{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:13:25Z","timestamp":1772554405809,"version":"3.50.1"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T00:00:00Z","timestamp":1703635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T00:00:00Z","timestamp":1703635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62377034"],"award-info":[{"award-number":["62377034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61907028"],"award-info":[{"award-number":["No.61907028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019595","name":"Youth and Middle-aged Scientific and Technological Innovation Leading Talents Program of the Corps","doi-asserted-by":"publisher","award":["2021KJXX-91"],"award-info":[{"award-number":["2021KJXX-91"]}],"id":[{"id":"10.13039\/501100019595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. GK202101004"],"award-info":[{"award-number":["No. GK202101004"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012270","name":"Shaanxi Key Science and Technology Innovation Team Project","doi-asserted-by":"publisher","award":["No. 2022TD-26"],"award-info":[{"award-number":["No. 2022TD-26"]}],"id":[{"id":"10.13039\/501100012270","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s00521-023-09343-w","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T09:02:36Z","timestamp":1703667756000},"page":"5231-5249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Special perceptual parsing for Chinese landscape painting scene understanding: a semantic segmentation approach"],"prefix":"10.1007","volume":"36","author":[{"given":"Rui","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Honghong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ru","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yumei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,27]]},"reference":[{"key":"9343_CR1","unstructured":"Bousselham W, Thibault G, Pagano L, Machireddy A, Gray J, Chang YH, Song X (2021) Efficient self-ensemble framework for semantic segmentation. arXiv preprint arXiv:2111.13280"},{"key":"9343_CR2","first-page":"213","volume-title":"End-to-end object detection with transformers","author":"N Carion","year":"2020","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. European conference on computer vision. Springer, Berlin, pp 213\u2013229"},{"key":"9343_CR3","first-page":"261","volume-title":"Image recoloring of art paintings for the color blind guided by semantic segmentation","author":"S Chatzistamatis","year":"2020","unstructured":"Chatzistamatis S, Rigos A, Tsekouras GE (2020) Image recoloring of art paintings for the color blind guided by semantic segmentation. International conference on engineering applications of neural networks. Springer, Berlin, pp 261\u2013273"},{"key":"9343_CR4","unstructured":"Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587"},{"key":"9343_CR5","doi-asserted-by":"crossref","unstructured":"Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"9343_CR6","doi-asserted-by":"crossref","unstructured":"Cheng B, Misra I, Schwing AG, Kirillov A, Girdhar R (2022) Masked-attention mask transformer for universal image segmentation","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"9343_CR7","doi-asserted-by":"crossref","unstructured":"Cheng B, Misra I, Schwing AG, Kirillov A, Girdhar R (2022) Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1290\u20131299","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"9343_CR8","doi-asserted-by":"crossref","unstructured":"Choi S, Kim JT, Choo J (2020) Cars can\u2019t fly up in the sky: improving urban-scene segmentation via height-driven attention networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9373\u20139383","DOI":"10.1109\/CVPR42600.2020.00939"},{"key":"9343_CR9","doi-asserted-by":"crossref","unstructured":"Cohen N, Newman Y, Shamir A (022) Semantic segmentation in art paintings. In: Computer graphics forum, vol\u00a041, pp 261\u2013275. Wiley Online Library","DOI":"10.1111\/cgf.14473"},{"key":"9343_CR10","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, chiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"key":"9343_CR11","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li FF (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE computer society conference on computer vision and pattern recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"9343_CR12","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et\u00a0al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"issue":"1","key":"9343_CR13","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98\u2013136","journal-title":"Int J Comput Vis"},{"key":"9343_CR14","doi-asserted-by":"crossref","unstructured":"He J, Deng Z, Qiao Y (2019) Dynamic multi-scale filters for semantic segmentation. In:Proceedings of the IEEE\/CVF international conference on computer vision, pp 3562\u20133572","DOI":"10.1109\/ICCV.2019.00366"},{"key":"9343_CR15","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R(2017) Mask R-CNN. In:Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"9343_CR16","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J(2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9343_CR17","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 603\u2013612","DOI":"10.1109\/ICCV.2019.00069"},{"key":"9343_CR18","unstructured":"Islam MA, Jia S, Bruce NDB (2020) How much position information do convolutional neural networks encode? arXiv preprint arXiv:2001.08248"},{"key":"9343_CR19","doi-asserted-by":"crossref","unstructured":"Kirillov A, He K, Girshick R, Rother C, Doll\u00e1r P(2019) Panoptic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9404\u20139413","DOI":"10.1109\/CVPR.2019.00963"},{"issue":"12","key":"9343_CR20","doi-asserted-by":"publisher","first-page":"2535","DOI":"10.1109\/TVCG.2016.2622269","volume":"23","author":"Y-C Lai","year":"2016","unstructured":"Lai Y-C, Chen B-A, Chen K-W, Si W-L, Yao C-Y, Zhang E (2016) Data-driven npr illustrations of natural flows in Chinese painting. IEEE Trans Vis Comput Graph 23(12):2535\u20132549","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"9343_CR21","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"9343_CR22","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S(2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"key":"9343_CR23","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988,","DOI":"10.1109\/ICCV.2017.324"},{"key":"9343_CR24","unstructured":"Li H, Tao C, Zhu X, Wang X, Huang G, Dai J(2021) Auto seg-loss: searching metric surrogates for semantic segmentation. ArXiv, ArXiv:abs\/2010.07930"},{"key":"9343_CR25","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"9343_CR26","unstructured":"Liu S, Li F, Zhang H, Yang X, Qi X, Su H, Zhu J, Zhang L (2022) DAB-DETR: dynamic anchor boxes are better queries for DETR. In: International conference on learning representations"},{"key":"9343_CR27","doi-asserted-by":"crossref","unstructured":"Li X, Wang W, Hu X, Yang J(2019) Selective kernel networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 510\u2013519","DOI":"10.1109\/CVPR.2019.00060"},{"key":"9343_CR28","doi-asserted-by":"publisher","first-page":"284","DOI":"10.2307\/2718351","volume":"25","author":"M Loehr","year":"1964","unstructured":"Loehr M (1964) The way of the brush: painting techniques of China and Japan. Harv J Asiat Stud 25:284\u2013289","journal-title":"Harv J Asiat Stud"},{"key":"9343_CR29","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"9343_CR30","unstructured":"Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101"},{"key":"9343_CR31","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), pp 565\u2013571, IEEE","DOI":"10.1109\/3DV.2016.79"},{"key":"9343_CR32","unstructured":"MMSegmentation Contributors (2020) MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https:\/\/github.com\/open-mmlab\/mmsegmentation"},{"key":"9343_CR33","unstructured":"PaddlePaddle Contributors (2019) Paddleseg, end-to-end image segmentation kit based on paddlepaddle. https:\/\/github.com\/PaddlePaddle\/PaddleSeg"},{"key":"9343_CR34","doi-asserted-by":"crossref","unstructured":"Peng Z, Huang W, Gu S, Xie L, Wang Y, Jiao J, Ye Q (2021) Conformer: local features coupling global representations for visual recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 367\u2013376","DOI":"10.1109\/ICCV48922.2021.00042"},{"key":"9343_CR35","doi-asserted-by":"crossref","unstructured":"Rezatofighi H, Tsoi N, Gwak JY, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 658\u2013666","DOI":"10.1109\/CVPR.2019.00075"},{"key":"9343_CR36","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18, pp 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9343_CR37","doi-asserted-by":"crossref","unstructured":"Strudel R, Pinel RG, Laptev I, Schmid C(2021) Segmenter: transformer for semantic segmentation. In: ICCV, pp 7242\u20137252. IEEE","DOI":"10.1109\/ICCV48922.2021.00717"},{"issue":"12","key":"9343_CR38","doi-asserted-by":"publisher","first-page":"3019","DOI":"10.1109\/TVCG.2017.2774292","volume":"24","author":"F Tang","year":"2017","unstructured":"Tang F, Dong W, Meng Y, Mei X, Huang F, Zhang X, Deussen O (2017) Animated construction of Chinese brush paintings. IEEE Trans Vis Comput Graph 24(12):3019\u20133031","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"9343_CR39","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H (2020) Conditional convolutions for instance segmentation. In: European conference on computer vision, pp 282\u2013298. Springer","DOI":"10.1007\/978-3-030-58452-8_17"},{"key":"9343_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2019.111322","volume":"237","author":"X-Y Tong","year":"2020","unstructured":"Tong X-Y, Xia G-S, Qikai L, Shen H, Li S, You S, Zhang L (2020) Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sens Environ 237:111322","journal-title":"Remote Sens Environ"},{"key":"9343_CR41","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst, 30"},{"key":"9343_CR42","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.3389\/fnhum.2014.01018","volume":"8","author":"T Wang","year":"2015","unstructured":"Wang T, Mo L, Vartanian O, Cant JS, Cupchik G (2015) An investigation of the neural substrates of mind wandering induced by viewing traditional Chinese landscape paintings. Front Hum Neurosci 8:1018","journal-title":"Front Hum Neurosci"},{"issue":"10","key":"9343_CR43","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Yadong M, Tan M, Wang X et al (2020) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43(10):3349\u20133364","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9343_CR44","first-page":"17721","volume":"33","author":"X Wang","year":"2020","unstructured":"Wang X, Zhang R, Kong T, Li L, Shen C (2020) Solov2: dynamic and fast instance segmentation. Adv Neural Inf Process Syst 33:17721\u201317732","journal-title":"Adv Neural Inf Process Syst"},{"key":"9343_CR45","doi-asserted-by":"crossref","unstructured":"Wang X, Kong T, Shen C, Jiang Y, Li L (2020) Solo: segmenting objects by locations. In: European conference on computer vision, pp 649\u2013665. Springer","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"9343_CR46","doi-asserted-by":"crossref","unstructured":"Wang G, Shen J, Yue M, Ma Y, Wu S (2022) A computational study of empty space ratios in Chinese landscape painting, pp 618\u20132011","DOI":"10.1162\/leon_a_02105"},{"key":"9343_CR47","doi-asserted-by":"crossref","unstructured":"Xiao T, Liu Y, Zhou B, Jiang Y, Sun J (2018) Unified perceptual parsing for scene understanding. In: Proceedings of the European conference on computer vision (ECCV), pp 418\u2013434","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"9343_CR48","doi-asserted-by":"crossref","unstructured":"Xiao T, Liu Y, Zhou B, Jiang Y, Sun J (2018) Unified perceptual parsing for scene understanding. In: Proceedings of the European conference on computer vision (ECCV), pp 418\u2013434","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"9343_CR49","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) Segformer: Simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 34:12077\u201312090","journal-title":"Adv Neural Inf Process Syst"},{"key":"9343_CR50","doi-asserted-by":"crossref","unstructured":"Xue A (2021) End-to-end chinese landscape painting creation using generative adversarial networks. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 3863\u20133871","DOI":"10.1109\/WACV48630.2021.00391"},{"key":"9343_CR51","doi-asserted-by":"crossref","unstructured":"Xu J, Xiong Z, Bhattacharyya SP (2023) Pidnet: a real-time semantic segmentation network inspired by pid controllers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 19529\u20131953","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"9343_CR52","doi-asserted-by":"crossref","unstructured":"Yang D, Ye X, Guo B (2021) Application of multitask joint sparse representation algorithm in chinese painting image classification. Complexity","DOI":"10.1155\/2021\/5546338"},{"key":"9343_CR53","doi-asserted-by":"crossref","unstructured":"Yin R, Monson E, Honig E, Daubechies I, Maggioni M (2016) Object recognition in art drawings: transfer of a neural network. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2299\u20132303. IEEE","DOI":"10.1109\/ICASSP.2016.7472087"},{"key":"9343_CR54","doi-asserted-by":"crossref","unstructured":"Yuan Y, Chen X, Wang J (2020) Object-contextual representations for semantic segmentation. In: European conference on computer vision, pp 173\u2013190. Springer","DOI":"10.1007\/978-3-030-58539-6_11"},{"issue":"1","key":"9343_CR55","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s00530-019-00627-7","volume":"26","author":"J Zhang","year":"2020","unstructured":"Zhang J, Zhou Y, Xia K, Jiang Y, Liu Y (2020) A novel automatic image segmentation method for chinese literati paintings using multi-view fuzzy clustering technology. Multimedia Syst 26(1):37\u201351","journal-title":"Multimedia Syst"},{"key":"9343_CR56","first-page":"10326","volume":"34","author":"W Zhang","year":"2021","unstructured":"Zhang W, Pang J, Chen K, Loy CC (2021) K-net: toward unified image segmentation. Adv Neural Inf Process Syst 34:10326\u201310338","journal-title":"Adv Neural Inf Process Syst"},{"key":"9343_CR57","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"issue":"23","key":"9343_CR58","doi-asserted-by":"publisher","first-page":"16441","DOI":"10.1007\/s11042-019-7476-9","volume":"79","author":"P Zhou","year":"2020","unstructured":"Zhou P, Li K, Wei W, Wang Z, Zhou M (2020) Fast generation method of 3d scene in Chinese landscape painting. Multimed Tools Appl 79(23):16441\u201316457","journal-title":"Multimed Tools Appl"},{"key":"9343_CR59","doi-asserted-by":"crossref","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2019) Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging","DOI":"10.1109\/TMI.2019.2959609"},{"key":"9343_CR60","doi-asserted-by":"crossref","unstructured":"Zhou B, Zhao H, Puig X, Fidler S, Barriuso A, Torralba A (2017) Scene parsing through ade20k dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 633\u2013641","DOI":"10.1109\/CVPR.2017.544"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09343-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09343-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09343-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T21:57:21Z","timestamp":1709935041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09343-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,27]]},"references-count":60,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9343"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09343-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,27]]},"assertion":[{"value":"25 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2023","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 declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}