{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T02:26:18Z","timestamp":1740104778061,"version":"3.37.3"},"reference-count":61,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J of Intelligent Sys"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1002\/int.22731","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T11:23:06Z","timestamp":1636370586000},"page":"4561-4585","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A fuzzy data augmentation technique to improve regularisation"],"prefix":"10.1155","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3584-8402","authenticated-orcid":false,"given":"Rukshima","family":"Dabare","sequence":"first","affiliation":[{"name":"Discipline of Information Technology Murdoch University Perth Western Australia Australia"},{"name":"Department of Mechanical Engineering Faculty of Engineering Technology The Open University of Sri Lanka Nawala Nugegoda Sri Lanka"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8767-1031","authenticated-orcid":false,"given":"Kok Wai","family":"Wong","sequence":"additional","affiliation":[{"name":"Discipline of Information Technology Murdoch University Perth Western Australia Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9529-6320","authenticated-orcid":false,"given":"Mohd Fairuz","family":"Shiratuddin","sequence":"additional","affiliation":[{"name":"Discipline of Information Technology Murdoch University Perth Western Australia Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4168-0888","authenticated-orcid":false,"given":"Polychronis","family":"Koutsakis","sequence":"additional","affiliation":[{"name":"Discipline of Information Technology Murdoch University Perth Western Australia Australia"}]}],"member":"311","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.2196\/11016"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-018-0482-1"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.10.054"},{"issue":"1","key":"e_1_2_7_6_1","first-page":"3","article-title":"Deep neural network for supervised single\u2010channel speech enhancement","volume":"44","author":"Saleem N","year":"2019","journal-title":"Arch Acoust"},{"key":"e_1_2_7_7_1","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N","year":"2014","journal-title":"J Mach Learn Res"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.09.009"},{"volume-title":"Deep Learning","year":"2016","author":"Goodfellow I","key":"e_1_2_7_9_1"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2015.2438544"},{"key":"e_1_2_7_11_1","unstructured":"KrizhevskyA SutskeverI HintonGE. ImageNet classification with deep convolutional neural networks. Paper presented at: Advances in Neural Information Processing Systems;\u00a02012."},{"key":"e_1_2_7_12_1","first-page":"150102876","article-title":"Deep image: scaling up image recognition","author":"Wu R","year":"2015","journal-title":"arXiv preprint arXiv"},{"key":"e_1_2_7_13_1","first-page":"170804552","article-title":"Improved regularisation of convolutional neural networks with cutout","author":"DeVries T","year":"2017","journal-title":"arXiv preprint arXiv"},{"issue":"3","key":"e_1_2_7_14_1","first-page":"364","article-title":"Convolutional neural network with data augmentation for SAR target recognition","volume":"13","author":"Ding J","year":"2016","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"e_1_2_7_15_1","doi-asserted-by":"crossref","unstructured":"KafleK YousefhussienM KananC.Data augmentation for visual question answering. Paper presented at: Proceedings of the 10th International Conference on Natural Language Generation;2017.","DOI":"10.18653\/v1\/W17-3529"},{"key":"e_1_2_7_16_1","first-page":"13125242","article-title":"Unsupervised feature learning by augmenting single images","author":"Dosovitskiy A","year":"2013","journal-title":"arXiv preprint arXiv"},{"key":"e_1_2_7_17_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/4706576","article-title":"A full stage data augmentation method in deep convolutional neural network for natural image classification","volume":"2020","author":"Zheng Q","year":"2020","journal-title":"Discrete Dyn Nat Soc"},{"key":"e_1_2_7_18_1","unstructured":"JaitlyN HintonGE.Vocal tract length perturbation (VTLP) improves speech recognition. Paper presented at: Proceedings of the ICML Workshop on Deep Learning for Audio Speech and Language;2013."},{"key":"e_1_2_7_19_1","doi-asserted-by":"crossref","unstructured":"CuiX GoelV KingsburyB.Data augmentation for deep convolutional neural network acoustic modeling. Paper presented at: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP);2015.","DOI":"10.1109\/ICASSP.2015.7178831"},{"key":"e_1_2_7_20_1","unstructured":"ZhangX ZhaoJ LeCunY. Character\u2010level convolutional networks for text classification. Paper presented at: Advances in Neural Information Processing Systems; 2015."},{"key":"e_1_2_7_21_1","doi-asserted-by":"crossref","unstructured":"KobayashiS.Contextual augmentation: data augmentation by words with paradigmatic relations. Paper presented at: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Vol 2 (Short Papers);2018.","DOI":"10.18653\/v1\/N18-2072"},{"key":"e_1_2_7_22_1","doi-asserted-by":"crossref","unstructured":"SennrichR HaddowB BirchA.Improving neural machine translation models with monolingual data. Paper presented at: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Vol 1\u00a0(Long Papers);2016.","DOI":"10.18653\/v1\/P16-1009"},{"key":"e_1_2_7_23_1","doi-asserted-by":"crossref","unstructured":"FadaeeM BisazzaA MonzC.Data augmentation for low\u2010resource neural machine translation. Paper presented at: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vol 2\u00a0(Short Papers);\u00a02017.","DOI":"10.18653\/v1\/P17-2090"},{"key":"e_1_2_7_24_1","unstructured":"XuY JiaR MouL et al.Improved relation classification by deep recurrent neural networks with data augmentation. Paper presented at: Proceedings of COLING 2016 the 26th International Conference on Computational Linguistics: Technical Papers;\u00a02016."},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.3390\/sym10110648"},{"key":"e_1_2_7_26_1","unstructured":"dosSantos TanakaFK AranhaC.Data augmentation using GANs.arXiv.2019."},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.07.032"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2018.01.003"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2015.06.013"},{"key":"e_1_2_7_31_1","first-page":"180603852","article-title":"Data augmentation instead of explicit regularization","author":"Hern\u00e1ndez\u2010Garc\u00eda A","year":"2018","journal-title":"arXiv preprint arXiv"},{"key":"e_1_2_7_32_1","doi-asserted-by":"crossref","unstructured":"MeierU CiresanDC GambardellaLM SchmidhuberJ.Better digit recognition with a committee of simple neural nets. Paper presented at: 2011 International Conference on Document Analysis and Recognition;2011.","DOI":"10.1109\/ICDAR.2011.252"},{"key":"e_1_2_7_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/K17-2010"},{"key":"e_1_2_7_34_1","first-page":"190710905","article-title":"Invariance reduces variance: understanding data augmentation in deep learning and beyond","author":"Chen S","year":"2019","journal-title":"arXiv preprint arXiv"},{"key":"e_1_2_7_35_1","first-page":"200500178","article-title":"On the benefits of invariance in neural networks","author":"Lyle C","year":"2020","journal-title":"arXiv preprint arXiv"},{"key":"e_1_2_7_36_1","doi-asserted-by":"crossref","unstructured":"FawziA SamulowitzH TuragaD FrossardP.Adaptive data augmentation for image classification. Paper presented at: 2016 IEEE International Conference on Image Processing (ICIP);2016.","DOI":"10.1109\/ICIP.2016.7533048"},{"key":"e_1_2_7_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2696121"},{"key":"e_1_2_7_38_1","doi-asserted-by":"crossref","unstructured":"Moreno\u2010BareaFJ StrazzeraF JerezJM UrdaD FrancoL. Forward noise adjustment scheme for data augmentation. Paper presented at: 2018 IEEE Symposium Series on Computational Intelligence (SSCI);\u00a02018.","DOI":"10.1109\/SSCI.2018.8628917"},{"key":"e_1_2_7_39_1","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00052"},{"key":"e_1_2_7_40_1","doi-asserted-by":"crossref","unstructured":"WangZ IveJ VelupillaiS SpeciaL.Is artificial data useful for biomedical natural language processing algorithms? Paper presented at: Proceedings of the 18th BioNLP Workshop and Shared Task;\u00a02019.","DOI":"10.18653\/v1\/W19-5026"},{"key":"e_1_2_7_41_1","doi-asserted-by":"crossref","unstructured":"GonogL ZhouY.A review: generative adversarial networks. Paper presented at: 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA);\u00a02019.","DOI":"10.1109\/ICIEA.2019.8833686"},{"key":"e_1_2_7_42_1","unstructured":"GoodfellowI. NIPS 2016 tutorial: Generative adversarial networks.arXiv preprint arXiv:170100160.2016."},{"key":"e_1_2_7_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/0098-3004(84)90020-7"},{"issue":"2","key":"e_1_2_7_44_1","first-page":"3","article-title":"The application of horizontal membership functions to fuzzy arithmetic operations","volume":"8","author":"Tomaszewska K","year":"2014","journal-title":"J Theor Appl Comput Sci"},{"key":"e_1_2_7_45_1","doi-asserted-by":"publisher","DOI":"10.1142\/1721"},{"key":"e_1_2_7_46_1","unstructured":"AbebeA GuinotV SolomatineD.Fuzzy alpha\u2010cut vs. Monte Carlo techniques in assessing uncertainty in model parameters. Paper presented at: Proceedings of the 4th International Conference on Hydroinformatics;\u00a02000."},{"volume-title":"Fuzzy Set Theory\u2014and its Applications","year":"2011","author":"Zimmermann H\u2010J","key":"e_1_2_7_47_1"},{"key":"e_1_2_7_48_1","doi-asserted-by":"crossref","unstructured":"BanerjeeA KrumpelmanC GhoshJ BasuS MooneyRJ.Model\u2010based overlapping clustering. Paper presented at: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining;\u00a02005.","DOI":"10.1145\/1081870.1081932"},{"key":"e_1_2_7_49_1","doi-asserted-by":"publisher","DOI":"10.1002\/int.22105"},{"key":"e_1_2_7_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3086-0"},{"key":"e_1_2_7_51_1","unstructured":"DuaDaKT.E. UCI Machine Learning Repository.http:\/\/archive.ics.uci.edu\/ml;\u00a02017."},{"key":"e_1_2_7_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2016.04.023"},{"key":"e_1_2_7_53_1","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/8272091"},{"key":"e_1_2_7_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.03.036"},{"volume-title":"Hands\u2010on Machine Learning with Scikit\u2010Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems","year":"2017","author":"G\u00e9ron A","key":"e_1_2_7_55_1"},{"key":"e_1_2_7_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2657381"},{"key":"e_1_2_7_57_1","first-page":"65","article-title":"A simple sequentially rejective multiple test procedure","volume":"6","author":"Holm S","year":"1979","journal-title":"Scand J Stat"},{"key":"e_1_2_7_58_1","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar J","year":"2006","journal-title":"J Mach Learn Res"},{"volume-title":"Document Clustering with Optimized Unsupervised Feature Selection and Centroid Allocation","year":"2018","author":"Al\u2010Jadir I","key":"e_1_2_7_59_1"},{"key":"e_1_2_7_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.02.056"},{"key":"e_1_2_7_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106444"},{"key":"e_1_2_7_62_1","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural Networks for Pattern Recognition","author":"Bishop CM","year":"1995"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/int.22731","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/int.22731","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/int.22731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T10:43:11Z","timestamp":1699785791000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/int.22731"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,8]]},"references-count":61,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.1002\/int.22731"],"URL":"https:\/\/doi.org\/10.1002\/int.22731","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"type":"print","value":"0884-8173"},{"type":"electronic","value":"1098-111X"}],"subject":[],"published":{"date-parts":[[2021,11,8]]},"assertion":[{"value":"2020-12-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-24","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}