{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T17:51:30Z","timestamp":1772819490871,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-15753-8","type":"journal-article","created":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T08:02:15Z","timestamp":1690617735000},"page":"21655-21676","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A novel vision-based defect detection method for hot-rolled steel strips via multi-branch network"],"prefix":"10.1007","volume":"83","author":[{"given":"Lei","family":"Yang","sequence":"first","affiliation":[]},{"given":"Xingyu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yanhong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,29]]},"reference":[{"issue":"12","key":"15753_CR1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"15753_CR2","first-page":"1640","volume":"22","author":"T Bo","year":"2017","unstructured":"Bo T, Jianyi K, Shiqian W (2017) Review of surface defect detection based on machine vision. J Image Graphics 22(12):1640\u20131663","journal-title":"J Image Graphics"},{"key":"15753_CR3","first-page":"1","volume":"70","author":"J Cao","year":"2020","unstructured":"Cao J, Yang G, Yang X (2020) A pixel-level segmentation convolutional neural network based on deep feature fusion for surface defect detection. IEEE Trans Instrum Meas 70:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"issue":"3","key":"15753_CR4","doi-asserted-by":"publisher","first-page":"4141","DOI":"10.1007\/s11042-015-3041-3","volume":"76","author":"J Cao","year":"2017","unstructured":"Cao J, Zhang J, Wen Z, Wang N, Liu X (2017) Fabric defect inspection using prior knowledge guided least squares regression. Multimed Tools Appl 76(3):4141\u20134157","journal-title":"Multimed Tools Appl"},{"key":"15753_CR5","doi-asserted-by":"crossref","unstructured":"Chaurasia A, Culurciello E (2017) Linknet: Exploiting encoder representations for efficient semantic segmentation. In Proceedings of IEEE Visual Communications and Image Processing (VCIP), pp 1\u20134. IEEE","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"15753_CR6","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587"},{"key":"15753_CR7","first-page":"100144","volume":"18","author":"T Chen","year":"2020","unstructured":"Chen T, Cai Z, Zhao X, Chen C, Liang X, Zou T, Wang P (2020) Pavement crack detection and recognition using the architecture of segnet. J Ind Inf Integr 18:100144","journal-title":"J Ind Inf Integr"},{"issue":"9","key":"15753_CR8","doi-asserted-by":"publisher","first-page":"8016","DOI":"10.1109\/TIE.2019.2945265","volume":"67","author":"W Choi","year":"2019","unstructured":"Choi W, Cha Y-J (2019) Sddnet: Real-time crack segmentation. IEEE Trans Industr Electron 67(9):8016\u20138025","journal-title":"IEEE Trans Industr Electron"},{"issue":"2","key":"15753_CR9","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1109\/JSEN.2020.3015868","volume":"21","author":"A Choudhary","year":"2020","unstructured":"Choudhary A, Goyal D, Letha SS (2020) Infrared thermography-based fault diagnosis of induction motor bearings using machine learning. IEEE Sens J 21(2):1727\u20131734","journal-title":"IEEE Sens J"},{"key":"15753_CR10","doi-asserted-by":"crossref","unstructured":"Xi D, Qin Y, Wang S (2023) Ydrsnet: An integrated yolov5-deeplabv3+ real-time segmentation network for gear pitting measurement. J Intell Manuf 34:1585\u20131599","DOI":"10.1007\/s10845-021-01876-y"},{"key":"15753_CR11","doi-asserted-by":"crossref","unstructured":"Fan X, Cao P, Shi P, Wang J, Xin Y, Huang W (2021) A nested unet with attention mechanism for road crack image segmentation. In Proceedings of IEEE 6th International Conference on Signal and Image Processing (ICSIP), pp 189\u2013193. IEEE","DOI":"10.1109\/ICSIP52628.2021.9688782"},{"key":"15753_CR12","unstructured":"Florian L-C and Adam SH (2017) Rethinking atrous convolution for semantic image segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE"},{"issue":"23","key":"15753_CR13","doi-asserted-by":"publisher","first-page":"7935","DOI":"10.1109\/JSEN.2017.2761858","volume":"17","author":"J Gan","year":"2017","unstructured":"Gan J, Li Q, Wang J, Haomin Yu (2017) A hierarchical extractor-based visual rail surface inspection system. IEEE Sens J 17(23):7935\u20137944","journal-title":"IEEE Sens J"},{"issue":"1","key":"15753_CR14","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TITS.2016.2568758","volume":"18","author":"X Gibert","year":"2016","unstructured":"Gibert X, Patel VM, Chellappa R (2016) Deep multitask learning for railway track inspection. IEEE Trans Intell Transport Syst 18(1):153\u2013164","journal-title":"IEEE Trans Intell Transport Syst"},{"issue":"9","key":"15753_CR15","doi-asserted-by":"publisher","first-page":"10844","DOI":"10.1109\/JSEN.2021.3059860","volume":"21","author":"R Guo","year":"2021","unstructured":"Guo R, Liu H, Xie G, Zhang Y (2021) Weld defect detection from imbalanced radiographic images based on contrast enhancement conditional generative adversarial network and transfer learning. IEEE Sens J 21(9):10844\u201310853","journal-title":"IEEE Sens J"},{"key":"15753_CR16","doi-asserted-by":"crossref","unstructured":"Hsiel Y-A, Tsai Y-CJ (2021) Dau-net: Dense attention u-net for pavement crack segmentation. In Proceedings of IEEE International Intelligent Transportation Systems Conference (ITSC), pp 2251\u20132256. IEEE","DOI":"10.1109\/ITSC48978.2021.9564806"},{"key":"15753_CR17","doi-asserted-by":"publisher","first-page":"130271","DOI":"10.1016\/j.matlet.2021.130271","volume":"301","author":"Z Huang","year":"2021","unstructured":"Huang Z, Jiajun Wu, Xie F (2021) Automatic surface defect segmentation for hot-rolled steel strip using depth-wise separable u-shape network. Mater Lett 301:130271","journal-title":"Mater Lett"},{"key":"15753_CR18","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1109\/TIP.2020.3036770","volume":"30","author":"B Hu","year":"2020","unstructured":"Hu B, Gao B, Woo WL, Ruan L, Jin J, Yang Y, Yu Y (2020) A lightweight spatial and temporal multi-feature fusion network for defect detection. IEEE Trans Image Process 30:472\u2013486","journal-title":"IEEE Trans Image Process"},{"key":"15753_CR19","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"1","key":"15753_CR20","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/TMM.2012.2225034","volume":"15","author":"N Imamoglu","year":"2012","unstructured":"Imamoglu N, Lin W, Fang Y (2012) A saliency detection model using low-level features based on wavelet transform. IEEE Trans Multimed 15(1):96\u2013105","journal-title":"IEEE Trans Multimed"},{"key":"15753_CR21","doi-asserted-by":"publisher","first-page":"126162","DOI":"10.1016\/j.conbuildmat.2021.126162","volume":"321","author":"N Kheradmandi","year":"2022","unstructured":"Kheradmandi N, Mehranfar V (2022) A critical review and comparative study on image segmentation-based techniques for pavement crack detection. Constr Build Mater 321:126162","journal-title":"Constr Build Mater"},{"key":"15753_CR22","doi-asserted-by":"crossref","unstructured":"Lau SLH, Wang X, Xu Y, and Chong EKP (2020) Automated pavement crack segmentation using fully convolutional u-net with a pretrained resnet-34 encoder. arXiv preprint arXiv:2001.01912","DOI":"10.1109\/ACCESS.2020.3003638"},{"issue":"6","key":"15753_CR23","doi-asserted-by":"publisher","first-page":"7321","DOI":"10.1007\/s11042-018-6483-6","volume":"78","author":"C Li","year":"2019","unstructured":"Li C, Liu C, Gao G, Liu Z, Wang Y (2019) Robust low-rank decomposition of multi-channel feature matrices for fabric defect detection. Multimed Tools Appl 78(6):7321\u20137339","journal-title":"Multimed Tools Appl"},{"key":"15753_CR24","doi-asserted-by":"publisher","first-page":"1822585","DOI":"10.1155\/2022\/1822585","volume":"2022","author":"C Li","year":"2022","unstructured":"Li C, Wen Y, Shi Q, Yang F, Ma H, Tian X (2022) A Pavement Crack Detection Method Based on Multiscale Attention and HFS. Comput Intell Neurosci. 2022:1822585. https:\/\/doi.org\/10.1155\/2022\/1822585","journal-title":"Comput Intell Neurosci."},{"issue":"20","key":"15753_CR25","doi-asserted-by":"publisher","first-page":"23390","DOI":"10.1109\/JSEN.2021.3106057","volume":"21","author":"M Li","year":"2021","unstructured":"Li M, Yao N, Liu S, Li S, Zhao Y, Kong SG (2021) Multisensor image fusion for automated detection of defects in printed circuit boards. IEEE Sens J 21(20):23390\u201323399","journal-title":"IEEE Sens J"},{"issue":"2","key":"15753_CR26","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1109\/TII.2019.2945403","volume":"16","author":"J Lian","year":"2019","unstructured":"Lian J, Jia W, Zareapoor M, Zheng Y, Luo R, Jain DK, Kumar N (2019) Deep-learning-based small surface defect detection via an exaggerated local variation-based generative adversarial network. IEEE Trans Ind Inform 16(2):1343\u20131351","journal-title":"IEEE Trans Ind Inform"},{"issue":"4","key":"15753_CR27","first-page":"1191","volume":"69","author":"Xu Liang","year":"2019","unstructured":"Liang Xu, Haibo Xu, Li X, Pan M (2019) A defect inspection for explosive cartridge using an improved visual attention and image-weighted eigenvalue. IEEE Trans Instrum Meas 69(4):1191\u20131204","journal-title":"IEEE Trans Instrum Meas"},{"key":"15753_CR28","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 (CVPR). IEEE, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"15753_CR29","doi-asserted-by":"publisher","first-page":"102164","DOI":"10.1016\/j.ndteint.2019.102164","volume":"108","author":"Q Luo","year":"2019","unstructured":"Luo Q, Gao B, Woo WL, Yang Y (2019) Temporal and spatial deep learning network for infrared thermal defect detection. Ndt E Int 108:102164","journal-title":"Ndt E Int"},{"issue":"3","key":"15753_CR30","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1109\/TIM.2019.2963555","volume":"69","author":"Q Luo","year":"2020","unstructured":"Luo Q, Fang X, Liu Li, Yang C, Sun Y (2020) Automated visual defect detection for flat steel surface: A survey. IEEE Trans Instrum Meas 69(3):626\u2013644","journal-title":"IEEE Trans Instrum Meas"},{"key":"15753_CR31","first-page":"1","volume":"70","author":"H Miao","year":"2020","unstructured":"Miao H, Zhao Z, Sun C, Li B, Yan R (2020) A u-net-based approach for tool wear area detection and identification. IEEE Trans Instrum Meas 70:1\u201310","journal-title":"IEEE Trans Instrum Meas"},{"key":"15753_CR32","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"issue":"4","key":"15753_CR33","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1109\/TPAMI.2016.2562626","volume":"39","author":"H Peng","year":"2016","unstructured":"Peng H, Li B, Ling H, Hu W, Xiong W, Maybank SJ (2016) Salient object detection via structured matrix decomposition. IEEE Trans Pattern Anal Mach Intell 39(4):818\u2013832","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"15753_CR34","doi-asserted-by":"publisher","first-page":"11710","DOI":"10.1109\/TITS.2021.3106647","volume":"23","author":"Z Qu","year":"2021","unstructured":"Qu Z, Chen W, Wang S-Y, Yi T-M, Liu L (2021) A crack detection algorithm for concrete pavement based on attention mechanism and multi-features fusion. IEEE Trans Intell Transp Syst 23(8):11710\u201311719","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"9","key":"15753_CR35","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput 29(9):2352\u20132449","journal-title":"Neural Comput"},{"key":"15753_CR36","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T 2015 U-net: Convolutional networks for biomedical image segmentation. In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"15753_CR37","doi-asserted-by":"publisher","first-page":"106000","DOI":"10.1016\/j.optlaseng.2019.106000","volume":"128","author":"G Song","year":"2020","unstructured":"Song G, Song K, Yan Y (2020) Saliency detection for strip steel surface defects using multiple constraints and improved texture features. Opt Lasers Eng 128:106000","journal-title":"Opt Lasers Eng"},{"key":"15753_CR38","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","volume":"285","author":"K Song","year":"2013","unstructured":"Song K, Yan Y (2013) A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl Surf Sci 285:858\u2013864","journal-title":"Appl Surf Sci"},{"issue":"1","key":"15753_CR39","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/JSEN.2008.2008409","volume":"9","author":"F Taghibakhsh","year":"2008","unstructured":"Taghibakhsh F, Karim KS, Belev G, Kasap SO (2008) X-ray detection using a two-transistor active pixel sensor array coupled to an a-se x-ray photoconductor. IEEE Sens J 9(1):51\u201356","journal-title":"IEEE Sens J"},{"key":"15753_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3087826","volume":"70","author":"D-M Tsai","year":"2021","unstructured":"Tsai D-M, Fan S-KS, Chou YH (2021) Auto-annotated deep segmentation for surface defect detection. IEEE Trans Instrum Meas 70:1\u201310","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"15753_CR41","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/TII.2019.2917522","volume":"16","author":"J Wang","year":"2019","unstructured":"Wang J, Li Q, Gan J, Haomin Yu, Yang Xi (2019) Surface defect detection via entity sparsity pursuit with intrinsic priors. IEEE Trans Industr Inf 16(1):141\u2013150","journal-title":"IEEE Trans Industr Inf"},{"key":"15753_CR42","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"15753_CR43","doi-asserted-by":"crossref","unstructured":"Xiao X, Lian S, Luo Z, Li S (2018) Weighted res-unet for high-quality retina vessel segmentation. In Proceedings of 9th International Conference on Information Technology in Medicine and Education (ITME), pp 327\u2013331. IEEE","DOI":"10.1109\/ITME.2018.00080"},{"issue":"33","key":"15753_CR44","doi-asserted-by":"publisher","first-page":"23729","DOI":"10.1007\/s11042-020-08976-6","volume":"79","author":"Y Xiao","year":"2020","unstructured":"Xiao Y, Tian Z, Jiachen Yu, Zhang Y, Liu S, Shaoyi Du, Lan X (2020) A review of object detection based on deep learning. Multimed Tools Appl 79(33):23729\u201323791","journal-title":"Multimed Tools Appl"},{"key":"15753_CR45","doi-asserted-by":"crossref","unstructured":"Yahyatabar M, Jouvet P, and Cheriet F (2020) Dense-unet: a light model for lung fields segmentation in chest x-ray images. In Proceedings of 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp 1242\u20131245. IEEE","DOI":"10.1109\/EMBC44109.2020.9176033"},{"issue":"3","key":"15753_CR46","doi-asserted-by":"publisher","first-page":"2220","DOI":"10.1109\/TII.2020.3015765","volume":"17","author":"H Yang","year":"2020","unstructured":"Yang H, Zhou Q, Song K, Yin Z (2020) An anomaly feature-editing-based adversarial network for texture defect visual inspection. IEEE Trans Industr Inf 17(3):2220\u20132230","journal-title":"IEEE Trans Industr Inf"},{"key":"15753_CR47","doi-asserted-by":"publisher","first-page":"108338","DOI":"10.1016\/j.knosys.2022.108338","volume":"242","author":"L Yang","year":"2022","unstructured":"Yang L, Fan J, Huo B, Li En, Liu Y (2022) A nondestructive automatic defect detection method with pixelwise segmentation. Knowl-Based Syst 242:108338","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"15753_CR48","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/s00170-017-0991-9","volume":"94","author":"L Yang","year":"2018","unstructured":"Yang L, Li En, Long T, Fan J, Mao Y, Fang Z, Liang Z (2018) A welding quality detection method for arc welding robot based on 3d reconstruction with sfs algorithm. Int J Adv Manuf Technol 94(1):1209\u20131220","journal-title":"Int J Adv Manuf Technol"},{"key":"15753_CR49","doi-asserted-by":"publisher","first-page":"102435","DOI":"10.1016\/j.ndteint.2021.102435","volume":"120","author":"L Yang","year":"2021","unstructured":"Yang L, Wang H, Huo B, Li F, Liu Y (2021) An automatic welding defect location algorithm based on deep learning. NDT E Int 120:102435","journal-title":"NDT E Int"},{"key":"15753_CR50","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122"},{"issue":"3","key":"15753_CR51","first-page":"656","volume":"68","author":"Yu Haomin","year":"2018","unstructured":"Haomin Yu, Li Q, Tan Y, Gan J, Wang J, Geng Y-a, Jia L (2018) A coarse-to-fine model for rail surface defect detection. IEEE Trans Instrum Meas 68(3):656\u2013666","journal-title":"IEEE Trans Instrum Meas"},{"key":"15753_CR52","doi-asserted-by":"crossref","unstructured":"Yu Z, Wu X, Gu X (2017) Fully convolutional networks for surface defect inspection in industrial environment. In Proceedings of International Conference on Computer Vision Systems, pp 417\u2013426. Springer","DOI":"10.1007\/978-3-319-68345-4_37"},{"issue":"20","key":"15753_CR53","doi-asserted-by":"publisher","first-page":"9364","DOI":"10.1109\/JSEN.2019.2927268","volume":"19","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Gao X, You D, Zhang N (2019) Data-driven detection of laser welding defects based on real-time spectrometer signals. IEEE Sens J 19(20):9364\u20139373","journal-title":"IEEE Sens J"},{"key":"15753_CR54","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 (CVPR). IEEE, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"},{"key":"15753_CR55","doi-asserted-by":"publisher","first-page":"103934","DOI":"10.1016\/j.autcon.2021.103934","volume":"132","author":"S Zhao","year":"2021","unstructured":"Zhao S, Zhang D, Xue Y, Zhou M, Huang H (2021) A deep learning-based approach for refined crack evaluation from shield tunnel lining images. Autom Constr 132:103934","journal-title":"Autom Constr"},{"key":"15753_CR56","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In Proceedings of Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp 3\u201311. Springer","DOI":"10.1007\/978-3-030-00889-5_1"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15753-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15753-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15753-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T10:25:37Z","timestamp":1707992737000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15753-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,29]]},"references-count":56,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["15753"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15753-8","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,29]]},"assertion":[{"value":"25 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2023","order":4,"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"}}]}}