{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T01:17:15Z","timestamp":1743038235002,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030891879"},{"type":"electronic","value":"9783030891886"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-89188-6_14","type":"book-chapter","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T22:02:23Z","timestamp":1635112943000},"page":"183-196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Prediction of Hip Joint Center from X-ray Images Using Generalized Regularized Extreme Learning Machine and Globalized Bounded Nelder-Mead Strategy"],"prefix":"10.1007","author":[{"given":"Fuchang","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghui","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiantao","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"issue":"4","key":"14_CR1","first-page":"479","volume":"76","author":"MD Schofer","year":"2010","unstructured":"Schofer, M.D., Pressel, T., Heyse, T.J., Schmitt, J., Boudriot, U.: Radiological determination of the anatomic hip centre from pelvic landmarks. Acta Orthop. Belg. 76(4), 479\u2013485 (2010)","journal-title":"Acta Orthop. Belg."},{"key":"14_CR2","doi-asserted-by":"publisher","unstructured":"Myers, C.A., Huff, D.N., Mason, J.B., Rullkoetter, P.J.: Effect of intraoperative treatment options on hip joint stability following total hip arthroplasty. J. Orthop. Res. (2021). https:\/\/doi.org\/10.1002\/jor.25055","DOI":"10.1002\/jor.25055"},{"issue":"7","key":"14_CR3","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1302\/2046-3758.97.BJR-2019-0260.R2","volume":"9","author":"S Kawahara","year":"2020","unstructured":"Kawahara, S., et al.: Digitalized analyses of intraoperative acetabular component position using image-matching technique in total hip arthroplasty. Bone Joint Res. 9(7), 360\u2013367 (2020)","journal-title":"Bone Joint Res."},{"issue":"1","key":"14_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.arth.2018.10.035","volume":"34","author":"LD Dorr","year":"2019","unstructured":"Dorr, L.D., Callaghan, J.J.: Death of the Lewinnek \u201cSafe Zone.\u201d J. Arthroplasty 34(1), 1\u20132 (2019)","journal-title":"J. Arthroplasty"},{"key":"14_CR5","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1097\/00003086-198007000-00019","volume":"150","author":"K Mose","year":"1980","unstructured":"Mose, K.: Methods of measuring in Legg-Calv\u00e9-Perthes disease with special regard to the prognosis. Clin. Orthop. Relat. Res. 150, 103\u2013109 (1980)","journal-title":"Clin. Orthop. Relat. Res."},{"issue":"6","key":"14_CR6","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1097\/BPO.0000000000000281","volume":"35","author":"AV Cuomo","year":"2015","unstructured":"Cuomo, A.V., Fedorak, G.T., Moseley, C.F.: A practical approach to determining the center of the femoral head in subluxated and dislocated hips. J. Pediatr. Orthop. 35(6), 556\u2013560 (2015)","journal-title":"J. Pediatr. Orthop."},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Adewuyi, A., Levy, E.T., Wells, J., Chhabra, A., Fey, N.P.: Kinematic simulations of static radiographs provides discriminating features of multiple hip pathologies. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4992\u20134995. IEEE (2020)","DOI":"10.1109\/EMBC44109.2020.9176846"},{"issue":"6","key":"14_CR8","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1016\/j.jbiomech.2005.02.008","volume":"39","author":"V Camomilla","year":"2006","unstructured":"Camomilla, V., Cereatti, A., Vannozzi, G., Cappozzo, A.: An optimized protocol for hip joint centre determination using the functional method. J. Biomech. 39(6), 1096\u20131106 (2006)","journal-title":"J. Biomech."},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Bennett, H.J., Valenzuela, K.A., Fleenor, K., Weinhandl, J.T.: A normative database of hip and knee joint biomechanics during dynamic tasks using four functional methods with three functional calibration tasks. J. Biomech. Eng. 142(4), 041011 (2020)","DOI":"10.1115\/1.4044503"},{"issue":"3","key":"14_CR10","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/S0021-9290(03)00288-4","volume":"37","author":"SJ Piazza","year":"2004","unstructured":"Piazza, S.J., Erdemir, A., Okita, N., Cavanagh, P.R.: Assessment of the functional method of hip joint center location subject to reduced range of hip motion. J. Biomech. 37(3), 349\u2013356 (2004)","journal-title":"J. Biomech."},{"issue":"10","key":"14_CR11","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1002\/jor.21426","volume":"29","author":"MO Heller","year":"2011","unstructured":"Heller, M.O., Kratzenstein, S., Ehrig, R.M., Wassilew, G., Duda, G.N., Taylor, W.R.: The weighted optimal common shape technique improves identification of the hip joint center of rotation in vivo. J. Orthop. Res. 29(10), 1470\u20131475 (2011)","journal-title":"J. Orthop. Res."},{"issue":"3","key":"14_CR12","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.arth.2005.10.021","volume":"21","author":"SP Krishnan","year":"2006","unstructured":"Krishnan, S.P., Carrington, R.W., Mohiyaddin, S., Garlick, N.: Common misconceptions of normal hip joint relations on pelvic radiographs. J. Arthroplasty 21(3), 409\u2013412 (2006)","journal-title":"J. Arthroplasty"},{"issue":"6","key":"14_CR13","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.aott.2017.09.004","volume":"51","author":"H Bombaci","year":"2017","unstructured":"Bombaci, H., Simsek, B., Soyarslan, M., Murat Yildirim, M.: Determination of the hip rotation centre from landmarks in pelvic radiograph. Acta Orthop. Traumatol. Turc. 51(6), 470\u2013473 (2017)","journal-title":"Acta Orthop. Traumatol. Turc."},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Wang, L., Ma, L., Li, Y., Niu, K., He, Z.: A DCNN system based on an iterative method for automatic landmark detection in cephalometric X-ray images. Biomed. Signal Process. Control 68, 102757 (2021)","DOI":"10.1016\/j.bspc.2021.102757"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Juneja, M., et al.: A review on cephalometric landmark detection techniques. Biomed. Signal Process. Control 66, 102486 (2021)","DOI":"10.1016\/j.bspc.2021.102486"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Abdel-Basset, M., Mohamed, R., Mirjalili, S.: A novel whale optimization algorithm integrated with Nelder\u2013Mead simplex for multi-objective optimization problems. Knowl.-Based Syst. 212, 106619 (2021)","DOI":"10.1016\/j.knosys.2020.106619"},{"key":"14_CR17","doi-asserted-by":"publisher","first-page":"1522","DOI":"10.1016\/j.neucom.2017.09.090","volume":"275","author":"FK Inaba","year":"2018","unstructured":"Inaba, F.K., Salles, E.O.T., Perron, S., Caporossi, G.: DGR-ELM: distributed generalized regularize ELM for classification. Neurocomputing 275, 1522\u20131530 (2018)","journal-title":"Neurocomputing"},{"issue":"23\u201326","key":"14_CR18","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1016\/j.compstruc.2004.03.072","volume":"82","author":"MA Luersen","year":"2004","unstructured":"Luersen, M.A., Le Riche, R.: Globalized Nelder-Mead method for engineering optimization. Comput. Struct. 82(23\u201326), 2251\u20132260 (2004)","journal-title":"Comput. Struct."},{"issue":"1","key":"14_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2010","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1\u2013122 (2010)","journal-title":"Found. Trends Mach. Learn."},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Huang, G.B., Zhou, H.M., Ding, X.J., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B-Cybern. 42(2), 513\u2013529 (2012)","DOI":"10.1109\/TSMCB.2011.2168604"},{"issue":"1\u20133","key":"14_CR21","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133), 489\u2013501 (2006)","journal-title":"Neurocomputing"},{"issue":"17","key":"14_CR22","doi-asserted-by":"publisher","first-page":"3716","DOI":"10.1016\/j.neucom.2011.06.013","volume":"74","author":"JM Mart\u00ednez-Mart\u00ednez","year":"2011","unstructured":"Mart\u00ednez-Mart\u00ednez, J.M., Escandell-Montero, P., Soria-Olivas, E., Mart\u00edn-Guerrero, J.D., Magdalena-Benedito, R., G\u00f3mez-Sanchis, J.: Regularized extreme learning machine for regression problems. Neurocomputing 74(17), 3716\u20133721 (2011)","journal-title":"Neurocomputing"},{"key":"14_CR23","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.neucom.2015.01.097","volume":"174","author":"ZX Xu","year":"2016","unstructured":"Xu, Z.X., Yao, M., Wu, Z.H., Dai, W.H.: Incremental regularized extreme learning machine and it\u2019s enhancement. Neurocomputing 174, 134\u2013142 (2016)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2021: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89188-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T13:20:34Z","timestamp":1648646434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89188-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030891879","9783030891886"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89188-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2021","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":"382","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":"93","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":"28","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":"24% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}