{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:23:12Z","timestamp":1779380592918,"version":"3.53.1"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s00521-022-07517-6","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T06:02:50Z","timestamp":1657087370000},"page":"19343-19376","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Fuzzy-twin proximal SVM kernel-based deep learning neural network model for hyperspectral image classification"],"prefix":"10.1007","volume":"34","author":[{"given":"Sanaboina Leela","family":"Krishna","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0842-373X","authenticated-orcid":false,"given":"I. Jasmine Selvakumari","family":"Jeya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. N.","family":"Deepa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"7517_CR1","doi-asserted-by":"crossref","unstructured":"He N, Fang L, Li S, Ghamisi P, Benediktsson JA (2017) Hyperspectral images classification by fusing extinction profiles feature. In: 2017 IEEE international geoscience and remote sensing symposium (IGARSS), IEEE, pp 2267\u20132270","DOI":"10.1109\/IGARSS.2017.8127441"},{"issue":"1","key":"7517_CR2","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1007\/s00521-016-2376-7","volume":"28","author":"R Rojas-Moraleda","year":"2017","unstructured":"Rojas-Moraleda R, Valous NA, Gowen A, Esquerre C, H\u00e4rtel S, Salinas L, O\u2019donnell C (2017) A frame-based ANN for classification of hyperspectral images: assessment of mechanical damage in mushrooms. Neural Comput Appl 28(1):969\u2013981","journal-title":"Neural Comput Appl"},{"key":"7517_CR3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.ins.2017.08.051","volume":"420","author":"C Shi","year":"2017","unstructured":"Shi C, Pun CM (2017) 3D multi-resolution wavelet convolutional neural networks for hyperspectral image classification. Info Sci 420:49\u201365","journal-title":"Info Sci"},{"issue":"12","key":"7517_CR4","doi-asserted-by":"publisher","first-page":"2355","DOI":"10.1109\/LGRS.2017.2764915","volume":"14","author":"Y Chen","year":"2017","unstructured":"Chen Y, Zhu L, Ghamisi P, Jia X, Li G, Tang L (2017) Hyperspectral images classification with Gabor filtering and convolutional neural network. IEEE Geosci Remote Sens Lett 14(12):2355\u20132359","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"12","key":"7517_CR5","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.3390\/rs9121255","volume":"9","author":"F Cao","year":"2017","unstructured":"Cao F, Yang Z, Ren J, Ling WK, Zhao H, Marshall S (2017) Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification. Remote Sens 9(12):1255","journal-title":"Remote Sens"},{"issue":"1","key":"7517_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11220-018-0196-9","volume":"19","author":"B Tu","year":"2018","unstructured":"Tu B, Zhang X, Wang J, Zhang G, Ou X (2018) Spectral\u2013spatial hyperspectral image classification via non-local means filtering feature extraction. Sens Imaging 19(1):1\u201325","journal-title":"Sens Imaging"},{"key":"7517_CR7","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.infrared.2018.10.012","volume":"95","author":"Z Chunhui","year":"2018","unstructured":"Chunhui Z, Bing G, Lejun Z, Xiaoqing W (2018) Classification of hyperspectral imagery based on spectral gradient, SVM and spatial random forest. Infrared Phys Technol 95:61\u201369","journal-title":"Infrared Phys Technol"},{"issue":"12","key":"7517_CR8","doi-asserted-by":"publisher","first-page":"7230","DOI":"10.1109\/TGRS.2018.2849443","volume":"56","author":"C Yang","year":"2018","unstructured":"Yang C, Bruzzone L, Zhao H, Tan Y, Guan R (2018) Superpixel-based unsupervised band selection for classification of hyperspectral images. IEEE Trans Geosci Remote Sens 56(12):7230\u20137245","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"12","key":"7517_CR9","doi-asserted-by":"publisher","first-page":"2036","DOI":"10.3390\/rs10122036","volume":"10","author":"J Li","year":"2018","unstructured":"Li J, Xi B, Du Q, Song R, Li Y, Ren G (2018) Deep kernel extreme-learning machine for the spectral\u2013spatial classification of hyperspectral imagery. Remote Sens 10(12):2036","journal-title":"Remote Sens"},{"issue":"12","key":"7517_CR10","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1080\/2150704X.2018.1511933","volume":"9","author":"B Liu","year":"2018","unstructured":"Liu B, Yu X, Yu A, Zhang P, Wan G (2018) Spectral-spatial classification of hyperspectral imagery based on recurrent neural networks. Remote Sens Lett 9(12):1118\u20131127","journal-title":"Remote Sens Lett"},{"issue":"24","key":"7517_CR11","doi-asserted-by":"publisher","first-page":"2892","DOI":"10.3390\/rs11242892","volume":"11","author":"YN Chen","year":"2019","unstructured":"Chen YN (2019) Multiple kernel feature line embedding for hyperspectral image classification. Remote Sens 11(24):2892","journal-title":"Remote Sens"},{"issue":"12","key":"7517_CR12","doi-asserted-by":"publisher","first-page":"4728","DOI":"10.1109\/JSTARS.2019.2950876","volume":"12","author":"S Zhong","year":"2019","unstructured":"Zhong S, Chang CI, Li J, Shang X, Chen S, Song M, Zhang Y (2019) Class feature weighted hyperspectral image classification. IEEE J Sel Top Appl Earth Observ Remote Sens 12(12):4728\u20134745","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"24","key":"7517_CR13","doi-asserted-by":"publisher","first-page":"5559","DOI":"10.3390\/s19245559","volume":"19","author":"N Li","year":"2019","unstructured":"Li N, Wang R, Zhao H, Wang M, Deng K, Wei W (2019) Improved classification method based on the diverse density and sparse representation model for a hyperspectral image. Sensors 19(24):5559","journal-title":"Sensors"},{"issue":"23","key":"7517_CR14","doi-asserted-by":"publisher","first-page":"3879","DOI":"10.3390\/rs12233879","volume":"12","author":"G Wang","year":"2020","unstructured":"Wang G, Ren P (2020) Hyperspectral image classification with feature-oriented adversarial active learning. Remote Sens 12(23):3879","journal-title":"Remote Sens"},{"issue":"4","key":"7517_CR15","first-page":"684","volume":"9","author":"G Subba Reddy","year":"2020","unstructured":"Subba Reddy G, Harikiran JJH (2020) Hyperspectral image classification using support vector machines. IAES Int J Artif Intell 9(4):684","journal-title":"IAES Int J Artif Intell"},{"issue":"24","key":"7517_CR16","doi-asserted-by":"publisher","first-page":"8833","DOI":"10.3390\/app10248833","volume":"10","author":"\u00c1 Acci\u00f3n","year":"2020","unstructured":"Acci\u00f3n \u00c1, Arg\u00fcello F, Heras DB (2020) Dual-window superpixel data augmentation for hyperspectral image classification. Appl Sci 10(24):8833","journal-title":"Appl Sci"},{"issue":"15","key":"7517_CR17","doi-asserted-by":"publisher","first-page":"3909","DOI":"10.1049\/iet-ipr.2020.0728","volume":"14","author":"R Vaddi","year":"2020","unstructured":"Vaddi R, Manoharan P (2020) Hyperspectral remote sensing image classification using combinatorial optimisation based un-supervised band selection and CNN. IET Image Process 14(15):3909\u20133919","journal-title":"IET Image Process"},{"issue":"24","key":"7517_CR18","doi-asserted-by":"publisher","first-page":"9393","DOI":"10.1080\/01431161.2020.1798553","volume":"41","author":"G Zhao","year":"2020","unstructured":"Zhao G, Wang X, Cheng Y (2020) Hyperspectral image classification based on local binary pattern and broad learning system. Int J Remote Sens 41(24):9393\u20139417","journal-title":"Int J Remote Sens"},{"key":"7517_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11227-021-03638-2","volume":"77","author":"ME Paoletti","year":"2021","unstructured":"Paoletti ME, Tao X, Haut JM, Moreno-\u00c1lvarez S, Plaza A (2021) Deep mixed precision for hyperspectral image classification. J Supercomput 77:1\u201312","journal-title":"J Supercomput"},{"key":"7517_CR20","first-page":"100580","volume":"23","author":"P Iyer","year":"2021","unstructured":"Iyer P, Sriram A, Lal S (2021) Deep learning ensemble method for classification of satellite hyperspectral images. Remote Sens Appl Soc Environ 23:100580","journal-title":"Remote Sens Appl Soc Environ"},{"issue":"4","key":"7517_CR21","first-page":"25","volume":"28","author":"S Zheng","year":"2021","unstructured":"Zheng S, Liu W, Shan R, Zhao J, Jiang G, Zhang Z (2021) Multi-scale dilated convolutional neural network for hyperspectral image classification. J Harbin Inst Technol (New Series) 28(4):25\u201332","journal-title":"J Harbin Inst Technol (New Series)"},{"key":"7517_CR22","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neucom.2021.03.035","volume":"448","author":"S Jia","year":"2021","unstructured":"Jia S, Jiang S, Lin Z, Li N, Xu M, Yu S (2021) A survey: deep learning for hyperspectral image classification with few labeled samples. Neurocomputing 448:179\u2013204","journal-title":"Neurocomputing"},{"issue":"17","key":"7517_CR23","doi-asserted-by":"publisher","first-page":"3396","DOI":"10.3390\/rs13173396","volume":"13","author":"F Zhao","year":"2021","unstructured":"Zhao F, Zhang J, Meng Z, Liu H (2021) Densely connected pyramidal dilated convolutional network for hyperspectral image classification. Remote Sens 13(17):3396","journal-title":"Remote Sens"},{"issue":"17","key":"7517_CR24","doi-asserted-by":"publisher","first-page":"3547","DOI":"10.3390\/rs13173547","volume":"13","author":"X He","year":"2021","unstructured":"He X, Chen Y (2021) Modifications of the multi-layer perceptron for hyperspectral image classification. Remote Sens 13(17):3547","journal-title":"Remote Sens"},{"issue":"18","key":"7517_CR25","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.3390\/rs13183637","volume":"13","author":"ME Paoletti","year":"2021","unstructured":"Paoletti ME, Haut JM (2021) Adaptable convolutional network for hyperspectral image classification. Remote Sens 13(18):3637","journal-title":"Remote Sens"},{"key":"7517_CR26","doi-asserted-by":"publisher","first-page":"104843","DOI":"10.1016\/j.cageo.2021.104843","volume":"155","author":"T Bahraini","year":"2021","unstructured":"Bahraini T, Azimpour P, Yazdi HS (2021) Modified-mean-shift-based noisy label detection for hyperspectral image classification. Comput Geosci 155:104843","journal-title":"Comput Geosci"},{"key":"7517_CR27","doi-asserted-by":"publisher","first-page":"1791","DOI":"10.1109\/LGRS.2020.3009017","volume":"18","author":"F Zhang","year":"2020","unstructured":"Zhang F, Bai J, Zhang J, Xiao Z, Pei C (2020) An optimized training method for GAN-based hyperspectral image classification. IEEE Geosci Remote Sens Lett 18:1791","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7517_CR28","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.1109\/LGRS.2020.3010837","volume":"18","author":"W Yao","year":"2020","unstructured":"Yao W, Lian C, Bruzzone L (2020) Clustercnn: clustering-based feature learning for hyperspectral image classification. IEEE Geosci Remote Sens Lett 18:1991","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"7517_CR29","doi-asserted-by":"publisher","first-page":"107805","DOI":"10.1016\/j.asoc.2021.107805","volume":"112","author":"M Wang","year":"2021","unstructured":"Wang M, Liu W, Chen M, Huang X, Han W (2021) A band selection approach based on a modified gray wolf optimizer and weight updating of bands for hyperspectral image. Appl Soft Comput 112:107805","journal-title":"Appl Soft Comput"},{"key":"7517_CR30","doi-asserted-by":"publisher","first-page":"116416","DOI":"10.1016\/j.image.2021.116416","volume":"99","author":"A Mookambiga","year":"2021","unstructured":"Mookambiga A, Gomathi V (2021) Kernel eigenmaps based multiscale sparse model for hyperspectral image classification. Signal Process Image Commun 99:116416","journal-title":"Signal Process Image Commun"},{"issue":"11","key":"7517_CR31","doi-asserted-by":"publisher","first-page":"13243","DOI":"10.1007\/s11227-021-03954-7","volume":"77","author":"R Tamilarasi","year":"2021","unstructured":"Tamilarasi R, Prabu S (2021) Automated building and road classifications from hyperspectral imagery through a fully convolutional network and support vector machine. J Supercomput 77(11):13243\u201313261","journal-title":"J Supercomput"},{"issue":"21","key":"7517_CR32","doi-asserted-by":"publisher","first-page":"4472","DOI":"10.3390\/rs13214472","volume":"13","author":"T Zhang","year":"2021","unstructured":"Zhang T, Shi C, Liao D, Wang L (2021) Deep spectral spatial inverted residual network for hyperspectral image classification. Remote Sens 13(21):4472","journal-title":"Remote Sens"},{"key":"7517_CR33","doi-asserted-by":"publisher","first-page":"115280","DOI":"10.1016\/j.eswa.2021.115280","volume":"182","author":"G Ortac","year":"2021","unstructured":"Ortac G, Ozcan G (2021) Comparative study of hyperspectral image classification by multidimensional convolutional neural network approaches to improve accuracy. Expert Syst Appl 182:115280","journal-title":"Expert Syst Appl"},{"key":"7517_CR34","doi-asserted-by":"publisher","first-page":"10298","DOI":"10.1109\/TGRS.2020.3037746","volume":"59","author":"F He","year":"2020","unstructured":"He F, Nie F, Wang R, Jia W, Zhang F, Li X (2020) Semisupervised band selection with graph optimization for hyperspectral image classification. IEEE Trans Geosci Remote Sens 59:10298","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR35","doi-asserted-by":"publisher","first-page":"10429","DOI":"10.1109\/TGRS.2021.3049282","volume":"59","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Li Y, Zou S, Zhang H, Bai Y (2021) Hyperspectral image classification with spatial consistence using fully convolutional spatial propagation network. IEEE Trans Geosci Remote Sens 59:10429","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR36","doi-asserted-by":"publisher","first-page":"2147","DOI":"10.1109\/LGRS.2020.3013707","volume":"18","author":"C Mu","year":"2020","unstructured":"Mu C, Zeng Q, Liu Y, Qu Y (2020) A two-branch network combined with robust principal component analysis for hyperspectral image classification. IEEE Geosci Remote Sens Lett 18:2147","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"1","key":"7517_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-97029-5","volume":"11","author":"S Cheng","year":"2021","unstructured":"Cheng S, Wang L, Du A (2021) Asymmetric coordinate attention spectral-spatial feature fusion network for hyperspectral image classification. Sci Rep 11(1):1\u201317","journal-title":"Sci Rep"},{"key":"7517_CR38","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.biosystemseng.2021.09.010","volume":"212","author":"Z Yu","year":"2021","unstructured":"Yu Z, Fang H, Zhangjin Q, Mi C, Feng X, He Y (2021) Hyperspectral imaging technology combined with deep learning for hybrid okra seed identification. Biosyst Eng 212:46\u201361","journal-title":"Biosyst Eng"},{"key":"7517_CR39","doi-asserted-by":"publisher","first-page":"102114","DOI":"10.1016\/j.displa.2021.102114","volume":"70","author":"Z Wang","year":"2021","unstructured":"Wang Z, Li J, Zhang T (2021) Discriminative graph convolution networks for hyperspectral image classification. Displays 70:102114","journal-title":"Displays"},{"key":"7517_CR40","doi-asserted-by":"publisher","first-page":"103948","DOI":"10.1016\/j.infrared.2021.103948","volume":"119","author":"P Manoharan","year":"2021","unstructured":"Manoharan P, Boggavarapu PKL (2021) Improved whale optimization based band selection for hyperspectral remote sensing image classification. Infrared Phys Technol 119:103948","journal-title":"Infrared Phys Technol"},{"issue":"24","key":"7517_CR41","doi-asserted-by":"publisher","first-page":"5043","DOI":"10.3390\/rs13245043","volume":"13","author":"Q Liu","year":"2021","unstructured":"Liu Q, Wu Z, Jia X, Xu Y, Wei Z (2021) From local to global: class feature fused fully convolutional network for hyperspectral image classification. Remote Sens 13(24):5043","journal-title":"Remote Sens"},{"issue":"24","key":"7517_CR42","doi-asserted-by":"publisher","first-page":"5009","DOI":"10.3390\/rs13245009","volume":"13","author":"L Huang","year":"2021","unstructured":"Huang L, Chen Y, He X (2021) Weakly supervised classification of hyperspectral image based on complementary learning. Remote Sens 13(24):5009","journal-title":"Remote Sens"},{"key":"7517_CR43","first-page":"102603","volume":"105","author":"N Wambugu","year":"2021","unstructured":"Wambugu N, Chen Y, Xiao Z, Tan K, Wei M, Liu X, Li J (2021) Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: a review. Int J Appl Earth Observ Geoinf 105:102603","journal-title":"Int J Appl Earth Observ Geoinf"},{"key":"7517_CR44","first-page":"1","volume":"60","author":"J Feng","year":"2022","unstructured":"Feng J, Li D, Gu J, Cao X, Shang R, Zhang X, Jiao L (2022) Deep reinforcement learning for semisupervised hyperspectral band selection. IEEE Trans Geosci Remote Sens 60:1\u201319","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR45","first-page":"1","volume":"60","author":"C Yu","year":"2022","unstructured":"Yu C, Han R, Song M, Liu C, Chang CI (2022) Feedback attention-based dense CNN for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201316","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR46","first-page":"1","volume":"60","author":"B Cui","year":"2022","unstructured":"Cui B, Dong XM, Zhan Q, Peng J, Sun W (2022) LiteDepthwiseNet: a lightweight network for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR47","first-page":"1","volume":"60","author":"L Mou","year":"2022","unstructured":"Mou L, Saha S, Hua Y, Bovolo F, Bruzzone L, Zhu XX (2022) Deep reinforcement learning for band selection in hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR48","first-page":"1","volume":"60","author":"J Bai","year":"2022","unstructured":"Bai J, Ding B, Xiao Z, Jiao L, Chen H, Regan AC (2022) Hyperspectral image classification based on deep attention graph convolutional network. IEEE Trans Geosci Remote Sens 60:1\u201316","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR49","first-page":"1","volume":"60","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Liu D, Gao D, Shi G (2022) S3Net: spectral-spatial-semantic network for hyperspectral image classification with the multiway attention mechanism. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3217293","volume":"60","author":"C Wang","year":"2022","unstructured":"Wang C, Zhang L, Wei W, Zhang Y (2022) Toward effective hyperspectral image classification using dual-level deep spatial manifold representation. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR51","first-page":"1","volume":"60","author":"S Jia","year":"2022","unstructured":"Jia S, Liu X, Xu M, Yan Q, Zhou J, Jia X, Li Q (2022) Gradient feature-oriented 3-D domain adaptation for hyperspectral image classification. IEEE Trans Geosci Remote Sens 60:1\u201317","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR52","doi-asserted-by":"publisher","first-page":"108224","DOI":"10.1016\/j.patcog.2021.108224","volume":"121","author":"A Sellami","year":"2022","unstructured":"Sellami A, Tabbone S (2022) Deep neural networks-based relevant latent representation learning for hyperspectral image classification. Pattern Recognit 121:108224","journal-title":"Pattern Recognit"},{"issue":"1","key":"7517_CR53","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1049\/ipr2.12330","volume":"16","author":"X Tan","year":"2022","unstructured":"Tan X, Xue Z, Yu X, Sun Y, Gao K (2022) Hyperspectral image classification with deep 3D capsule network and Markov random field. IET Image Process 16(1):79\u201391","journal-title":"IET Image Process"},{"issue":"1","key":"7517_CR54","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/2150704X.2021.1992034","volume":"13","author":"Y Wang","year":"2022","unstructured":"Wang Y, Song T, Xie Y, Roy SK (2022) A probabilistic neighbourhood pooling-based attention network for hyperspectral image classification. Remote Sens Lett 13(1):65\u201375","journal-title":"Remote Sens Lett"},{"key":"7517_CR55","doi-asserted-by":"crossref","unstructured":"Minhazur Rahman AFM, Ahmed B (2022) Hyperspectral image classification using factor analysis and convolutional neural networks. In: Proceedings of the international conference on Big Data, IoT, and machine learning, Springer, Singapore , pp 129\u2013139","DOI":"10.1007\/978-981-16-6636-0_11"},{"key":"7517_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2020.3034656","volume":"60","author":"H Fu","year":"2022","unstructured":"Fu H, Sun G, Ren J, Zhang A, Jia X (2022) Fusion of PCA and segmented-PCA domain multiscale 2-D-SSA for effective spectral-spatial feature extraction and data classification in hyperspectral imagery. IEEE Trans Geosci Remote Sens 60:1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2020.3034656","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7517_CR57","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/JSTARS.2021.3133009","volume":"15","author":"B Tu","year":"2022","unstructured":"Tu B, He W, He W, Ou X, Plaza AJ (2022) Hyperspectral classification via global-local hierarchical weighting fusion network. IEEE J Sel Top Appl Earth Observ Remote Sens 15:184","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"7517_CR58","doi-asserted-by":"crossref","unstructured":"Hitendra Sarma T, Kakarla S (2022) A new CNN for pixel classification in hyperspectral images. In: Proceedings of international conference on data science and applications, Springer, Singapore , pp 773\u2013782","DOI":"10.1007\/978-981-16-5348-3_61"},{"key":"7517_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2020.3017414","volume":"19","author":"D Hong","year":"2022","unstructured":"Hong D, Gao L, Hang R, Zhang B, Chanussot J (2022) Deep encoder-decoder networks for classification of hyperspectral and LiDAR data. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2020.3017414","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"1","key":"7517_CR60","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3390\/rs14010171","volume":"14","author":"Q Wang","year":"2022","unstructured":"Wang Q, Chen M, Zhang J, Kang S, Wang Y (2022) Improved active deep learning for semi-supervised classification of hyperspectral image. Remote Sens 14(1):171","journal-title":"Remote Sens"},{"key":"7517_CR61","doi-asserted-by":"publisher","first-page":"108316","DOI":"10.1016\/j.patcog.2021.108316","volume":"122","author":"C Shi","year":"2022","unstructured":"Shi C, Fang L, Lv Z, Zhao M (2022) Explainable scale distillation for hyperspectral image classification. Pattern Recognit 122:108316","journal-title":"Pattern Recognit"},{"issue":"4","key":"7517_CR62","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1007\/s10278-013-9669-5","volume":"27","author":"DS Nachimuthu","year":"2014","unstructured":"Nachimuthu DS, Baladhandapani A (2014) Multidimensional texture characterization: on analysis for brain tumor tissues using MRS and MRI. J Dig Imaging 27(4):496\u2013506","journal-title":"J Dig Imaging"},{"issue":"1","key":"7517_CR63","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s10586-018-2005-6","volume":"22","author":"V Ranganayaki","year":"2019","unstructured":"Ranganayaki V, Deepa SN (2019) Linear and non-linear proximal support vector machine classifiers for wind speed prediction. Cluster Comput 22(1):379\u2013390","journal-title":"Cluster Comput"},{"key":"7517_CR64","first-page":"1","volume":"24","author":"YJ Natarajan","year":"2019","unstructured":"Natarajan YJ, Nachimuthu DS (2019) New SVM kernel soft computing models for wind speed prediction in renewable energy applications. Soft Comput 24:1\u201318","journal-title":"Soft Comput"},{"issue":"2","key":"7517_CR65","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s40009-016-0521-6","volume":"40","author":"V Ranganayaki","year":"2017","unstructured":"Ranganayaki V, Deepa SN (2017) SVM based neuro fuzzy model for short term wind power forecasting. Natl Acad Sci Lett 40(2):131\u2013134","journal-title":"Natl Acad Sci Lett"},{"key":"7517_CR66","doi-asserted-by":"publisher","first-page":"18411","DOI":"10.1007\/s00500-020-05048-7","volume":"24","author":"M Revathi","year":"2020","unstructured":"Revathi M, Jeya IJS, Deepa SN (2020) Deep learning-based soft computing model for image classification application. Soft Comput 24:18411\u201318430","journal-title":"Soft Comput"},{"key":"7517_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/7493535","volume":"2016","author":"I Selvakumari Jeya","year":"2016","unstructured":"Selvakumari Jeya I, Deepa SN (2016) Lung cancer classification employing proposed real coded genetic algorithm based radial basis function neural network classifier. Comput Math Methods Med 2016:1","journal-title":"Comput Math Methods Med"},{"issue":"4","key":"7517_CR68","first-page":"748","volume":"15","author":"J Selvakumari","year":"2018","unstructured":"Selvakumari J, Jeyaraj S (2018) Using visible and invisible watermarking algorithms for indexing medical images. Int Arab J Inf Technol 15(4):748\u2013755","journal-title":"Int Arab J Inf Technol"},{"key":"7517_CR69","volume-title":"Neural networks: tricks of the trade","year":"2003","unstructured":"Orr GB, M\u00fcller KR (eds) (2003) Neural networks: tricks of the trade. Springer, Berlin"},{"key":"7517_CR70","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-021-01901-7","author":"YD Zhang","year":"2022","unstructured":"Zhang YD, Wang W, Zhang X, Wang SH (2022) Secondary pulmonary tuberculosis recognition by 4-direction varying-distance GLCM and fuzzy SVM. Mobile Netw Appl. https:\/\/doi.org\/10.1007\/s11036-021-01901-7","journal-title":"Mobile Netw Appl"},{"key":"7517_CR71","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-022-04626-2","author":"S Laxmi","year":"2022","unstructured":"Laxmi S, Gupta SK, Kumar S (2022) Intuitionistic fuzzy least square twin support vector machines for pattern classification. Ann Oper Res. https:\/\/doi.org\/10.1007\/s10479-022-04626-2","journal-title":"Ann Oper Res"},{"key":"7517_CR72","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2022.3161729","author":"MA Ganaie","year":"2022","unstructured":"Ganaie MA, Tanveer M, Lin CT (2022) Large scale fuzzy least squares twin SVMs for class imbalance learning. IEEE Trans Fuzzy Syst. https:\/\/doi.org\/10.1109\/TFUZZ.2022.3161729","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"7517_CR73","doi-asserted-by":"publisher","first-page":"2863","DOI":"10.3233\/JIFS-201680","volume":"42","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Liang J, Chen L, Wang Y, Gong J (2022) Evaluation and prediction of free driving behavior type based on fuzzy comprehensive support vector machine. J Intell Fuzzy Syst 42(4):2863\u20132879","journal-title":"J Intell Fuzzy Syst"},{"key":"7517_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2022.03.009","author":"P Borah","year":"2022","unstructured":"Borah P, Gupta D (2022) Affinity and transformed class probability-based fuzzy least squares support vector machines. Fuzzy Sets Syst. https:\/\/doi.org\/10.1016\/j.fss.2022.03.009","journal-title":"Fuzzy Sets Syst"},{"key":"7517_CR75","doi-asserted-by":"crossref","unstructured":"Wang K, An J, Ma X, Ma C, Bao H (2022) Imbalance classification based on deep learning and fuzzy support vector machine. In: International conference on bio-inspired computing: theories and applications, vol 1566 CCIS, Springer, Singapore, pp 32\u201344","DOI":"10.1007\/978-981-19-1253-5_3"},{"key":"7517_CR76","doi-asserted-by":"publisher","first-page":"802712","DOI":"10.3389\/fbioe.2021.802712","volume":"9","author":"KF Wang","year":"2022","unstructured":"Wang KF, An J, Wei Z, Cui C, Ma XH, Ma C, Bao HQ (2022) Deep learning-based imbalanced classification with fuzzy support vector machine. Front Bioeng Biotechnol 9:802712","journal-title":"Front Bioeng Biotechnol"},{"issue":"3","key":"7517_CR77","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1007\/s00500-021-06553-z","volume":"26","author":"S Memi\u015f","year":"2022","unstructured":"Memi\u015f S, Engino\u011flu S, Erkan U (2022) A classification method in machine learning based on soft decision-making via fuzzy parameterized fuzzy soft matrices. Soft Comput 26(3):1165\u20131180","journal-title":"Soft Comput"},{"issue":"1","key":"7517_CR78","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s11600-021-00700-8","volume":"70","author":"N Moosavi","year":"2022","unstructured":"Moosavi N, Bagheri M, Nabi-Bidhendi M, Heidari R (2022) Fuzzy support vector regression for permeability estimation of petroleum reservoir using well logs. Acta Geophysica 70(1):161\u2013172","journal-title":"Acta Geophysica"},{"issue":"4","key":"7517_CR79","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.3390\/app12041771","volume":"12","author":"VR Sharabiani","year":"2022","unstructured":"Sharabiani VR, Kaveh M, Taghinezhad E, Abbaszadeh R, Khalife E, Szymanek M, Dziwulska-Hunek A (2022) Application of artificial neural networks, support vector, adaptive neuro-fuzzy inference systems for the moisture ratio of parboiled hulls. Appl Sci 12(4):1771","journal-title":"Appl Sci"},{"issue":"15","key":"7517_CR80","doi-asserted-by":"publisher","first-page":"21935","DOI":"10.1007\/s11356-021-17443-0","volume":"29","author":"H Khodakhah","year":"2022","unstructured":"Khodakhah H, Aghelpour P, Hamedi Z (2022) Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH. Environ Sci Pollut Res 29(15):21935\u201321954","journal-title":"Environ Sci Pollut Res"},{"issue":"3","key":"7517_CR81","doi-asserted-by":"publisher","first-page":"2266","DOI":"10.1002\/int.22773","volume":"37","author":"D Li","year":"2022","unstructured":"Li D, Xu X, Wang Z, Cao C, Wang M (2022) Boundary-based Fuzzy-SVDD for one-class classification. Int J Intell Syst 37(3):2266\u20132292","journal-title":"Int J Intell Syst"},{"issue":"1","key":"7517_CR82","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/bdcc6010013","volume":"6","author":"EA Algehyne","year":"2022","unstructured":"Algehyne EA, Jibril ML, Algehainy NA, Alamri OA, Alzahrani AK (2022) Fuzzy neural network expert system with an improved Gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia. Big Data Cognit Comput 6(1):13","journal-title":"Big Data Cognit Comput"},{"issue":"2","key":"7517_CR83","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1007\/s11063-021-10671-y","volume":"54","author":"BB Hazarika","year":"2022","unstructured":"Hazarika BB, Gupta D (2022) Density weighted twin support vector machines for binary class imbalance learning. Neural Process Lett 54(2):1091\u20131130","journal-title":"Neural Process Lett"},{"key":"7517_CR84","doi-asserted-by":"publisher","first-page":"104687","DOI":"10.1016\/j.engappai.2022.104687","volume":"110","author":"S Laxmi","year":"2022","unstructured":"Laxmi S, Gupta SK (2022) Multi-category intuitionistic fuzzy twin support vector machines with an application to plant leaf recognition. Eng Appl Artif Intell 110:104687","journal-title":"Eng Appl Artif Intell"},{"key":"7517_CR85","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.neunet.2022.02.007","volume":"149","author":"PY Hao","year":"2022","unstructured":"Hao PY, Chiang JH, Chen YD (2022) Possibilistic classification by support vector networks. Neural Netw 149:40\u201356","journal-title":"Neural Netw"},{"key":"7517_CR86","doi-asserted-by":"publisher","first-page":"123348","DOI":"10.1016\/j.fuel.2022.123348","volume":"316","author":"Y Huang","year":"2022","unstructured":"Huang Y, Li F, Bao G, Xiao Q, Wang H (2022) Modeling the effects of biodiesel chemical composition on iodine value using novel machine learning algorithm. Fuel 316:123348","journal-title":"Fuel"},{"key":"7517_CR87","doi-asserted-by":"publisher","first-page":"123837","DOI":"10.1016\/j.fuel.2022.123837","volume":"319","author":"J Zhou","year":"2022","unstructured":"Zhou J, Guo Y, Wang S, Cheng G (2022) Research on intelligent optimization separation technology of coal and gangue base on LS-FSVM by using a binary artificial sheep algorithm. Fuel 319:123837","journal-title":"Fuel"},{"key":"7517_CR88","doi-asserted-by":"crossref","unstructured":"Malathi M, Sinthia P, Mary GAA, Nalini M, Wahed FF (2022) Segmentation of breast cancer using fuzzy C means and classification by SVM based on LBP features. In: AIP conference proceedings, vol 2405, no 1, AIP Publishing LLC, p 020002","DOI":"10.1063\/5.0072671"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07517-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07517-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07517-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T05:14:26Z","timestamp":1668489266000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07517-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":88,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["7517"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07517-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"3 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors confirm there is no conflict of interest in publishing this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}