{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T11:10:43Z","timestamp":1762254643104,"version":"3.37.3"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T00:00:00Z","timestamp":1692057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T00:00:00Z","timestamp":1692057600000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08923-0","type":"journal-article","created":{"date-parts":[[2023,8,15]],"date-time":"2023-08-15T14:02:43Z","timestamp":1692108163000},"page":"22621-22636","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cross-region building counting in satellite imagery using counting consistency"],"prefix":"10.1007","volume":"35","author":[{"given":"Muaaz","family":"Zakria","sequence":"first","affiliation":[]},{"given":"Hamza","family":"Rawal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9322-0728","authenticated-orcid":false,"given":"Waqas","family":"Sultani","sequence":"additional","affiliation":[]},{"given":"Mohsen","family":"Ali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,15]]},"reference":[{"key":"8923_CR1","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.isprsjprs.2020.02.002","volume":"162","author":"MU Ali","year":"2020","unstructured":"Ali MU, Sultani W, Ali M (2020) Destruction from sky: Weakly supervised approach for destruction detection in satellite imagery. ISPRS J Photogramm Remote Sens 162:115\u2013124","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"11","key":"8923_CR2","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.3390\/rs11111369","volume":"11","author":"B Benjdira","year":"2019","unstructured":"Benjdira B, Bazi Y, Koubaa A, Ouni K (2019) Unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images. Remote Sens 11(11):1369","journal-title":"Remote Sens"},{"key":"8923_CR3","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neucom.2015.12.114","volume":"192","author":"A De Myttenaere","year":"2016","unstructured":"De Myttenaere A, Golden B, Le Grand B, Rossi F (2016) Mean absolute percentage error for regression models. Neurocomputing 192:38\u201348","journal-title":"Neurocomputing"},{"key":"8923_CR4","unstructured":"Ganin Y, Lempitsky V(2015) Unsupervised domain adaptation by backpropagation. In: International conference on machine learning. PMLR, pp 1180\u20131189"},{"key":"8923_CR5","doi-asserted-by":"crossref","unstructured":"Guerrero-G\u00f3mez-Olmedo R, Torre-Jim\u00e9nez B, L\u00f3pez-Sastre R, Maldonado-Basc\u00f3n S, Onoro-Rubio D(2015) Extremely overlapping vehicle counting. In: Iberian conference on pattern recognition and image analysis. Springer, Berlin, pp 423\u2013431","DOI":"10.1007\/978-3-319-19390-8_48"},{"issue":"10","key":"8923_CR6","doi-asserted-by":"publisher","first-page":"2071","DOI":"10.1080\/01431160110075901","volume":"23","author":"JT Harvey","year":"2002","unstructured":"Harvey JT (2002) Estimating census district populations from satellite imagery: some approaches and limitations. Int J Remote Sens 23(10):2071\u20132095","journal-title":"Int J Remote Sens"},{"key":"8923_CR7","doi-asserted-by":"crossref","unstructured":"Hossain MA, Reddy Mahesh KK, Cannons K, Xu Z, Wang Y (2020) Domain adaptation in crowd counting. In: 2020 17th conference on computer and robot vision (CRV). IEEE, pp 150\u2013157","DOI":"10.1109\/CRV50864.2020.00028"},{"key":"8923_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104191","volume":"111","author":"H Hou","year":"2021","unstructured":"Hou H, Zhou Y, Zhao J, Yao R, Chen Y, Zheng Y, El Saddik A (2021) Unsupervised cross-domain person re-identification with self-attention and joint-flexible optimization. Image Vis Comput 111:104191","journal-title":"Image Vis Comput"},{"key":"8923_CR9","doi-asserted-by":"crossref","unstructured":"Iqbal J, Ali M (2020) MLSL: multi-level self-supervised learning for domain adaptation with spatially independent and semantically consistent labeling. In: The IEEE winter conference on applications of computer vision, pp 1864\u20131873","DOI":"10.1109\/WACV45572.2020.9093626"},{"key":"8923_CR10","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.isprsjprs.2020.07.001","volume":"167","author":"J Iqbal","year":"2020","unstructured":"Iqbal J, Ali M (2020) Weakly-supervised domain adaptation for built-up region segmentation in aerial and satellite imagery. ISPRS J Photogramm Remote Sens 167:263\u2013275","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"8923_CR11","unstructured":"ISPRS. 2d semantic labelling contest. https:\/\/www2.isprs.org\/commissions\/comm2\/wg4\/benchmark\/semantic-labeling\/. Accessed 24 Jan 2020, 11:30 AM"},{"issue":"6301","key":"8923_CR12","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1126\/science.aaf7894","volume":"353","author":"N Jean","year":"2016","unstructured":"Jean N, Burke M, Michael Xie W, Davis M, Lobell DB, Ermon S (2016) Combining satellite imagery and machine learning to predict poverty. Science 353(6301):790\u2013794","journal-title":"Science"},{"key":"8923_CR13","unstructured":"Kang D, Chan A(2018) Crowd counting by adaptively fusing predictions from an image pyramid. arXiv preprint arXiv:1805.06115"},{"key":"8923_CR14","unstructured":"Korda N, Szorenyi B, Li S (2016) Distributed clustering of linear bandits in peer to peer networks. In: International conference on machine learning. PMLR, pp 1301\u20131309"},{"key":"8923_CR15","unstructured":"Lam D, Kuzma R, McGee K, Dooley S, Laielli M, Klaric M, Bulatov Y, McCord B (2018) xview: objects in context in overhead imagery. arXiv preprint arXiv:1802.07856"},{"issue":"8","key":"8923_CR16","doi-asserted-by":"publisher","first-page":"947","DOI":"10.14358\/PERS.71.8.947","volume":"71","author":"G Li","year":"2005","unstructured":"Li G, Weng Q (2005) Using landsat ETM+ imagery to measure population density in Indianapolis, Indiana, USA. Photogram Eng Remote Sens 71(8):947\u2013958","journal-title":"Photogram Eng Remote Sens"},{"key":"8923_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.103065","volume":"201","author":"H Li","year":"2020","unstructured":"Li H, Kong W, Zhang S (2020) Effective crowd counting using multi-resolution context and image quality assessment-guided training. Comput Vis Image Underst 201:103065","journal-title":"Comput Vis Image Underst"},{"key":"8923_CR18","doi-asserted-by":"crossref","unstructured":"Li M, Zhang Z, Huang K, Tan T (2008) Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In: 2008 19th international conference on pattern recognition. IEEE, pp 1\u20134","DOI":"10.1109\/ICPR.2008.4761705"},{"key":"8923_CR19","unstructured":"Li S, Kar P(2015) Context-aware bandits. arXiv preprint arXiv:1510.03164"},{"key":"8923_CR20","doi-asserted-by":"crossref","unstructured":"Li S, Karatzoglou A, Gentile C (2016) Collaborative filtering bandits. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, pp 539\u2013548","DOI":"10.1145\/2911451.2911548"},{"key":"8923_CR21","doi-asserted-by":"crossref","unstructured":"Li W, Yongbo L, Xiangyang X (2019) Coda: counting objects via scale-aware adversarial density adaption. In: 2019 IEEE international conference on multimedia and expo (ICME). IEEE, pp 193\u2013198","DOI":"10.1109\/ICME.2019.00041"},{"issue":"5","key":"8923_CR22","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1109\/TPAMI.2018.2832198","volume":"41","author":"J Liang","year":"2018","unstructured":"Liang J, He R, Sun Z, Tan T (2018) Aggregating randomized clustering-promoting invariant projections for domain adaptation. IEEE Trans Pattern Anal Mach Intell 41(5):1027\u20131042","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8923_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.106996","volume":"96","author":"J Liang","year":"2019","unstructured":"Liang J, He R, Sun Z, Tan T (2019) Exploring uncertainty in pseudo-label guided unsupervised domain adaptation. Pattern Recogn 96:106996","journal-title":"Pattern Recogn"},{"issue":"6","key":"8923_CR24","doi-asserted-by":"publisher","first-page":"4279","DOI":"10.1109\/TGRS.2019.2962039","volume":"58","author":"W Liu","year":"2020","unstructured":"Liu W, Qin R (2020) A multikernel domain adaptation method for unsupervised transfer learning on cross-source and cross-region remote sensing data classification. IEEE Trans Geosci Remote Sens 58(6):4279\u20134289","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8923_CR25","doi-asserted-by":"crossref","unstructured":"Liu W, Salzmann M, Fua P (2019) Context-aware crowd counting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5099\u20135108","DOI":"10.1109\/CVPR.2019.00524"},{"key":"8923_CR26","doi-asserted-by":"crossref","unstructured":"Liu X, Van De Weijer J, Bagdanov AD (2018) Leveraging unlabeled data for crowd counting by learning to rank. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7661\u20137669","DOI":"10.1109\/CVPR.2018.00799"},{"key":"8923_CR27","doi-asserted-by":"crossref","unstructured":"Liu X, Chen SW, Aditya S, Sivakumar N, Dcunha S, Qu C, Taylor CJ, Das J, Kumar V (2018) Robust fruit counting: combining deep learning, tracking, and structure from motion. In: 2018 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 1045\u20131052","DOI":"10.1109\/IROS.2018.8594239"},{"key":"8923_CR28","doi-asserted-by":"crossref","unstructured":"Mahadik K, Wu Q, Li S, Sabne A (2020) Fast distributed bandits for online recommendation systems. In: Proceedings of the 34th ACM international conference on supercomputing, pp 1\u201313","DOI":"10.1145\/3392717.3392748"},{"key":"8923_CR29","doi-asserted-by":"crossref","unstructured":"Marsden M, McGuinness K, Little S, Keogh CE, O\u2019Connor NE (2018) People, penguins and petri dishes: Adapting object counting models to new visual domains and object types without forgetting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8070\u20138079","DOI":"10.1109\/CVPR.2018.00842"},{"key":"8923_CR30","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.cviu.2017.06.002","volume":"162","author":"AS Mozafari","year":"2017","unstructured":"Mozafari AS, Jamzad M (2017) Cluster-based adaptive SVM: a latent subdomains discovery method for domain adaptation problems. Comput Vis Image Underst 162:116\u2013134","journal-title":"Comput Vis Image Underst"},{"key":"8923_CR31","doi-asserted-by":"crossref","unstructured":"Onoro-Rubio D, L\u00f3pez-Sastre RJ (2016) Towards perspective-free object counting with deep learning. In: European conference on computer vision. Springer, Berlin, pp 615\u2013629","DOI":"10.1007\/978-3-319-46478-7_38"},{"issue":"9","key":"8923_CR32","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.14358\/PERS.69.9.1031","volume":"69","author":"F Qiu","year":"2003","unstructured":"Qiu F, Woller KL, Briggs R (2003) Modeling urban population growth from remotely sensed imagery and tiger GIS road data. Photogram Eng Remote Sens 69(9):1031\u20131042","journal-title":"Photogram Eng Remote Sens"},{"key":"8923_CR33","doi-asserted-by":"crossref","unstructured":"Rabaud V, Belongie S (2006) Counting crowded moving objects. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201906). IEEE, vol\u00a01, pp 705\u2013711","DOI":"10.1109\/CVPR.2006.92"},{"issue":"4","key":"8923_CR34","doi-asserted-by":"publisher","first-page":"905","DOI":"10.3390\/s17040905","volume":"17","author":"M Rahnemoonfar","year":"2017","unstructured":"Rahnemoonfar M, Sheppard C (2017) Deep count: fruit counting based on deep simulated learning. Sensors 17(4):905","journal-title":"Sensors"},{"key":"8923_CR35","doi-asserted-by":"crossref","unstructured":"Saeedi P, Zwick H (2008) Automatic building detection in aerial and satellite images. In: 2008 10th international conference on control, automation, robotics and vision. IEEE, pp 623\u2013629","DOI":"10.1109\/ICARCV.2008.4795590"},{"key":"8923_CR36","doi-asserted-by":"crossref","unstructured":"Sam Deepak B, Surya S, Venkatesh BR (2017) Switching convolutional neural network for crowd counting. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 4031\u20134039","DOI":"10.1109\/CVPR.2017.429"},{"issue":"CSCW1","key":"8923_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3449257","volume":"5","author":"S Scepanovic","year":"2021","unstructured":"Scepanovic S, Joglekar S, Law S, Quercia D (2021) Jane Jacobs in the sky: Predicting urban vitality with open satellite data. Proc ACM Hum Comput Interact 5(CSCW1):1\u201325","journal-title":"Proc ACM Hum Comput Interact"},{"key":"8923_CR38","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.isprsjprs.2019.03.014","volume":"151","author":"A Shakeel","year":"2019","unstructured":"Shakeel A, Sultani W, Ali M (2019) Deep built-structure counting in satellite imagery using attention based re-weighting. ISPRS J Photogramm Remote Sens 151:313\u2013321","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"8923_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103166","volume":"204","author":"P Soviany","year":"2021","unstructured":"Soviany P, Ionescu RT, Rota P, Sebe N (2021) Curriculum self-paced learning for cross-domain object detection. Comput Vis Image Underst 204:103166","journal-title":"Comput Vis Image Underst"},{"key":"8923_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.103156","volume":"204","author":"H Su","year":"2021","unstructured":"Su H, Gong S, Zhu X (2021) Multi-perspective cross-class domain adaptation for open logo detection. Comput Vis Image Underst 204:103156","journal-title":"Comput Vis Image Underst"},{"key":"8923_CR41","doi-asserted-by":"crossref","unstructured":"Subhani MN, Ali M (2020) Learning from scale-invariant examples for domain adaptation in semantic segmentation. arXiv preprint arXiv:2007.14449","DOI":"10.1007\/978-3-030-58542-6_18"},{"key":"8923_CR42","doi-asserted-by":"crossref","unstructured":"Sultani W, Chen C, Shah M (2018) Real-world anomaly detection in surveillance videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6479\u20136488","DOI":"10.1109\/CVPR.2018.00678"},{"issue":"10","key":"8923_CR43","doi-asserted-by":"publisher","first-page":"7178","DOI":"10.1109\/TGRS.2020.2980417","volume":"58","author":"SL Onur Tasar","year":"2020","unstructured":"Onur Tasar SL, Happy YT, Alliez P (2020) Colormapgan: unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks. IEEE Trans Geosci Remote Sens 58(10):7178\u20137193","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"8","key":"8923_CR44","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1057\/jors.2014.103","volume":"66","author":"C Tofallis","year":"2015","unstructured":"Tofallis C (2015) A better measure of relative prediction accuracy for model selection and model estimation. J Oper Res Soc 66(8):1352\u20131362","journal-title":"J Oper Res Soc"},{"key":"8923_CR45","doi-asserted-by":"crossref","unstructured":"Tu P, Sebastian T, Doretto G, Krahnstoever N, Rittscher J, Yu T (2008) Unified crowd segmentation. In: European conference on computer vision. Springe, Berlin, pp 691\u2013704","DOI":"10.1007\/978-3-540-88693-8_51"},{"key":"8923_CR46","unstructured":"Van Etten A, Lindenbaum D, Bacastow TM (2018) Spacenet: a remote sensing dataset and challenge series. arXiv preprint arXiv:1807.01232"},{"key":"8923_CR47","doi-asserted-by":"crossref","unstructured":"Wang J, Song Y, Leung T, Rosenberg C, Wang J, Philbin J, Chen B, Wu Y(2014) Learning fine-grained image similarity with deep ranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1386\u20131393","DOI":"10.1109\/CVPR.2014.180"},{"key":"8923_CR48","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.rse.2018.03.007","volume":"210","author":"L Wang","year":"2018","unstructured":"Wang L, Wang S, Zhou Y, Liu W, Hou Y, Zhu J, Wang F (2018) Mapping population density in china between 1990 and 2010 using remote sensing. Remote Sens Environ 210:269\u2013281","journal-title":"Remote Sens Environ"},{"key":"8923_CR49","unstructured":"Wu B, Nevatia R (2005) Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors. In: Tenth IEEE international conference on computer vision (ICCV\u201905). IEEE, vol 1, pp 90\u201397"},{"key":"8923_CR50","doi-asserted-by":"crossref","unstructured":"Xia G-S, Bai X, Ding J, Zhu Z, Belongie S, Luo J, Datcu M, Pelillo M, Zhang L (2018) Dota: a large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3974\u20133983","DOI":"10.1109\/CVPR.2018.00418"},{"key":"8923_CR51","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.cviu.2019.06.001","volume":"186","author":"G-S Xia","year":"2019","unstructured":"Xia G-S, Huang J, Xue N, Qikai L, Zhu X (2019) Geosay: a geometric saliency for extracting buildings in remote sensing images. Comput Vis Image Underst 186:37\u201347","journal-title":"Comput Vis Image Underst"},{"key":"8923_CR52","doi-asserted-by":"crossref","unstructured":"Xiong F, Shi X, Yeung D-Y (2017) Spatiotemporal modeling for crowd counting in videos. In: Proceedings of the IEEE international conference on computer vision, pp 5151\u20135159","DOI":"10.1109\/ICCV.2017.551"},{"issue":"1","key":"8923_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-16185-w","volume":"11","author":"C Yeh","year":"2020","unstructured":"Yeh C, Perez A, Driscoll A, Azzari G, Tang Z, Lobell D, Ermon S, Burke M (2020) Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nat Commun 11(1):1\u201311","journal-title":"Nat Commun"},{"key":"8923_CR54","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.isprsjprs.2020.04.002","volume":"164","author":"L Zabawa","year":"2020","unstructured":"Zabawa L, Kicherer A, Klingbeil L, T\u00f6pfer R, Kuhlmann H, Roscher R (2020) Counting of grapevine berries in images via semantic segmentation using convolutional neural networks. ISPRS J Photogramm Remote Sens 164:73\u201383","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"11","key":"8923_CR55","doi-asserted-by":"publisher","first-page":"7920","DOI":"10.1109\/TGRS.2020.2985072","volume":"58","author":"J Zhang","year":"2020","unstructured":"Zhang J, Liu J, Pan B, Shi Z (2020) Domain adaptation based on correlation subspace dynamic distribution alignment for remote sensing image scene classification. IEEE Trans Geosci Remote Sens 58(11):7920\u20137930","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"8923_CR56","doi-asserted-by":"crossref","unstructured":"Zhang S, Wu G, Costeira JP, Moura JMF (2017) FCN-RLSTM: deep spatio-temporal neural networks for vehicle counting in city cameras. In: Proceedings of the IEEE international conference on computer vision, pp 3667\u20133676","DOI":"10.1109\/ICCV.2017.396"},{"key":"8923_CR57","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhou D, Chen S, Gao S, Ma Y (2016) Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 589\u2013597","DOI":"10.1109\/CVPR.2016.70"},{"key":"8923_CR58","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.isprsjprs.2020.07.002","volume":"167","author":"J Zheng","year":"2020","unstructured":"Zheng J, Haohuan F, Li W, Wenzhao W, Zhao Y, Dong R, Le Yu (2020) Cross-regional oil palm tree counting and detection via a multi-level attention domain adaptation network. ISPRS J Photogramm Remote Sens 167:154\u2013177","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"8923_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104137","volume":"108","author":"Q Zhou","year":"2021","unstructured":"Zhou Q, Wang S et al (2021) Cluster adaptation networks for unsupervised domain adaptation. Image Vis Comput 108:104137","journal-title":"Image Vis Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08923-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08923-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08923-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T15:11:36Z","timestamp":1694877096000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08923-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,15]]},"references-count":59,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8923"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08923-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,8,15]]},"assertion":[{"value":"28 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 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":"We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. We confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the corresponding author is the sole contact for the Editorial process (including the Editorial Manager and direct communications with the office). He\/she is responsible for communicating with the other authors about progress, submissions of revisions, and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the corresponding author.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}