{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:21:28Z","timestamp":1766067688112,"version":"3.28.0"},"reference-count":63,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1109\/aiccsa56895.2022.10017554","type":"proceedings-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T19:11:22Z","timestamp":1674241882000},"page":"1-8","source":"Crossref","is-referenced-by-count":3,"title":["3D Deformable Protein Shapes Classification based on Triangles-Stars and Composite Deep Neural Networks"],"prefix":"10.1109","author":[{"given":"Kamel","family":"Madi","sequence":"first","affiliation":[{"name":"Umanis, Research and Innovation Levallois-Perret,France,92300"}]},{"given":"Eric","family":"Paquet","sequence":"additional","affiliation":[{"name":"National Research Council,Ontario,Canada"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEVicIVPR48672.2020.9306543"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.01.040"},{"issue":"85","key":"ref3","first-page":"2825","article-title":"Scikit-leam: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SMI.2004.1314502"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/S0923-5965(00)00020-5"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s00453-003-1050-5"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2596722"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2022.3146796"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2010.01655.x"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/SMI.2006.21"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2009.01515.x"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2011.6130444"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/978-3-642-24785-9_58","article-title":"De-formable shape retrieval by learning diffusion kernels","volume-title":"Scale Space and Var. Methods in Comput. Vision","volume":"6667","author":"Aflalo","year":"2011"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13536"},{"key":"ref15","first-page":"665","article-title":"Convolutional-recursive deep learning for 3D object classification","volume-title":"Adv. in Neural Inf. Process. Syst. 25: 26th Annu. Conf. on Neural Inf. Process. Syst. 2012.","author":"Socher"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2015.2480802"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.609"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.075"},{"issue":"1","key":"ref19","first-page":"3563","article-title":"What regularized auto-encoders learn from the data-generating distribution","volume":"15","author":"Alain","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2015.7298845"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.08.127"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2016.04.005"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/icme.2014.6890145"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2016.2605920"},{"issue":"5","key":"ref25","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1111\/cgf.12694","article-title":"Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces","volume":"34","author":"Huang","year":"2015","journal-title":"Comput. Graph. Forum"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.05.001"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.03.019"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818116"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/AICCSA47632.2019.9035320"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2015.14"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.11.015"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2011.04.017"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20844-7_6"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001414500013"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17711-8_9"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.12.029"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12127-2_16"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"article-title":"Gated graph sequence neural networks","volume-title":"n 4th International Conference on Learning Representations, ICLR 2016","author":"Li","key":"ref39"},{"key":"ref40","first-page":"1114","article-title":"Learning steady-states of iterative algorithms over graphs","volume-title":"Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmassan","volume":"80","author":"Dai"},{"volume-title":"Spectral networks and locally connected networks on graphs","year":"2014","author":"Bruna","key":"ref41"},{"article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"5th International Conference on Learning Representations, ICLR 2017","author":"Kipf","key":"ref42"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2018.2879624"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.576"},{"key":"ref45","first-page":"1145","article-title":"Deep neural networks for learning graph representations","volume-title":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence","author":"Cao"},{"key":"ref46","first-page":"5694","article-title":"Graphrnn: Generating realistic graphs with deep auto-regressive models","volume-title":"Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmassan","volume":"80","author":"You"},{"key":"ref47","first-page":"339","article-title":"Gaan: Gated attention networks for learning on large and spatiotemporal graphs","volume-title":"Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018","author":"Zhang"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/MMUL.2014.20"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2014.09.038"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/SMA.2001.923387"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/S0262-8856(03)00119-7"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1142\/S021946780300097X"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1142\/s0218654303000127"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1142\/s0218001404003228"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.01.002"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/2911996.2912035"},{"key":"ref58","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"3rd Int. Conf. on Learn. Representations","author":"Kingma"},{"key":"ref59","first-page":"25","article-title":"Protein shape retrieval contest","volume-title":"12th Eurographics Workshop on 3D Object Retrieval","author":"Langenfeld"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1004"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1134"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2015.7353481"},{"issue":"1","key":"ref63","first-page":"37","article-title":"Evaluation: From precision, recall and F-measure to ROC., informedness, markedness & correlation","volume":"2","author":"Powers","year":"2011","journal-title":"J. of Mach. Learn. Technol."}],"event":{"name":"2022 IEEE\/ACS 19th International Conference on Computer Systems and Applications (AICCSA)","start":{"date-parts":[[2022,12,5]]},"location":"Abu Dhabi, United Arab Emirates","end":{"date-parts":[[2022,12,8]]}},"container-title":["2022 IEEE\/ACS 19th International Conference on Computer Systems and Applications (AICCSA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10017434\/10017304\/10017554.pdf?arnumber=10017554","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T15:08:27Z","timestamp":1709392107000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10017554\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/aiccsa56895.2022.10017554","relation":{},"subject":[],"published":{"date-parts":[[2022,12]]}}}