sign Language word recognition with a sensory glove using artificial neural networks. Eng. Appl. Artif. Intell. 24(7), 1204\u20131213 (2011)","DOI":"10.1016\/j.engappai.2011.06.015"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Camgoz, N.c., Koller, O., Hadfield, S., et al.: Sign language transformers: joint end-to-end sign language recognition and translation. In: Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10020\u201310030 (2020)","DOI":"10.1109\/CVPR42600.2020.01004"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Jin, X., Lan, C.L., Zeng, W.J., et al.: Style normalization and restitution for generalizable person re-identification. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3140\u20133149. IEEE, Seattle, WA, USA (2020)","DOI":"10.1109\/CVPR42600.2020.00321"},{"key":"8_CR10","unstructured":"Redmon, J., Farhadi, A.: YOLOv3; an incremental improvement. arXiv: 1804.02767 (2018)"},{"issue":"6","key":"8_CR11","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"8_CR12","first-page":"249","volume":"57","author":"XJ Guo","year":"2021","unstructured":"Guo, X.J., Sui, H.D.: Application of improved YOLOv3 in foreign object debris target detection on airfield pavement. Comput. Eng. Appl. 57(8), 249\u2013255 (2021)","journal-title":"Comput. Eng. Appl."},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"8126","DOI":"10.1609\/aaai.v33i01.33018126","volume":"33","author":"HQ Chao","year":"2019","unstructured":"Chao, H.Q., He, Y.W., Zhang, J.P., et al.: Gait set: regarding gait as a set for cross-view gait recognition. Proceedings of the AAAI Conference on Artificial Intelligence 33, 8126\u20138133 (2019)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"12","key":"8_CR14","doi-asserted-by":"publisher","first-page":"11062","DOI":"10.1609\/aaai.v35i12.17320","volume":"35","author":"HL Zheng","year":"2021","unstructured":"Zheng, H.L., Wu, Y.J., Deng, L., et al.: Going deeper with directly-trained larger spiking neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35(12), 11062\u201311070 (2021)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Guo, D., Zhou, W.G., Wang, M., et al.: Hierarchical LSTM for sign language translation. In: Proceedings of the 32 ND AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence Conference and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence, pp. 6845\u20136852 (2018)","DOI":"10.1609\/aaai.v32i1.12235"},{"key":"8_CR16","unstructured":"Yu, S.Q., Tan, D.L., Tan, T.N.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition (ICPR'06), pp. 44\u2013444. IEEE, Hong Kong, China (2006)"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Camgoz, N.C., Hadfield, S., Koller, O., et al.: Neural sign language translation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7784\u20137793. IEEE Computer Society, Piscataway, NJ (2018)","DOI":"10.1109\/CVPR.2018.00812"},{"issue":"7","key":"8_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103280","volume":"80","author":"SJ Zhang","year":"2021","unstructured":"Zhang, S.J., Zhang, Q.: Sign language recognition based on global-local attention. J. Vis. Commun. Image Represent. 80(7), 103280 (2021)","journal-title":"J. Vis. Commun. Image Represent."},{"issue":"10","key":"8_CR19","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.3390\/electronics11101614","volume":"11","author":"Z Ren","year":"2022","unstructured":"Ren, Z., Zhang, Y., Wang, S.: A hybrid framework for lung cancer classification. Electronics 11(10), 1614 (2022). May","journal-title":"Electronics"},{"key":"8_CR20","unstructured":"Wang, W., Pei, Y., Wang, S.H., Gorrz, J.M., Zhang, Y.D.: PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN. Biocell"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Multimedia Technology and Enhanced Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50580-5_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T22:27:36Z","timestamp":1731364056000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50580-5_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031505799","9783031505805"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50580-5_8","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICMTEL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Technology and Enhanced Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leicester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icmtel2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icmtel.eai-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy Plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"285","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":"121","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":"0","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":"42% - 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":"3.1","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":"6.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","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)"}}]}}