{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:17:03Z","timestamp":1780589823072,"version":"3.54.1"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000270","name":"UK Natural Environment Research Council\u2019s National Capability Long-term Single Centre Science Programme, Climate Linked Atlantic Sector Science","doi-asserted-by":"publisher","award":["NE\/R015953\/1"],"award-info":[{"award-number":["NE\/R015953\/1"]}],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Theme 1.3 - Biological Dynamics"},{"DOI":"10.13039\/100012338","name":"Alan Turing Institute","doi-asserted-by":"publisher","award":["EP\/N510129\/1"],"award-info":[{"award-number":["EP\/N510129\/1"]}],"id":[{"id":"10.13039\/100012338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3022242","type":"journal-article","created":{"date-parts":[[2020,9,7]],"date-time":"2020-09-07T21:34:56Z","timestamp":1599514496000},"page":"170013-170032","source":"Crossref","is-referenced-by-count":41,"title":["Collaborative Deep Learning Models to Handle Class Imbalance in FlowCam Plankton Imagery"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7008-7242","authenticated-orcid":false,"given":"Thomas","family":"Kerr","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1924-5871","authenticated-orcid":false,"given":"James R.","family":"Clark","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7795-7535","authenticated-orcid":false,"given":"Elaine S.","family":"Fileman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claire E.","family":"Widdicombe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Pugeault","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2009.12.033"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.4319\/lom.2007.5.204"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.pocean.2015.05.020"},{"key":"ref31","first-page":"1","article-title":"Snapshot ensembles: Train 1, get M for free","author":"huang","year":"2019","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1002\/lom3.10324"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1093\/plankt\/25.6.669"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780140810"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05792-3_1"},{"key":"ref34","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3354\/meps168285"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.0040027"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.icesjms.2004.03.012"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1093\/plankt\/fbn092"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2016.7533053"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/OCEANSKOBE.2018.8558836"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/BF00393019"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1023\/A:1006517211724"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACVMOT.2005.29"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1093\/plankt\/fbp094"},{"key":"ref19","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.12.035"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/277050a0"},{"key":"ref27","first-page":"57","article-title":"Improving minority class prediction using case-specific feature weights","author":"cardie","year":"1997","journal-title":"Proc 14th Int Conf Mach Learn"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5670\/oceanog.2007.63"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1093\/plankt\/6.4.637"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/OCEANSAP.2016.7485680"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-404060-1.50033-2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/48.972110"},{"key":"ref7","first-page":"67","article-title":"The video plankton recorder (VPR): Design and initial results","volume":"36","author":"davis","year":"0","journal-title":"Arch Hydrobiol Beih Ergebn Limnol"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1261605"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.4319\/lom.2007.5.195"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1093\/plankt\/13.5.1093"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/11538059_91"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref45","first-page":"2980","article-title":"Focal loss for dense object detection","volume":"42","author":"lin","year":"2018","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref47","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"arXiv 1905 11946"},{"key":"ref21","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-49430-8_15"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.07.011"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref44","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref26","article-title":"PlanktonSet 1.0: Plankton imagery data collected from F.G. Walton Smith in Straits of Florida from 2014-06-03 to 2014-06-06 and used in the 2015 National Data Science Bowl (NCEI Accession 0127422)","author":"cowen","year":"2015"},{"key":"ref43","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref25","article-title":"WHOI-plankton—A large scale fine grained visual recognition benchmark dataset for plankton classification","author":"orenstein","year":"2015","journal-title":"arXiv 1510 00745"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09187202.pdf?arnumber=9187202","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:44Z","timestamp":1639770944000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9187202\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3022242","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}