{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:26:25Z","timestamp":1783034785751,"version":"3.54.6"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09863-0","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T12:26:46Z","timestamp":1767011206000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A deep convolutional neural network-based multi-label classification algorithm for massive heterogeneous data"],"prefix":"10.1007","volume":"28","author":[{"given":"Yonghao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0753-9226","authenticated-orcid":false,"given":"Yali","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"issue":"12","key":"9863_CR1","doi-asserted-by":"publisher","first-page":"2351008","DOI":"10.1142\/S0218001423510084","volume":"37","author":"C Fan","year":"2023","unstructured":"Fan C, Wang B, Wang Z. Enhanced graph neural network with multi-task learning and data augmentation for semi-supervised node classification. Int J Pattern Recognit Artif Intell. 2023;37(12):2351008. https:\/\/doi.org\/10.1142\/S021800142351.","journal-title":"Int J Pattern Recognit Artif Intell"},{"issue":"2","key":"9863_CR2","doi-asserted-by":"publisher","first-page":"413","DOI":"10.48550\/arXiv.2003.04651","volume":"40","author":"M Schelling","year":"2021","unstructured":"Schelling M, Hermosilla P, V\u00e1zquez P-P, Ropinski T. Enabling viewpoint learning through dynamic label generation. Comput Graphics Forum. 2021;40(2):413\u201323. https:\/\/doi.org\/10.48550\/arXiv.2003.04651.","journal-title":"Comput Graphics Forum"},{"issue":"3","key":"9863_CR3","doi-asserted-by":"publisher","first-page":"637","DOI":"10.3233\/IDA-205720","volume":"26","author":"BS Neves Oliveira","year":"2022","unstructured":"Neves Oliveira BS, De Oliveira AF, De Lira VM, Linhares CDST, Antonio FDMJ. Held: hierarchical entity-label disambiguation in named entity recognition task using deep learning. Intell Data Anal. 2022;26(3):637\u201357. https:\/\/doi.org\/10.3233\/IDA-205720.","journal-title":"Intell Data Anal"},{"issue":"9","key":"9863_CR4","doi-asserted-by":"publisher","first-page":"22501591","DOI":"10.1142\/S0218126622501596","volume":"31","author":"M Assefa","year":"2022","unstructured":"Assefa M, Jiang W, Yilma G, et al. Self-supervised multi-label transformation prediction for video representation learning. J Circuits Syst Computers. 2022;31(9):22501591\u20132250159. https:\/\/doi.org\/10.1142\/S0218126622501596.","journal-title":"J Circuits Syst Computers"},{"issue":"6","key":"9863_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijar.2022.02.002","volume":"145","author":"RRN Khemchandani","year":"2022","unstructured":"Khemchandani RRN, Jain S. Multi-label learning via minimax probability machine. Int J Approximate Reasoning. 2022;145(6):1\u201317. https:\/\/doi.org\/10.1016\/j.ijar.2022.02.002.","journal-title":"Int J Approximate Reasoning"},{"issue":"13","key":"9863_CR6","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.neucom.2021.07.057","volume":"464","author":"ST Aroyehun","year":"2021","unstructured":"Aroyehun ST, Angel J, Majumder N, Gelbukh A, Hussain A. Leveraging label hierarchy using transfer and multi-task learning: a case study on patent classification. Neurocomputing. 2021;464(13):421\u201331. https:\/\/doi.org\/10.1016\/j.neucom.2021.07.057.","journal-title":"Neurocomputing"},{"issue":"1","key":"9863_CR7","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/JSEN.2021.3127322","volume":"22","author":"LDS Nolasco","year":"2022","unstructured":"Nolasco LDS, Lazzaretti AE, Mulinari BM. Deepdfml-nilm: a new cnn-based architecture for detection, feature extraction and multi-label classification in nilm signals. IEEE Sens J. 2022;22(1):501\u20139. https:\/\/doi.org\/10.1109\/JSEN.2021.3127322.","journal-title":"IEEE Sens J"},{"issue":"7","key":"9863_CR8","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.knosys.2023.110548","volume":"271","author":"D Paul","year":"2023","unstructured":"Paul D, Bardhan S, Saha S, Mathew J. Ml-knockoffgan: deep online feature selection for multi-label learning. Knowl Based Syst. 2023;271(7):11\u2013112. https:\/\/doi.org\/10.1016\/j.knosys.2023.110548.","journal-title":"Knowl Based Syst"},{"key":"9863_CR9","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.neucom.2021.02.032","volume":"442","author":"M Roseberry","year":"2021","unstructured":"Roseberry M, Krawczyk B, Djenouri Y, Cano A. Self-adjusting k nearest neighbors for continual learning from multi-label drifting data streams. Neurocomputing. 2021;442:10\u201325. https:\/\/doi.org\/10.1016\/j.neucom.2021.02.032.","journal-title":"Neurocomputing"},{"key":"9863_CR10","doi-asserted-by":"publisher","unstructured":"Wang M, Yu HTY, Min F. Noise label learning through label confidence statistical inference. Knowl Based Syst. 2021;227(9). https:\/\/doi.org\/10.1016\/j.knosys.2021.107234. 107234.1-107234.15.","DOI":"10.1016\/j.knosys.2021.107234"},{"issue":"5","key":"9863_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3502729","volume":"16","author":"S Singh","year":"2022","unstructured":"Singh S, Chouzenoux E, Chierchia G, Majumdar A. Multi-label deep convolutional transform learning for non-intrusive load monitoring. ACM Trans Knowl Discovery Data. 2022;16(5):1\u20136. https:\/\/doi.org\/10.1145\/3502729.","journal-title":"ACM Trans Knowl Discovery Data"},{"key":"9863_CR12","doi-asserted-by":"publisher","first-page":"3294","DOI":"10.1016\/j.procs.2025.04.587","volume":"258","author":"H Kumawat","year":"2025","unstructured":"Kumawat H, Sharan A, Verma S. Impact analysis of text representation on biomedical Multi-Label text alassification with deep learning. Procedia Comput Sci. 2025;258:3294\u2013304. https:\/\/doi.org\/10.1016\/j.procs.2025.04.587.","journal-title":"Procedia Comput Sci"},{"issue":"6","key":"9863_CR13","doi-asserted-by":"publisher","first-page":"3443","DOI":"10.1007\/s10994-023-06440-8","volume":"113","author":"W Kuang","year":"2024","unstructured":"Kuang W, Li Z. Multi-label image classification with multi-layered multi-perspective dynamic semantic representation. Mach Learn. 2024;113(6):3443\u201361. https:\/\/doi.org\/10.1007\/s10994-023-06440-8.","journal-title":"Mach Learn"},{"key":"9863_CR14","doi-asserted-by":"publisher","unstructured":"Hssayni EH, Joudar NE, Ettaouil M. (2022). A deep learning framework for time series classification using normal cloud representation and convolutional neural network optimization. Computational intelligence, 38(6):2056\u20132074. https:\/\/doi.org\/10.1111\/coin.12556","DOI":"10.1111\/coin.12556"},{"issue":"9","key":"9863_CR15","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1109\/TC.2021.3062227","volume":"70","author":"S Disabato","year":"2021","unstructured":"Disabato S, Roveri M, Alippi C. Distributed deep convolutional neural networks for the internet-of-things. IEEE Trans Comput. 2021;70(9):1239\u201352. https:\/\/doi.org\/10.1109\/TC.2021.3062227.","journal-title":"IEEE Trans Comput"},{"issue":"04","key":"9863_CR16","doi-asserted-by":"publisher","first-page":"437","DOI":"10.3969\/j.issn.1671-7775.2023.04.010","volume":"44","author":"S Wang Ying","year":"2023","unstructured":"Wang Ying S, Yang Q, Ershuai, et al. Underwater jet flow pattern recognition based on deep learning. J Jiangsu Univ (Natural Science). 2023;44(04):437\u201343. https:\/\/doi.org\/10.3969\/j.issn.1671-7775.2023.04.010.","journal-title":"J Jiangsu Univ (Natural Science)"},{"issue":"3","key":"9863_CR17","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1049\/cvi2.12019","volume":"15","author":"S Hayat","year":"2021","unstructured":"Hayat S, Kun S, Shahzad S, Suwansrikham P, Mateen M, Yu Y. Entropy information-based heterogeneous deep selective fused features using deep convolutional neural network for sketch recognition. IET Comput Vision. 2021;15(3):165\u201380. https:\/\/doi.org\/10.1049\/cvi2.12019.","journal-title":"IET Comput Vision"},{"issue":"4","key":"9863_CR18","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1007\/s10845-020-01710-x","volume":"33","author":"S Jain","year":"2022","unstructured":"Jain S, Seth G, Paruthi A, et al. Synthetic data augmentation for surface defect detection and classification using deep learning. J Intell Manuf. 2022;33(4):1007\u201320. https:\/\/doi.org\/10.1007\/s10845-020-01710-x.","journal-title":"J Intell Manuf"},{"issue":"11","key":"9863_CR19","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1306\/100212121019","volume":"106","author":"TT Tran","year":"2022","unstructured":"Tran TT, Payenberg THD, Jian FX. Deep convolutional neural networks for generating grain-size logs from core photographs. AAPG Bull. 2022;106(11):2259\u201374. https:\/\/doi.org\/10.1306\/100212121019.","journal-title":"AAPG Bull"},{"issue":"7","key":"9863_CR20","doi-asserted-by":"publisher","first-page":"2725","DOI":"10.1007\/s00371-022-02488-0","volume":"39","author":"R Soroush","year":"2022","unstructured":"Soroush R, Baleghi Y. NIR\/RGB image fusion for scene classification using deep neural networks. Visual Comput. 2022;39(7):2725\u201339. https:\/\/doi.org\/10.1007\/s00371-022-02488-0.","journal-title":"Visual Comput"},{"key":"9863_CR21","doi-asserted-by":"publisher","first-page":"101641","DOI":"10.1016\/j.aei.2022.101641","volume":"53","author":"C Emunds","year":"2022","unstructured":"Emunds C, Pauen N, Richter V, et al. SpaRSE-BIM: classification of IFC-based geometry via sparse convolutional neural networks. Adv Eng Inform. 2022;53:101641. https:\/\/doi.org\/10.1016\/j.aei.2022.101641.","journal-title":"Adv Eng Inform"},{"issue":"12","key":"9863_CR22","first-page":"255","volume":"38","author":"YK Liang","year":"2021","unstructured":"Liang YK, Xie XL, Zhang YC, et al. Multi-label classification algorithm based on label correlation. Comput Simul. 2021;38(12):255\u201361.","journal-title":"Comput Simul"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09863-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09863-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09863-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T12:26:48Z","timestamp":1767011208000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09863-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9863"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09863-0","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]},"assertion":[{"value":"8 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study did not involve human participants or animal experiments, therefore ethical approval is not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors agree to the publication of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"341"}}