{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T03:21:30Z","timestamp":1784258490359,"version":"3.55.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"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":["61936001"],"award-info":[{"award-number":["61936001"]}],"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":["61773324"],"award-info":[{"award-number":["61773324"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s13042-023-01910-w","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T17:01:59Z","timestamp":1691082119000},"page":"283-302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Gaussian mixture model with local consistency: a hierarchical minimum message length-based approach"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5621-592X","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoyin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeng","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihong","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"key":"1910_CR1","volume-title":"Machine learning: a probabilistic perspective","author":"KP Murphy","year":"2012","unstructured":"Murphy KP (2012) Machine learning: a probabilistic perspective. The MIT Press, Cambridge"},{"key":"1910_CR2","doi-asserted-by":"crossref","first-page":"3828","DOI":"10.1109\/TIP.2021.3065799","volume":"30","author":"M Zhao","year":"2021","unstructured":"Zhao M, Jia X, Fan L, Liang Y, Yan DM (2021) Robust ellipse fitting using hierarchical Gaussian mixture models. IEEE Trans Image Process 30:3828\u20133843","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"1910_CR3","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1109\/TPAMI.2018.2845371","volume":"41","author":"L Yu","year":"2019","unstructured":"Yu L, Yang T, Chan AB (2019) Density-preserving hierarchical EM algorithm: simplifying Gaussian mixture models for approximate inference. IEEE Trans Pattern Anal Mach Intell 41(6):1323\u20131337","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"1910_CR4","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/TNNLS.2021.3105570","volume":"34","author":"H Lan","year":"2023","unstructured":"Lan H, Liu ZQ, Hsiao JH, Yu D, Chan AB (2023) Clustering hidden Markov models with variational Bayesian hierarchical EM. IEEE Trans Neural Netw Learn Syst 34(3):1537\u20131551","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"1910_CR5","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/TETCI.2018.2829981","volume":"2","author":"M Wang","year":"2018","unstructured":"Wang M, Abdelfattah S, Moustafa N, Hu J (2018) Deep Gaussian mixture-hidden Markov model for classification of EEG signals. IEEE Trans Emerg Top Comput Intell 2(4):278\u2013287","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"6","key":"1910_CR6","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1109\/TPAMI.2020.3048727","volume":"44","author":"Z Liu","year":"2022","unstructured":"Liu Z, Yu L, Hsiao JH, Chan AB (2022) PRIMAL-GMM: PaRametric MAnifold learning of Gaussian mixture models. IEEE Trans Pattern Anal Mach Intell 44(6):3197\u20133211","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1910_CR7","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/TSIPN.2020.2983139","volume":"6","author":"HP Maretic","year":"2020","unstructured":"Maretic HP, Frossard P (2020) Graph Laplacian mixture model. IEEE Trans Signal Inf Process over Netw 6:261\u2013270","journal-title":"IEEE Trans Signal Inf Process over Netw"},{"issue":"7","key":"1910_CR8","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1109\/TNNLS.2020.3049056","volume":"33","author":"W Zhou","year":"2022","unstructured":"Zhou W, Zhang HT, Wang J (2022) An efficient sparse Bayesian learning algorithm based on Gaussian-scale mixtures. IEEE Trans Neural Netw Learn Syst 33(7):3065\u20133078","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1910_CR9","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1109\/TIP.2023.3263099","volume":"32","author":"H Fu","year":"2023","unstructured":"Fu H, Liang F, Lin J, Li B, Akbari M, Liang J, Zhang G, Liu D, Tu C, Han J (2023) Learned image compression with Gaussian-Laplacian-logistic mixture model and concatenated residual modules. IEEE Trans Image Process 32:2063\u20132076","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"1910_CR10","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/TASE.2022.3144583","volume":"20","author":"Y Cao","year":"2023","unstructured":"Cao Y, Jan NM, Huang B, Wang Y, Pan Z, Gui W (2023) No-delay multimodal process monitoring using Kullback-Leibler divergence-based statistics in probabilistic mixture models. IEEE Trans Autom Sci Eng 20(1):167\u2013178","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"1910_CR11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2020.105894","volume":"199","author":"D Relan","year":"2021","unstructured":"Relan D, Relan R (2021) Unsupervised sorting of retinal vessels using locally consistent Gaussian mixtures. Comput Methods Progr Biomed 199:1\u201312","journal-title":"Comput Methods Progr Biomed"},{"issue":"4","key":"1910_CR12","doi-asserted-by":"crossref","first-page":"4151","DOI":"10.1109\/TKDE.2021.3126577","volume":"35","author":"J Neera","year":"2023","unstructured":"Neera J, Chen XM, Aslam N, Wang KZ, Shu Z (2023) Private and utility enhanced recommendations with local differential privacy and Gaussian mixture model. IEEE Trans Knowl Data Eng 35(4):4151\u20134163","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1910_CR13","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1109\/72.238318","volume":"4","author":"L Xu","year":"1993","unstructured":"Xu L, Krzyzak A, Oja E (1993) Rival penalized competitive learning for clustering analysis, RBF net, and curve detection. IEEE Trans Neural Netw 4(4):636\u2013649","journal-title":"IEEE Trans Neural Netw"},{"issue":"12\u201313","key":"1910_CR14","doi-asserted-by":"crossref","first-page":"2265","DOI":"10.1016\/j.neucom.2011.03.013","volume":"74","author":"CD Wang","year":"2011","unstructured":"Wang CD, Lai JH (2011) Energy based competitive learning. Neurocomputing 74(12\u201313):2265\u20132275","journal-title":"Neurocomputing"},{"issue":"3","key":"1910_CR15","first-page":"1","volume":"15","author":"XY Liu","year":"2021","unstructured":"Liu XY, Yang B, Chen HC, Musial K, Chen HX, Li Y, Zuo WL (2021) A scalable redefined stochastic blockmodel. ACM Trans Knowl Discov Data 15(3):1\u201328","journal-title":"ACM Trans Knowl Discov Data"},{"key":"1910_CR16","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1016\/j.patcog.2013.09.036","volume":"47","author":"H Zeng","year":"2014","unstructured":"Zeng H, Cheung YM (2014) Learning a mixture model for clustering with the completed likelihood minimum message length criterion. Pattern Recogn 47:2011\u20132030","journal-title":"Pattern Recogn"},{"issue":"4","key":"1910_CR17","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1109\/TCSVT.2018.2793359","volume":"29","author":"BH Li","year":"2019","unstructured":"Li BH, Lu HC, Zhang Y, Lin ZC, Wu W (2019) Subspace clustering under complex noise. IEEE Trans Circuits Syst Video Technol 29(4):930\u2013940","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1910_CR18","first-page":"575","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"J Chen","year":"2019","unstructured":"Chen J, Milot L, Cheung H, Martel AL (2019) Unsupervised clustering of quantitative imaging phenotypes using autoencoder and Gaussian mixture model. In: Shen D et al (eds) International Conference on Medical Image Computing and Computer-Assisted Intervention, vol 11767. Springer, Shenzhen, pp 575\u2013582"},{"key":"1910_CR19","doi-asserted-by":"crossref","first-page":"106417","DOI":"10.1016\/j.gexplo.2019.106417","volume":"210","author":"XC Liu","year":"2020","unstructured":"Liu XC, Wang WL, Pei YR, Yu PP (2020) A knowledge-driven way to interpret the isometric log-ratio transformation and mixture distributions of geochemical data. J Geochem Explor 210:106417","journal-title":"J Geochem Explor"},{"issue":"2","key":"1910_CR20","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/comjnl\/11.2.185","volume":"11","author":"CS Wallace","year":"1968","unstructured":"Wallace CS, Boulton DM (1968) An information measure for classification. Comput J 11(2):185\u2013194","journal-title":"Comput J"},{"key":"1910_CR21","unstructured":"Oliver, J.J., Baxter, R.A., Wallace, C.S. (1996) Unsupervised learning using MML. In: Proceedings of the thirteenth international conference on international conference on machine learning (ICML). ACM, Bari, p 364\u2013372"},{"issue":"3","key":"1910_CR22","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1111\/j.2517-6161.1987.tb01695.x","volume":"49","author":"CS Wallace","year":"1987","unstructured":"Wallace CS, Freeman PR (1987) Estimation and inference by compact coding. J R Stat Soc Ser B 49(3):240\u2013252","journal-title":"J R Stat Soc Ser B"},{"issue":"3","key":"1910_CR23","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1109\/34.990138","volume":"24","author":"MAT Figueiredo","year":"2002","unstructured":"Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381\u2013396","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1910_CR24","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.23919\/EUSIPCO54536.2021.9616056","volume-title":"2021 29th European signal processing conference (EUSIPCO)","author":"ZX Xian","year":"2021","unstructured":"Xian ZX, Azam M, Amayri M, Bouguila N (2021) Model selection criterion for multivariate bounded asymmetric Gaussian mixture model. 2021 29th European signal processing conference (EUSIPCO). IEEE, Dublin, pp 1436\u20131440"},{"key":"1910_CR25","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1007\/s13042-019-01059-5","volume":"11","author":"RK Yadav","year":"2020","unstructured":"Yadav RK, Verma S, Venkatesan S (2020) Regularization on a rapidly varying manifold. Int J Mach Learn Cybern 11:1571\u20131590","journal-title":"Int J Mach Learn Cybern"},{"key":"1910_CR26","first-page":"512","volume-title":"Proceedings of the twenty-fourth AAAI conference on artificial intelligence (AAAI-10)","author":"J Liu","year":"2010","unstructured":"Liu J, Cai D, He X (2010) Gaussian mixture model with local consistency. Proceedings of the twenty-fourth AAAI conference on artificial intelligence (AAAI-10), vol 24. AAAI Press, Atlanta, pp 512\u2013517"},{"key":"1910_CR27","doi-asserted-by":"crossref","first-page":"107724","DOI":"10.1016\/j.asoc.2021.107724","volume":"111","author":"Y Wang","year":"2021","unstructured":"Wang Y, Li T, Chen L, Xu G, Zhou J, Chen CP (2021) Random fourier feature-based fuzzy clustering with p-Laplacian regularization. Appl Soft Comput 111:107724","journal-title":"Appl Soft Comput"},{"key":"1910_CR28","doi-asserted-by":"crossref","first-page":"104326","DOI":"10.1016\/j.jbi.2023.104326","volume":"140","author":"Y Ban","year":"2023","unstructured":"Ban Y, Lao H, Li B, Su W, Zhang X (2023) Diagnosis of Alzheimer\u2019s disease using hypergraph p-Laplacian regularized multi-task feature learning. J Biomed Inform 140:104326","journal-title":"J Biomed Inform"},{"key":"1910_CR29","doi-asserted-by":"crossref","first-page":"108716","DOI":"10.1016\/j.patcog.2022.108716","volume":"129","author":"C Gu","year":"2022","unstructured":"Gu C, Lu X, Zhang C (2022) Example-based color transfer with Gaussian mixture modeling. Pattern Recogn 129:108716","journal-title":"Pattern Recogn"},{"key":"1910_CR30","doi-asserted-by":"crossref","DOI":"10.1002\/0471721182","volume-title":"Finite mixture model","author":"G McLachlan","year":"2000","unstructured":"McLachlan G, Peel D (2000) Finite mixture model. Wiley, New York"},{"key":"1910_CR31","doi-asserted-by":"crossref","first-page":"107836","DOI":"10.1016\/j.patcog.2021.107836","volume":"114","author":"H Asheri","year":"2021","unstructured":"Asheri H, Hosseini R, Araabi BN (2021) A new EM algorithm for flexibly tied GMMs with large number of components. Pattern Recogn 114:107836","journal-title":"Pattern Recogn"},{"key":"1910_CR32","doi-asserted-by":"crossref","first-page":"803","DOI":"10.2307\/2532201","volume":"49","author":"JD Banfield","year":"1993","unstructured":"Banfield JD, Raftery AE (1993) Model-based Gaussian and non-Gaussian clustering. Biometrics 49:803\u2013821","journal-title":"Biometrics"},{"issue":"5","key":"1910_CR33","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/0031-3203(94)00125-6","volume":"28","author":"G Celeux","year":"1995","unstructured":"Celeux G, Govaert G (1995) Gaussian parsimonious clustering models. Pattern Recogn 28(5):781\u2013793","journal-title":"Pattern Recogn"},{"key":"1910_CR34","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.csda.2017.12.005","volume":"121","author":"C Grazian","year":"2018","unstructured":"Grazian C, Robert CP (2018) Jeffreys priors for mixture estimation: properties and alternatives. Comput Stat Data Anal 121:149\u2013163","journal-title":"Comput Stat Data Anal"},{"key":"1910_CR35","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316870","volume-title":"Bayesian theory","author":"JM Bernardo","year":"1994","unstructured":"Bernardo JM, Smith AFM (1994) Bayesian theory. Wiley, Chichester"},{"key":"1910_CR36","volume-title":"Statistical analysis of finite mixture distributions","author":"D Titterington","year":"1985","unstructured":"Titterington D, Afm S, Smith FM, Makov UE et al (1985) Statistical analysis of finite mixture distributions. Wiley, New York"},{"key":"1910_CR37","doi-asserted-by":"crossref","unstructured":"Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In Proceedings of the 14th international conference on neural information processing systems: natural and synthetic, vol 14. IEEE, Cambridge, p 585\u2013591","DOI":"10.7551\/mitpress\/1120.003.0080"},{"issue":"1","key":"1910_CR38","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79\u201386","journal-title":"Ann Math Stat"},{"issue":"4","key":"1910_CR39","doi-asserted-by":"crossref","first-page":"2348","DOI":"10.1109\/TSMC.2021.3049490","volume":"52","author":"D Cheng","year":"2022","unstructured":"Cheng D, Huang J, Zhang S, Zhang X, Luo X (2022) A novel approximate spectral clustering algorithm with dense cores and density peaks. IEEE Trans Syst Man Cybern Syst 52(4):2348\u20132360","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"1910_CR40","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.ins.2016.08.086","volume":"373","author":"J Xu","year":"2016","unstructured":"Xu J, Wang G, Deng W (2016) DenPEHC: density peak based efficient hierarchical clustering. Inf Sci 373:200\u2013218","journal-title":"Inf Sci"},{"issue":"5","key":"1910_CR41","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s00138-013-0568-z","volume":"25","author":"T Elguebaly","year":"2014","unstructured":"Elguebaly T, Bouguila N (2014) Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection. Mach Vis Appl 25(5):1145\u20131162","journal-title":"Mach Vis Appl"},{"key":"1910_CR42","volume-title":"Finite mixture models","author":"D Peel","year":"2000","unstructured":"Peel D, MacLahlan G (2000) Finite mixture models. Wiley, New york"},{"issue":"2","key":"1910_CR43","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G Schwarz","year":"1978","unstructured":"Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461\u2013464","journal-title":"Ann Stat"},{"key":"1910_CR44","doi-asserted-by":"crossref","DOI":"10.1142\/0822","volume-title":"Stochastic complexity in statistical inquiry","author":"J Rissanen","year":"1998","unstructured":"Rissanen J (1998) Stochastic complexity in statistical inquiry. World Scientific, Singapore"},{"issue":"6","key":"1910_CR45","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","volume":"19","author":"H Akaike","year":"1974","unstructured":"Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716\u2013723","journal-title":"IEEE Trans Autom Control"},{"issue":"3","key":"1910_CR46","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/BF02294361","volume":"52","author":"H Bozdogan","year":"1987","unstructured":"Bozdogan H (1987) Model selection and Akaike\u2019s information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52(3):345\u2013370","journal-title":"Psychometrika"},{"key":"1910_CR47","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/3-540-48432-9_5","volume-title":"Energy minimization methods in computer vision and pattern recognition","author":"MA Figueiredo","year":"1999","unstructured":"Figueiredo MA, Leitao JM, Jain AK (1999) On fitting mixture models. In: Hancock ER et al (eds) Energy minimization methods in computer vision and pattern recognition, vol 1654. Springer, pp 54\u201369"},{"key":"1910_CR48","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3248064","author":"D Cheng","year":"2023","unstructured":"Cheng D, Huang J, Zhang S, Xia S, Wang G, Xie J (2023) K-Means Clustering With Natural Density Peaks for Discovering Arbitrary-Shaped Clusters. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3248064","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"1910_CR49","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/TKDE.2019.2903410","volume":"32","author":"D Huang","year":"2020","unstructured":"Huang D, Wang C, Wu J, Lai J, Kwoh CK (2020) Ultra-scalable spectral clustering and ensemble clustering. IEEE Trans Knowl Data Eng 32(6):1212\u20131226","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1910_CR50","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning. Springer, New York"},{"key":"1910_CR51","first-page":"731","volume-title":"Computer vision and pattern recognition (CVPR)","author":"J Shi","year":"1997","unstructured":"Shi J, Malik J (1997) Normalized cuts and image segmentation. Computer vision and pattern recognition (CVPR). IEEE, San Juan, pp 731\u2013737"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01910-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01910-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01910-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T17:08:50Z","timestamp":1729876130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01910-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,3]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1910"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01910-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,3]]},"assertion":[{"value":"29 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}