{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:56:36Z","timestamp":1743152196739,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031337826"},{"type":"electronic","value":"9783031337833"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-33783-3_29","type":"book-chapter","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T23:02:39Z","timestamp":1686265359000},"page":"308-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine-Learning Based Estimation of\u00a0the\u00a0Bending Magnitude Sensed by\u00a0a\u00a0Fiber Optic Device"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9874-4088","authenticated-orcid":false,"given":"Luis M.","family":"Valent\u00edn-Coronado","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7299-3924","authenticated-orcid":false,"given":"Rodolfo","family":"Mart\u00ednez-Manuel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9835-5533","authenticated-orcid":false,"given":"Jonathan","family":"Esquivel-Hern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8296-6209","authenticated-orcid":false,"given":"Sophie","family":"LaRochelle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Ogundare, J.O.: Precision surveying: the principles and geomatics practice. John Wiley & Sons (2015)","DOI":"10.1002\/9781119147770"},{"issue":"8","key":"29_CR2","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1109\/50.618377","volume":"15","author":"A Kersey","year":"1997","unstructured":"Kersey, A., et al.: Fiber grating sensors. J. Lightwave Technol. 15(8), 1442\u20131463 (1997)","journal-title":"J. Lightwave Technol."},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Lee, B.H.: Interferometric fiber optic sensors. Sensors 12(3), 2467\u20132486 (2012). https:\/\/www.mdpi.com\/1424-8220\/12\/3\/2467","DOI":"10.3390\/s120302467"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Ci\u0229szczyk, S., Kisa\u0142a, P.: Inverse problem of determining periodic surface profile oscillation defects of steel materials with a fiber bragg grating sensor. Appl. Opt. 55(6), 1412\u20131420 (2016). https:\/\/opg.optica.org\/ao\/abstract.cfm?URI=ao-55-6-1412","DOI":"10.1364\/AO.55.001412"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Voulodimos, A., Doulamis, N., Bebis, G., Stathaki, T.: Recent developments in deep learning for engineering applications. Computational Intell. Neurosc. (2018)","DOI":"10.1155\/2018\/8141259"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Pasupa, K., Sunhem, W.: A comparison between shallow and deep architecture classifiers on small dataset. In: 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 1\u20136 (2016)","DOI":"10.1109\/ICITEED.2016.7863293"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Pham, C.C., Jeon, J.W.: Robust object proposals re-ranking for object detection in autonomous driving using convolutional neural networks. Signal Proces. Image Commun. 53, 110\u2013122, (2017). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0923596517300231","DOI":"10.1016\/j.image.2017.02.007"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Li, S., Deng, M., Lee, J., Sinha, A., Barbastathis, G.: Imaging through glass diffusers using densely connected convolutional networks. Optica, 5(7), 803\u2013813 (2018). https:\/\/opg.optica.org\/optica\/abstract.cfm?URI=optica-5-7-803","DOI":"10.1364\/OPTICA.5.000803"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Aisawa, S., Noguchi, K., Matsumoto, T.: Remote image classification through multimode optical fiber using a neural network. Opt. Lett. 16(9), 645\u2013647 (1991). https:\/\/opg.optica.org\/ol\/abstract.cfm?URI=ol-16-9-645","DOI":"10.1364\/OL.16.000645"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Lohani, S., Knutson, E.M., O\u2019Donnell, M., Huver, S.D., Glasser, R.T.: On the use of deep neural networks in optical communications. Appl. Opt. 57(15), 4180\u20134190 (2018). https:\/\/opg.optica.org\/ao\/abstract.cfm?URI=ao-57-15-4180","DOI":"10.1364\/AO.57.004180"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Rivenson, Y., et al.: Deep learning microscopy. Optica 4(11), 1437\u20131443 (2017). https:\/\/opg.optica.org\/optica\/abstract.cfm?URI=optica-4-11-1437","DOI":"10.1364\/OPTICA.4.001437"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Nehme, E., Weiss, L.E., Michaeli, T., Shechtman, Y.: Deep-storm: super-resolution single-molecule microscopy by deep learning. Optica 5(4), 458\u2013464 (2018). https:\/\/opg.optica.org\/optica\/abstract.cfm?URI=optica-5-4-458","DOI":"10.1364\/OPTICA.5.000458"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Corsi, A., Chang, J.H., Wang, R., Wang, L., Rusch, L.A., LaRochelle, S.: Highly elliptical core fiber with stress-induced birefringence for mode multiplexing. Opt. Lett. 45(10), 2822\u20132825 (2020). https:\/\/opg.optica.org\/ol\/abstract.cfm?URI=ol-45-10-2822","DOI":"10.1364\/OL.387751"},{"key":"29_CR14","unstructured":"Jakkula, V.: Tutorial on support vector machine (svm), School of EECS, vol. 37(2.5), p. 3. Washington State University (2006)"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Sun, S., Huang, R.: An adaptive k-nearest neighbor algorithm. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, vol.\u00a01, pp. 91\u201394 IEEE (2010)","DOI":"10.1109\/FSKD.2010.5569740"},{"key":"29_CR16","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1023\/A:1007465528199","volume":"29","author":"MG Nir Friedman","year":"1997","unstructured":"Nir Friedman, M.G., Geiger, D.: Bayesian network classifiers. Mach. Learn. 29, 131\u2013163 (1997)","journal-title":"Mach. Learn."},{"key":"29_CR17","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"B Leo","year":"2001","unstructured":"Leo, B.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"4","key":"29_CR18","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Information Process. Manag. 45(4), 427\u2013437 (2009)","journal-title":"Information Process. Manag."},{"issue":"8","key":"29_CR19","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/TKDE.2019.2912815","volume":"32","author":"T-T Wong","year":"2019","unstructured":"Wong, T.-T., Yeh, P.-Y.: Reliable accuracy estimates from k-fold cross validation. IEEE Trans. Knowl. Data Eng. 32(8), 1586\u20131594 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33783-3_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T07:10:52Z","timestamp":1689750652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33783-3_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031337826","9783031337833"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33783-3_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tepic","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","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":"21 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mcpr22023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccc.inaoep.mx\/~mcpr\/index.html","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"61","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":"30","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":"49% - 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":"2.754","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":"3.58","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}