{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:30:24Z","timestamp":1772044224997,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T00:00:00Z","timestamp":1697760000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T00:00:00Z","timestamp":1697760000000},"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":["62072015"],"award-info":[{"award-number":["62072015"]}],"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":["61632006"],"award-info":[{"award-number":["61632006"]}],"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":["61602486"],"award-info":[{"award-number":["61602486"]}],"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":["61876012"],"award-info":[{"award-number":["61876012"]}],"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":["61902053"],"award-info":[{"award-number":["61902053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17346-x","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T03:26:37Z","timestamp":1697772397000},"page":"45207-45240","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["IE-GAN: a data-driven crowd simulation method via generative adversarial networks"],"prefix":"10.1007","volume":"83","author":[{"given":"Xuanqi","family":"Lin","sequence":"first","affiliation":[]},{"given":"Yuchen","family":"Liang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6650-6790","authenticated-orcid":false,"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yongli","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,20]]},"reference":[{"key":"17346_CR1","doi-asserted-by":"publisher","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. https:\/\/doi.org\/10.48550\/arXiv.1411.1784, arXiv:1411.1784","DOI":"10.48550\/arXiv.1411.1784"},{"issue":"5","key":"17346_CR2","doi-asserted-by":"publisher","first-page":"4282","DOI":"10.1103\/PhysRevE.51.4282","volume":"51","author":"D Helbing","year":"1995","unstructured":"Helbing D, Moln\u00e1r P (1995) Social force model for pedestrian dynamics. Phys Rev E 51(5):4282\u20134286. https:\/\/doi.org\/10.1103\/PhysRevE.51.4282","journal-title":"Phys Rev E"},{"key":"17346_CR3","doi-asserted-by":"publisher","unstructured":"Gupta A, Johnson J, Fei-Fei L, et\u00a0al (2018) Social GAN: socially acceptable trajectories with generative adversarial networks. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition. IEEE, Salt Lake City, UT, pp 2255\u20132264, https:\/\/doi.org\/10.1109\/CVPR.2018.00240","DOI":"10.1109\/CVPR.2018.00240"},{"key":"17346_CR4","doi-asserted-by":"publisher","unstructured":"Sadeghian A, Kosaraju V, Sadeghian A, et\u00a0al (2019) SoPhie: an attentive gan for predicting paths compliant to social and physical constraints. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). IEEE, Long Beach, CA, USA, pp 1349\u20131358, https:\/\/doi.org\/10.1109\/CVPR.2019.00144","DOI":"10.1109\/CVPR.2019.00144"},{"key":"17346_CR5","doi-asserted-by":"publisher","unstructured":"Amirian J, Hayet JB, Pettre J (2019) Social ways: learning multi-modal distributions of pedestrian trajectories with GANs. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW). IEEE, Long Beach, CA, USA, pp 2964\u20132972, https:\/\/doi.org\/10.1109\/CVPRW.2019.00359","DOI":"10.1109\/CVPRW.2019.00359"},{"key":"17346_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-3-031-19772-7_30","volume-title":"Computer vision - ECCV 2022","author":"P Xu","year":"2022","unstructured":"Xu P, Hayet JB, Karamouzas I (2022) SocialVAE: human trajectory prediction using timewise latents. In: Avidan S, Brostow G, Ciss\u00e9 M et al (eds) Computer vision - ECCV 2022. Lecture Notes in Computer Science. Springer Nature Switzerland, Cham, pp 511\u2013528. https:\/\/doi.org\/10.1007\/978-3-031-19772-7_30"},{"key":"17346_CR7","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1007\/978-981-16-1249-7_52","volume-title":"Soft computing and signal processing","author":"A Shah","year":"2022","unstructured":"Shah A, Chavan P, Jadhav D (2022) Convolutional neural network-based image segmentation techniques. In: Reddy VS, Prasad VK, ang J et al (eds) Soft computing and signal processing. Advances in Intelligent Systems and Computing. Springer, Singapore, pp 553\u2013561. https:\/\/doi.org\/10.1007\/978-981-16-1249-7_52"},{"issue":"25","key":"17346_CR8","doi-asserted-by":"publisher","first-page":"36,699","DOI":"10.1007\/s11042-021-11604-6","volume":"81","author":"N Balani","year":"2022","unstructured":"Balani N, Chavan P, Ghonghe M (2022) Design of high-speed blockchain-based sidechaining peer to peer communication protocol over 5G networks. Multimed Tools Appl 81(25):36,699-36,713. https:\/\/doi.org\/10.1007\/s11042-021-11604-6","journal-title":"Multimed Tools Appl"},{"key":"17346_CR9","doi-asserted-by":"publisher","unstructured":"Mohamed A, Qian K, Elhoseiny M, et\u00a0al (2020) Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). IEEE, Seattle, WA, USA, pp 14,412\u201314,420, https:\/\/doi.org\/10.1109\/CVPR42600.2020.01443","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"17346_CR10","doi-asserted-by":"publisher","unstructured":"Shi L, Wang L, Long C, et\u00a0al (2021) SGCN:sparse graph convolution network for pedestrian trajectory prediction. In: 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). IEEE, Nashville, TN, USA, pp 8990\u20138999, https:\/\/doi.org\/10.1109\/CVPR46437.2021.00888","DOI":"10.1109\/CVPR46437.2021.00888"},{"key":"17346_CR11","doi-asserted-by":"publisher","unstructured":"Lee KH, Choi MG, Hong Q, et\u00a0al (2007) Group behavior from video: a data-driven approach to crowd simulation. In: Proceedings of the 2007 ACM SIGGRAPH\/Eurographics symposium on computer animation. Eurographics Association, Goslar, DEU, SCA \u201907, pp 109\u2013118, https:\/\/doi.org\/10.1145\/1272690.1272706","DOI":"10.1145\/1272690.1272706"},{"issue":"3","key":"17346_CR12","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1111\/j.1467-8659.2007.01089.x","volume":"26","author":"A Lerner","year":"2007","unstructured":"Lerner A, Chrysanthou Y, Lischinski D (2007) Crowds by Example. Comput Graph. Forum 26(3):655\u2013664. https:\/\/doi.org\/10.1111\/j.1467-8659.2007.01089.x","journal-title":"Comput Graph. Forum"},{"key":"17346_CR13","doi-asserted-by":"publisher","unstructured":"Alahi A, Goel K, Ramanathan V, et\u00a0al (2016) Social LSTM: human trajectory prediction in crowded spaces. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Las Vegas, NV, USA, pp 961\u2013971, https:\/\/doi.org\/10.1109\/CVPR.2016.110","DOI":"10.1109\/CVPR.2016.110"},{"key":"17346_CR14","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.neunet.2018.09.002","volume":"108","author":"T Fernando","year":"2018","unstructured":"Fernando T, Denman S, Sridharan S et al (2018) Soft + Hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection. Neural Netw 108:466\u2013478. https:\/\/doi.org\/10.1016\/j.neunet.2018.09.002","journal-title":"Neural Netw"},{"key":"17346_CR15","doi-asserted-by":"publisher","unstructured":"Hug R, Becker S, H\u00fcbner W, et\u00a0al (2018) Particle-based pedestrian path prediction using LSTM-MDL models. In: 2018 21st international conference on intelligent transportation systems (ITSC), pp 2684\u20132691, https:\/\/doi.org\/10.1109\/ITSC.2018.8569478, iSSN: 2153-0017","DOI":"10.1109\/ITSC.2018.8569478"},{"key":"17346_CR16","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.neucom.2018.05.022","volume":"310","author":"X Wei","year":"2018","unstructured":"Wei X, Lu W, Zhu L et al (2018) Learning motion rules from real data: neural network for crowd simulation. Neurocomputing 310:125\u2013134. https:\/\/doi.org\/10.1016\/j.neucom.2018.05.022","journal-title":"Neurocomputing"},{"key":"17346_CR17","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.simpat.2018.02.007","volume":"84","author":"B Liu","year":"2018","unstructured":"Liu B, Liu H, Zhang H et al (2018) A social force evacuation model driven by video data. Simul Model Pract Theory 84:190\u2013203. https:\/\/doi.org\/10.1016\/j.simpat.2018.02.007","journal-title":"Simul Model Pract Theory"},{"key":"17346_CR18","doi-asserted-by":"publisher","unstructured":"Zhao M, Turner SJ, Cai W (2013) A data-driven crowd simulation model based on clustering and classification. In: 2013 IEEE\/ACM 17th international symposium on distributed simulation and real time applications, pp 125\u2013134, https:\/\/doi.org\/10.1109\/DS-RT.2013.21, iSSN: 1550-6525","DOI":"10.1109\/DS-RT.2013.21"},{"key":"17346_CR19","doi-asserted-by":"publisher","unstructured":"Zhang G, Yu Z, Jin D, et\u00a0al (2022) Physics-infused machine learning for crowd simulation. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. Association for Computing Machinery, New York, NY, USA, KDD \u201922, pp 2439\u20132449, https:\/\/doi.org\/10.1145\/3534678.3539440","DOI":"10.1145\/3534678.3539440"},{"key":"17346_CR20","doi-asserted-by":"publisher","unstructured":"Amirian J, van Toll W, Hayet JB, et\u00a0al (2019) Data-driven crowd simulation with generative adversarial networks. In: Proceedings of the 32nd international conference on computer animation and social agents. Association for Computing Machinery, New York, NY, USA, CASA \u201919, pp 7\u201310, https:\/\/doi.org\/10.1145\/3328756.3328769","DOI":"10.1145\/3328756.3328769"},{"issue":"12","key":"17346_CR21","doi-asserted-by":"publisher","first-page":"24,609","DOI":"10.1109\/TITS.2022.3193442","volume":"23","author":"SM Pang","year":"2022","unstructured":"Pang SM, Cao JX, Jian MY et al (2022) BR-GAN: a pedestrian trajectory prediction model combined with behavior recognition. IEEE Trans Intell Trans Syst 23(12):24,609-24,620. https:\/\/doi.org\/10.1109\/TITS.2022.3193442","journal-title":"IEEE Trans Intell Trans Syst"},{"key":"17346_CR22","doi-asserted-by":"publisher","unstructured":"Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: 2009 IEEE conference on computer vision and pattern recognition, pp 935\u2013942, https:\/\/doi.org\/10.1109\/CVPR.2009.5206641, iSSN: 1063-6919","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"17346_CR23","doi-asserted-by":"publisher","unstructured":"Luber M, Stork JA, Tipaldi GD, et\u00a0al (2010) People tracking with human motion predictions from social forces. In: 2010 IEEE international conference on robotics and automation, pp 464\u2013469, https:\/\/doi.org\/10.1109\/ROBOT.2010.5509779, iSSN: 1050-4729","DOI":"10.1109\/ROBOT.2010.5509779"},{"key":"17346_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1007\/978-3-642-15549-9_33","volume-title":"Computer vision - ECCV 2010","author":"S Pellegrini","year":"2010","unstructured":"Pellegrini S, Ess A, Van Gool L (2010) Improving data association by joint modeling of pedestrian trajectories and groupings. In: Daniilidis K, Maragos P, Paragios N (eds) Computer vision - ECCV 2010. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp 452\u2013465. https:\/\/doi.org\/10.1007\/978-3-642-15549-9_33"},{"key":"17346_CR25","doi-asserted-by":"publisher","unstructured":"Yamaguchi K, Berg AC, Ortiz LE et al (2011) Who are you with and where are you going? In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1345\u20131352. https:\/\/doi.org\/10.1109\/CVPR.2011.5995468","DOI":"10.1109\/CVPR.2011.5995468"},{"issue":"3","key":"17346_CR26","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1145\/1141911.1142008","volume":"25","author":"A Treuille","year":"2006","unstructured":"Treuille A, Cooper S, Popovi\u0107 Z (2006) Continuum crowds. ACM Trans Graph 25(3):1160\u20131168. https:\/\/doi.org\/10.1145\/1141911.1142008","journal-title":"ACM Trans Graph"},{"issue":"8","key":"17346_CR27","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1016\/j.trb.2005.09.006","volume":"40","author":"G Antonini","year":"2006","unstructured":"Antonini G, Bierlaire M, Weber M (2006) Discrete choice models of pedestrian walking behavior. Transport Res B-METH 40(8):667\u2013687. https:\/\/doi.org\/10.1016\/j.trb.2005.09.006","journal-title":"Transport Res B-METH"},{"issue":"104","key":"17346_CR28","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.engappai.2021.104210","volume":"100","author":"M Rostami","year":"2021","unstructured":"Rostami M, Berahmand K, Nasiri E et al (2021) Review of swarm intelligence-based feature selection methods. Eng Appl Artif Intell 100(104):210. https:\/\/doi.org\/10.1016\/j.engappai.2021.104210","journal-title":"Eng Appl Artif Intell"},{"issue":"110","key":"17346_CR29","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.knosys.2023.110521","volume":"269","author":"R Sheikhpour","year":"2023","unstructured":"Sheikhpour R, Berahmand K, Forouzandeh S (2023) Hessian-based semi-supervised feature selection using generalized uncorrelated constraint. Knowl Based Syst 269(110):521. https:\/\/doi.org\/10.1016\/j.knosys.2023.110521","journal-title":"Knowl Based Syst"},{"key":"17346_CR30","unstructured":"Xu K, Ba J, Kiros R, et\u00a0al (2015) Show, attend and tell: neural image caption generation with visual attention. In: Proceedings of the 32nd international conference on machine learning. PMLR, pp 2048\u20132057"},{"issue":"1","key":"17346_CR31","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1609\/aaai.v36i1.19933","volume":"36","author":"J Duan","year":"2022","unstructured":"Duan J, Wang L, Long C et al (2022) Complementary attention gated network for pedestrian trajectory prediction. Proc AAAI Conf Artif Intell 36(1):542\u2013550. https:\/\/doi.org\/10.1609\/aaai.v36i1.19933","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"11","key":"17346_CR32","doi-asserted-by":"publisher","first-page":"20,046","DOI":"10.1109\/TITS.2022.3170874","volume":"23","author":"K Chen","year":"2022","unstructured":"Chen K, Song X, Yuan H et al (2022) Fully convolutional encoder-decoder with an attention mechanism for practical pedestrian trajectory prediction. IEEE trans Intell Transp Syst 23(11):20,046-20,060. https:\/\/doi.org\/10.1109\/TITS.2022.3170874","journal-title":"IEEE trans Intell Transp Syst"},{"issue":"10","key":"17346_CR33","doi-asserted-by":"publisher","first-page":"11,434","DOI":"10.1007\/s10489-021-02997-w","volume":"52","author":"L Zhou","year":"2022","unstructured":"Zhou L, Zhao Y, Yang D et al (2022) GCHGAT: pedestrian trajectory prediction using group constrained hierarchical graph attention networks. Appl Intell 52(10):11,434-11,447. https:\/\/doi.org\/10.1007\/s10489-021-02997-w","journal-title":"Appl Intell"},{"key":"17346_CR34","doi-asserted-by":"publisher","unstructured":"Lv P, Wang W, Wang Y et al (2023) Ssagcn: social soft attention graph convolution network for pedestrian trajectory prediction. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3250485","DOI":"10.1109\/TNNLS.2023.3250485"},{"issue":"11","key":"17346_CR35","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M et al (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144. https:\/\/doi.org\/10.1145\/3422622","journal-title":"Commun ACM"},{"key":"17346_CR36","doi-asserted-by":"publisher","unstructured":"Graves A (2014) Generating Sequences With Recurrent Neural Networks. https:\/\/doi.org\/10.48550\/arXiv.1308.0850, arXiv:1308.0850","DOI":"10.48550\/arXiv.1308.0850"},{"key":"17346_CR37","doi-asserted-by":"publisher","unstructured":"Graves A, Jaitly N (2014) Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of the 31st international conference on machine learning. PMLR, pp 1764\u20131772, https:\/\/doi.org\/10.5555\/3044805.3045089","DOI":"10.5555\/3044805.3045089"},{"key":"17346_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1007\/978-3-319-10599-4_40","volume-title":"Computer vision- ECCV 2014","author":"JFP Kooij","year":"2014","unstructured":"Kooij JFP, Schneider N, Flohr F et al (2014) Context-based pedestrian path prediction. In: Fleet D, Pajdla T, Schiele B et al (eds) Computer vision- ECCV 2014. Lecture Notes in Computer Science. Springer International Publishing, Cham, pp 618\u2013633. https:\/\/doi.org\/10.1007\/978-3-319-10599-4_40"},{"key":"17346_CR39","doi-asserted-by":"crossref","unstructured":"Lee N, Choi W, Vernaza P, et\u00a0al (2017) Desire: distant future prediction in dynamic scenes with interacting agents. pp 336\u2013345, https:\/\/openaccess.thecvf.com\/content_cvpr_2017\/papers\/Lee_DESIRE_Distant_Future_CVPR_2017_paper.pdf","DOI":"10.1109\/CVPR.2017.233"},{"key":"17346_CR40","doi-asserted-by":"publisher","unstructured":"Pellegrini S, Ess A, Schindler K, et\u00a0al (2009) You\u2019ll never walk alone: modeling social behavior for multi-target tracking. In: 2009 IEEE 12th international conference on computer vision, pp 261\u2013268, https:\/\/doi.org\/10.1109\/ICCV.2009.5459260, iSSN: 2380-7504","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"17346_CR41","doi-asserted-by":"publisher","unstructured":"Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. https:\/\/doi.org\/10.48550\/arXiv.1412.6980, arXiv:1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"17346_CR42","doi-asserted-by":"publisher","unstructured":"Konev S, Brodt K, Sanakoyeu A (2022) MotionCNN: a strong baseline for motion prediction in autonomous driving. https:\/\/doi.org\/10.48550\/arXiv.2206.02163,https:\/\/ui.adsabs.harvard.edu\/abs\/2022arXiv220602163K","DOI":"10.48550\/arXiv.2206.02163"},{"key":"17346_CR43","unstructured":"Ye J, Zhao Z, Wu M (2007) Discriminative K-means for clustering. In: advances in neural information processing systems, vol\u00a020. Curran Associates, Inc., https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2007\/hash\/a5cdd4aa0048b187f7182f1b9ce7a6a7-Abstract.html"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17346-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17346-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17346-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T11:38:56Z","timestamp":1714390736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17346-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,20]]},"references-count":43,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17346"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17346-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,20]]},"assertion":[{"value":"10 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 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 there is no conflict of interest\/Competing interests regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest\/Competing interests"}}]}}