{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:38:39Z","timestamp":1778258319977,"version":"3.51.4"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Pozna\u00f1 University of Technology, Institute of Automatic Control and Robotics","award":["0211\/SBAD\/0121"],"award-info":[{"award-number":["0211\/SBAD\/0121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3155873","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T20:34:42Z","timestamp":1646253282000},"page":"24985-24994","source":"Crossref","is-referenced-by-count":3,"title":["Hybrid Restricted Boltzmann Machine\u2013 Convolutional Neural Network Model for Image Recognition"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0004-9491","authenticated-orcid":false,"given":"Szymon","family":"Sobczak","sequence":"first","affiliation":[{"name":"Institute of Automatic Control and Robotics, Pozna&#x0144; University of Technology, Pozna&#x0144;, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0624-7608","authenticated-orcid":false,"given":"Rafal","family":"Kapela","sequence":"additional","affiliation":[{"name":"Institute of Automatic Control and Robotics, Pozna&#x0144; University of Technology, Pozna&#x0144;, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00444-8"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.10.006"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01247-4"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2017.01.010"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-019-00768-7"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3460319.3464816"},{"key":"ref7","volume-title":"Algorithms for Clustering Data","author":"Jain","year":"1988"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1080\/14786440109462720"},{"key":"ref9","volume-title":"Matrix Computation","author":"Golub","year":"1996"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2013.12.027"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7443.001.0001"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90014-2"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1126\/science.3755256"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog0901_7"},{"key":"ref15","volume-title":"Information Processing in Dynamical Systems: Foundations of Harmony Theory","author":"Smolensky","year":"1986"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2014.12.005"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273596"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.100.032128"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-99353-2"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489163"},{"key":"ref23","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. ICLR","author":"Kingma"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1561\/2200000089"},{"key":"ref25","article-title":"Masked autoencoders are scalable vision learners","author":"He","year":"2021","journal-title":"arXiv:2111.06377"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-021-00702-9"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0147"},{"key":"ref28","first-page":"449","article-title":"Deep Boltzmann machines","volume-title":"Proc. 12th Int. Conf. Artif. Intell. Statist.","author":"Hinton"},{"key":"ref29","first-page":"693","article-title":"Efficient learning of deep Boltzmann machines","volume":"9","author":"Salakhutdinov","year":"2010","journal-title":"J. Mach. Learn. Res. Proc. Track"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/fi12070113"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-021-01605-0"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-019-01782-8"},{"key":"ref34","article-title":"MLP-mixer: An all-MLP architecture for vision","author":"Tolstikhin","year":"2021","journal-title":"arXiv:2105.01601"},{"key":"ref35","article-title":"Towards deep learning models resistant to large perturbations","author":"Shaeiri","year":"2020","journal-title":"arXiv:2003.13370"},{"issue":"4","key":"ref36","first-page":"1","article-title":"Face recognition using local binary patterns (LBP)","volume":"13","author":"Abdur","year":"2013","journal-title":"Global J. Comput. Sci. Technol. Graph. Vis."},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2010.12.001"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1088\/1674-1056\/abd160"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2019.103195"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_32"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/66.3.605"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15552-9_54"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/0262-8856(93)90005-2"},{"key":"ref45","first-page":"215","article-title":"An analysis of single-layer networks in unsupervised feature learning","volume-title":"Proc. AISTATS","author":"Coates"},{"key":"ref46","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Simonyan"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref48","article-title":"Network in network","author":"Lin","year":"2014","journal-title":"arXiv:1312.4400"},{"issue":"1","key":"ref49","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1950.tb00463.x"},{"key":"ref51","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014","journal-title":"arXiv:1412.6572"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206537"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09724261.pdf?arnumber=9724261","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T23:49:55Z","timestamp":1705535395000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9724261\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":54,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3155873","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}