{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T13:48:12Z","timestamp":1768744092731,"version":"3.49.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T00:00:00Z","timestamp":1701561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T00:00:00Z","timestamp":1701561600000},"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":["U21B2024"],"award-info":[{"award-number":["U21B2024"]}],"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":["61931008"],"award-info":[{"award-number":["61931008"]}],"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":["62071415"],"award-info":[{"award-number":["62071415"]}],"id":[{"id":"10.13039\/501100001809","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":[[2024,3]]},"DOI":"10.1007\/s00521-023-09262-w","type":"journal-article","created":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T11:01:56Z","timestamp":1701601316000},"page":"3433-3446","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic interactive refinement network for camouflaged object detection"],"prefix":"10.1007","volume":"36","author":[{"given":"Yaoqi","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lidong","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiyao","family":"Shou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongfa","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YuHan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4630-3774","authenticated-orcid":false,"given":"Yixiu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenggang","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibing","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,3]]},"reference":[{"issue":"8","key":"9262_CR1","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","volume":"39","author":"D-P Fan","year":"2020","unstructured":"Fan D-P, Zhou T, Ji G-P, Zhou Y, Chen G, Fu H, Shen J, Shao L (2020) Inf-net: automatic covid-19 lung infection segmentation from CT images. IEEE Trans Med Imaging 39(8):2626\u20132637","journal-title":"IEEE Trans Med Imaging"},{"key":"9262_CR2","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1109\/TIP.2021.3058783","volume":"30","author":"Y-H Wu","year":"2021","unstructured":"Wu Y-H, Gao S-H, Mei J, Xu J, Fan D-P, Zhang R-G, Cheng M-M (2021) Jcs: an explainable covid-19 diagnosis system by joint classification and segmentation. IEEE Trans Image Process 30:3113\u20133126","journal-title":"IEEE Trans Image Process"},{"key":"9262_CR3","doi-asserted-by":"crossref","unstructured":"Xie E, Wang W, Wang W, Ding M, Shen C, Luo P (2020) Segmenting transparent objects in the wild, In: Computer vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XIII 16. Springer, pp. 696\u2013711","DOI":"10.1007\/978-3-030-58601-0_41"},{"key":"9262_CR4","doi-asserted-by":"crossref","unstructured":"Fan D-P, Ji G-P, Zhou T, Chen G, Fu H, Shen J, Shao L (2020) Pranet: parallel reverse attention network for polyp segmentation, In: International conference on medical image computing and computer-assisted intervention. Springer, pp. 263\u2013273","DOI":"10.1007\/978-3-030-59725-2_26"},{"issue":"13","key":"9262_CR5","doi-asserted-by":"publisher","first-page":"10375","DOI":"10.1007\/s00521-021-06496-4","volume":"34","author":"A Nogueira-Rodriguez","year":"2022","unstructured":"Nogueira-Rodriguez A, Dominguez-Carbajales R, Campos-Tato F, Herrero J, Puga M, Remedios D, Rivas L, Sanchez E, Iglesias A, Cubiella J, Fdez-Riverola F (2022) Real-time polyp detection model using convolutional neural networks. Neural Comput Appl 34(13):10375\u201310396","journal-title":"Neural Comput Appl"},{"key":"9262_CR6","doi-asserted-by":"crossref","unstructured":"Yu S, Zhang B, Xiao J, Lim EG (2021) Structure-consistent weakly supervised salient object detection with local saliency coherence, In: Proceedings of the AAAI conference on artificial intelligence (AAAI)","DOI":"10.1609\/aaai.v35i4.16434"},{"key":"9262_CR7","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1007\/s00521-020-05081-5","volume":"33","author":"A Kompella","year":"2021","unstructured":"Kompella A, Kulkarni RV (2021) A semi-supervised recurrent neural network for video salient object detection. Neural Comput Appl 33:2065\u20132083","journal-title":"Neural Comput Appl"},{"issue":"3","key":"9262_CR8","first-page":"2311","volume":"35","author":"M Ma","year":"2021","unstructured":"Ma M, Xia C, Li J (2021) Pyramidal feature shrinking for salient object detection. Proceed AAAI Conf Artif Intell 35(3):2311\u20132318","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"9262_CR9","doi-asserted-by":"publisher","first-page":"11789","DOI":"10.1007\/s00521-022-07069-9","volume":"34","author":"Z Wang","year":"2022","unstructured":"Wang Z, Zhang Y, Liu Y, Wang Z, Coleman S, Kerr D (2022) Tf-sod: a novel transformer framework for salient object detection. Neural Comput Appl 34:11789\u201311806","journal-title":"Neural Comput Appl"},{"issue":"4","key":"9262_CR10","first-page":"3004","volume":"35","author":"B Xu","year":"2021","unstructured":"Xu B, Liang H, Liang R, Chen P (2021) Locate globally, segment locally: a progressive architecture with knowledge review network for salient object detection. Proceed AAAI Conf Artif Intell 35(4):3004\u20133012","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"9262_CR11","doi-asserted-by":"publisher","first-page":"7547","DOI":"10.1007\/s00521-021-06845-3","volume":"34","author":"T Chen","year":"2022","unstructured":"Chen T, Hu X, Xiao J, Zhang G, Wang S (2022) Cfidnet: cascaded feature interaction decoder for rgb-d salient object detection\u2019\u2019. Neural Comput Appl 34:7547\u20137563","journal-title":"Neural Comput Appl"},{"key":"9262_CR12","doi-asserted-by":"crossref","unstructured":"Fan D-P, Ji G-P, Sun G, Cheng M-M, Shen J, Shao L (2020) Camouflaged object detection, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 2777\u20132787","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"9262_CR13","doi-asserted-by":"publisher","first-page":"43290","DOI":"10.1109\/ACCESS.2021.3064443","volume":"9","author":"J Yan","year":"2021","unstructured":"Yan J, Le T-N, Nguyen K-D, Tran M-T, Do T-T, Nguyen TV (2021) Mirrornet: bio-inspired camouflaged object segmentation. IEEE Access 9:43290\u201343300","journal-title":"IEEE Access"},{"key":"9262_CR14","unstructured":"Ren J, Hu X, Zhu L, Xu X, Xu Y, Wang W, Deng Z, Heng P-A (2021) Deep texture-aware features for camouflaged object detection, arXiv preprint arXiv:2102.02996"},{"key":"9262_CR15","doi-asserted-by":"crossref","unstructured":"Zhai Q, Li X, Yang F, Chen C, Cheng H, Fan D-P (2021) Mutual graph learning for camouflaged object detection, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 12997\u201313007","DOI":"10.1109\/CVPR46437.2021.01280"},{"key":"9262_CR16","doi-asserted-by":"publisher","first-page":"2050","DOI":"10.1109\/TCYB.2018.2879859","volume":"50","author":"S Chen","year":"2020","unstructured":"Chen S, Wang B, Tan X, Xuelong H (2020) Embedding attention and residual network for accurate salient object detection. IEEE Trans Cybernet 50:2050\u20132062","journal-title":"IEEE Trans Cybernet"},{"key":"9262_CR17","doi-asserted-by":"crossref","unstructured":"Li A, Zhang J, Lv Y, Liu B, Zhang T, Dai Y (2021) Uncertainty-aware joint salient object and camouflaged object detection, In: Proceedings of the 2021 IEEE\/CVF conference on computer vision and pattern recognition, pp. 10071\u201310081","DOI":"10.1109\/CVPR46437.2021.00994"},{"issue":"5","key":"9262_CR18","doi-asserted-by":"publisher","first-page":"5364","DOI":"10.1109\/TIE.2021.3078379","volume":"69","author":"K Wang","year":"2021","unstructured":"Wang K, Bi H, Zhang Y, Zhang C, Liu Z, Zheng S (2021) D 2 c-net: a dual-branch, dual-guidance and cross-refine network for camouflaged object detection. IEEE Trans Ind Electr 69(5):5364\u20135374","journal-title":"IEEE Trans Ind Electr"},{"key":"9262_CR19","unstructured":"Dong B, Zhuge M, Wang Y, Bi H, Chen G (2021) Toward accurate camouflaged object detection with mixture convolution and interactive fusion, arXiv preprint arXiv:2101.05687"},{"key":"9262_CR20","doi-asserted-by":"crossref","unstructured":"Mei H, Ji G-P, Wei Z, Yang X, Wei X, Fan D-P (2021) Camouflaged object segmentation with distraction mining, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 8772\u20138781","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"9262_CR21","unstructured":"Fan D-P, Ji G-P, Cheng M-M, Shao L (2021) Concealed object detection, In: IEEE transactions on pattern analysis and machine intelligence, pp. 1\u20131"},{"issue":"04","key":"9262_CR22","doi-asserted-by":"publisher","first-page":"2050028","DOI":"10.1142\/S021946782050028X","volume":"20","author":"A Mondal","year":"2020","unstructured":"Mondal A (2020) Camouflaged object detection and tracking: a survey. Int J Image Graphics 20(04):2050028","journal-title":"Int J Image Graphics"},{"issue":"3","key":"9262_CR23","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1006\/cviu.2001.0912","volume":"82","author":"A Tankus","year":"2001","unstructured":"Tankus A, Yeshurun Y (2001) Convexity-based visual camouflage breaking. Comput Vis Image Underst 82(3):208\u2013237","journal-title":"Comput Vis Image Underst"},{"key":"9262_CR24","doi-asserted-by":"crossref","unstructured":"Bhajantri NU, Nagabhushan P (2006) Camouflage defect identification: a novel approach, In: 9th International conference on information technology (ICIT\u201906). IEEE, pp. 145\u2013148","DOI":"10.1109\/ICIT.2006.34"},{"issue":"2","key":"9262_CR25","first-page":"1060","volume":"3","author":"C Kavitha","year":"2011","unstructured":"Kavitha C, Rao BP, Govardhan A (2011) An efficient content based image retrieval using color and texture of image sub blocks. Int J Eng Sci Technol (IJEST) 3(2):1060\u20131068","journal-title":"Int J Eng Sci Technol (IJEST)"},{"key":"9262_CR26","doi-asserted-by":"crossref","unstructured":"Siricharoen P, Aramvith S, Chalidabhongse T, Siddhichai S (2010) Robust outdoor human segmentation based on color-based statistical approach and edge combination, In: The 2010 international conference on green circuits and systems. IEEE, pp. 463\u2013468","DOI":"10.1109\/ICGCS.2010.5543017"},{"key":"9262_CR27","unstructured":"Skurowski P, Abdulameer H, B\u0142aszczyk J, Depta T, Kornacki A, Kozie\u0142 P (2018) Animal camouflage analysis: chameleon database, Unpublished Manuscript, 2(6):7"},{"key":"9262_CR28","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cviu.2019.04.006","volume":"184","author":"T-N Le","year":"2019","unstructured":"Le T-N, Nguyen TV, Nie Z, Tran M-T, Sugimoto A (2019) Anabranch network for camouflaged object segmentation. Comput Vis Image Underst 184:45\u201356","journal-title":"Comput Vis Image Underst"},{"issue":"4","key":"9262_CR29","first-page":"3599","volume":"35","author":"J Zhu","year":"2021","unstructured":"Zhu J, Zhang X, Zhang S, Liu J (2021) Inferring camouflaged objects by texture-aware interactive guidance network. Proceed AAAI Conf Artif Intell 35(4):3599\u20133607","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"9262_CR30","doi-asserted-by":"crossref","unstructured":"Li A, Zhang J, Lv Y, Liu B, Zhang T, Dai Y (2021) Uncertainty-aware joint salient object and camouflaged object detection, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 10071\u201310081","DOI":"10.1109\/CVPR46437.2021.00994"},{"key":"9262_CR31","doi-asserted-by":"crossref","unstructured":"Chen S, Tan X, Wang B, Hu X (2018) Reverse attention for salient object detection, In: Proceedings of the European conference on computer vision (ECCV), pp. 234\u2013250","DOI":"10.1007\/978-3-030-01240-3_15"},{"key":"9262_CR32","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation, In: International conference on medical image computing and computer-assisted intervention. Springer, pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9262_CR33","doi-asserted-by":"crossref","unstructured":"Zhang P, Wang D, Lu H, Wang H, Ruan X (2017) Amulet: aggregating multi-level convolutional features for salient object detection, In: Proceedings of the IEEE international conference on computer vision, pp. 202\u2013211","DOI":"10.1109\/ICCV.2017.31"},{"issue":"7","key":"9262_CR34","first-page":"12321","volume":"34","author":"J Wei","year":"2020","unstructured":"Wei J, Wang S, Huang Q (2020) F$$^3$$net: fusion, feedback and focus for salient object detection. Proceed AAAI Conf Art Intell 34(7):12321\u201312328","journal-title":"Proceed AAAI Conf Art Intell"},{"issue":"2","key":"9262_CR35","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","volume":"43","author":"S Gao","year":"2019","unstructured":"Gao S, Cheng M-M, Zhao K, Zhang X-Y, Yang M-H, Torr PH (2019) Res2net: a new multi-scale backbone architecture. IEEE Trans Patt Anal Mach Intell 43(2):652\u2013662","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"9262_CR36","doi-asserted-by":"crossref","unstructured":"Wu Z, Su L, Huang Q (2019) Cascaded partial decoder for fast and accurate salient object detection, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 3907\u20133916","DOI":"10.1109\/CVPR.2019.00403"},{"key":"9262_CR37","doi-asserted-by":"crossref","unstructured":"Chen S, Fu Y (2020) Progressively guided alternate refinement network for rgb-d salient object detection, In: European conference on computer vision. Springer, pp. 520\u2013538","DOI":"10.1007\/978-3-030-58598-3_31"},{"key":"9262_CR38","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3061265","author":"T Wang","year":"2021","unstructured":"Wang T, Zheng Z, Yan C, Zhang J, Sun Y, Zheng B, Yang Y (2021) Each part matters: local patterns facilitate cross-view geo-localization. IEEE Trans Circu Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2021.3061265","journal-title":"IEEE Trans Circu Syst Video Technol"},{"key":"9262_CR39","doi-asserted-by":"crossref","unstructured":"Pang Y, Zhao X, Zhang L, Lu H (2020) Multi-scale interactive network for salient object detection, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9413\u20139422","DOI":"10.1109\/CVPR42600.2020.00943"},{"key":"9262_CR40","doi-asserted-by":"crossref","unstructured":"Qin X, Zhang Z, Huang C, Gao C, Dehghan M, Jagersand M (2019) Basnet: boundary-aware salient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 7479\u20137489","DOI":"10.1109\/CVPR.2019.00766"},{"key":"9262_CR41","doi-asserted-by":"crossref","unstructured":"Luo Z, Mishra A, Achkar A, Eichel J, Li S, Jodoin P-M (2017) Non-local deep features for salient object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6609\u20136617","DOI":"10.1109\/CVPR.2017.698"},{"key":"9262_CR42","doi-asserted-by":"crossref","unstructured":"Wu Z, Su L, Huang Q (2019) Stacked cross refinement network for edge-aware salient object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 7264\u20137273","DOI":"10.1109\/ICCV.2019.00736"},{"key":"9262_CR43","doi-asserted-by":"crossref","unstructured":"Zhao J-X, Liu J-J, Fan D-P, Cao Y, Yang J, Cheng M-M (2019) Egnet: edge guidance network for salient object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 8779\u20138788","DOI":"10.1109\/ICCV.2019.00887"},{"key":"9262_CR44","doi-asserted-by":"crossref","unstructured":"Zhou H, Xie X, Lai J-H, Chen Z, Yang L (2020) Interactive two-stream decoder for accurate and fast saliency detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9141\u20139150","DOI":"10.1109\/CVPR42600.2020.00916"},{"issue":"9","key":"9262_CR45","first-page":"5761","volume":"44","author":"J Zhang","year":"2021","unstructured":"Zhang J, Fan D-P, Dai Y, Anwar S, Saleh F, Aliakbarian S, Barnes N (2021) Uncertainty inspired rgb-d saliency detection. IEEE Trans Patt Anal Mach Intell 44(9):5761\u20135779","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"9262_CR46","doi-asserted-by":"crossref","unstructured":"Sun Y, Chen G, Zhou T, Zhang Y, Liu N (2021) Context-aware cross-level fusion network for camouflaged object detection, arXiv preprint arXiv:2105.12555","DOI":"10.24963\/ijcai.2021\/142"},{"key":"9262_CR47","doi-asserted-by":"crossref","unstructured":"Lv Y, Zhang J, Dai Y, Li A, Liu B, Barnes N, Fan D-P (2021) Simultaneously localize, segment and rank the camouflaged objects, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 11591\u201311601","DOI":"10.1109\/CVPR46437.2021.01142"},{"key":"9262_CR48","doi-asserted-by":"crossref","unstructured":"Perazzi F, Kr\u00e4henb\u00fchl P, Pritch Y, Hornung A 2012 Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on computer vision and pattern recognition. IEEE, pp. 733\u2013740","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"9262_CR49","doi-asserted-by":"crossref","unstructured":"Margolin R, Zelnik-Manor L, Tal A (2014) How to evaluate foreground maps?. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 248\u2013255","DOI":"10.1109\/CVPR.2014.39"},{"key":"9262_CR50","doi-asserted-by":"crossref","unstructured":"Fan D-P, Cheng M-M, Liu Y, Li T, Borji A (2017) Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE international conference on computer vision, pp. 4548\u20134557","DOI":"10.1109\/ICCV.2017.487"},{"key":"9262_CR51","doi-asserted-by":"crossref","unstructured":"Fan D-P, Ji G-P, Qin X, Cheng M-M (2021) Cognitive vision inspired object segmentation metric and loss function, Scientia Sinica Informat, 6(6):","DOI":"10.1360\/SSI-2020-0370"},{"key":"9262_CR52","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8026\u20138037","journal-title":"Adv Neural Inf Process Syst"},{"key":"9262_CR53","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"9262_CR54","unstructured":"Kingma DP, Jimmy B (2014) Adam: a method for stochastic optimization, arXiv preprint arXiv:1412.6980"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09262-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09262-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09262-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T12:06:24Z","timestamp":1707480384000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09262-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,3]]},"references-count":54,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9262"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09262-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,3]]},"assertion":[{"value":"20 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 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 they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}