{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T23:14:05Z","timestamp":1782774845858,"version":"3.54.5"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Yellow Crane Talents Program funding","award":["HUBT 4301\/00550"],"award-info":[{"award-number":["HUBT 4301\/00550"]}]},{"name":"Yellow Crane Talents Program funding","award":["HUBT 4301\/00550"],"award-info":[{"award-number":["HUBT 4301\/00550"]}]},{"name":"Yellow Crane Talents Program funding","award":["HUBT 4301\/00550"],"award-info":[{"award-number":["HUBT 4301\/00550"]}]},{"name":"Yellow Crane Talents Program funding","award":["HUBT 4301\/00550"],"award-info":[{"award-number":["HUBT 4301\/00550"]}]},{"name":"Yellow Crane Talents Program funding","award":["HUBT 4301\/00550"],"award-info":[{"award-number":["HUBT 4301\/00550"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62376089"],"award-info":[{"award-number":["62376089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62376089"],"award-info":[{"award-number":["62376089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62376089"],"award-info":[{"award-number":["62376089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62376089"],"award-info":[{"award-number":["62376089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62376089"],"award-info":[{"award-number":["62376089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s13042-024-02499-4","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T11:57:29Z","timestamp":1736251049000},"page":"9999-10023","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A two-stage optimized robust kernel density estimation for Bayesian classification with outliers"],"prefix":"10.1007","volume":"16","author":[{"given":"Chenghao","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"2499_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssresearch.2022.102817","volume":"10","author":"XL Shu","year":"2023","unstructured":"Shu XL, Ye YW (2023) Knowledge discovery: Methods from data mining and machine learning. Soc Sci Re 10:102817","journal-title":"Soc Sci Re"},{"issue":"1","key":"2499_CR2","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1080\/00949655.2017.1379521","volume":"88","author":"T Wang","year":"2018","unstructured":"Wang T, Li Q, Chen B, Li ZH (2018) Multiple outliers detection in sparse high-dimensional regression. J Stat Comput Sim 88(1):89\u2013107","journal-title":"J Stat Comput Sim"},{"key":"2499_CR3","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1109\/ACCESS.2018.2886197","volume":"7","author":"SB Su","year":"2018","unstructured":"Su SB, Xiao LM, Ruan L, Gu F, Li SP, Wang ZK, Xu RB (2018) An efficient density-based local outlier detection approach for scattered data. IEEE Access 7:1006\u20131020","journal-title":"IEEE Access"},{"issue":"2","key":"2499_CR4","first-page":"33","volume":"79","author":"S Omar","year":"2013","unstructured":"Omar S, Ngadi A, Jebur HH (2013) Machine learning techniques for anomaly detection: an overview. Int J Com App 79(2):33\u201341","journal-title":"Int J Com App"},{"key":"2499_CR5","doi-asserted-by":"publisher","first-page":"107964","DOI":"10.1109\/ACCESS.2019.2932769","volume":"7","author":"H Wang","year":"2019","unstructured":"Wang H, Bah MJ, Hammad M (2019) Progress in outlier detection techniques: A survey. IEEE Access 7:107964\u2013108000","journal-title":"IEEE Access"},{"issue":"2","key":"2499_CR6","first-page":"1","volume":"2","author":"E Asmare","year":"2018","unstructured":"Asmare E, Begashaw A (2018) Review on Parametric and Nonparametric Methods of Efficiency Analysis. Open Acc Bio 2(2):1\u20137","journal-title":"Open Acc Bio"},{"key":"2499_CR7","doi-asserted-by":"crossref","unstructured":"Yang X, Latecki LJ, Pokrajac D (2009) Outlier detection with globally optimal exemplar-based GMM. In: Proceedings of the 2009 International Conference on Data Mining(SIAM), pp. 145-154","DOI":"10.1137\/1.9781611972795.13"},{"issue":"8","key":"2499_CR8","doi-asserted-by":"publisher","first-page":"161","DOI":"10.14257\/ijca.2015.8.8.17","volume":"8","author":"X Tang","year":"2015","unstructured":"Tang X, Yuan R, Chen J (2015) Outlier detection in energy disaggregation using subspace learning and Gaussian mixture model. Int J Control Autom 8(8):161\u2013170","journal-title":"Int J Control Autom"},{"issue":"11","key":"2499_CR9","doi-asserted-by":"publisher","first-page":"3923","DOI":"10.1007\/s13042-023-01873-y","volume":"14","author":"G Zhao","year":"2023","unstructured":"Zhao G, Yang T, Fu D (2023) Manifold-based denoising, outlier detection, and dimension reduction algorithm for high-dimensional data. Int J Mach Learn Cyb 14(11):3923\u20133942","journal-title":"Int J Mach Learn Cyb"},{"issue":"12","key":"2499_CR10","doi-asserted-by":"publisher","first-page":"4451","DOI":"10.1016\/j.patcog.2012.05.002","volume":"45","author":"J Cao","year":"2012","unstructured":"Cao J, Kwong S, Wang R (2012) A noise-detection based adaboost algorithm for mislabeled data. Pattern Recogn 45(12):4451\u20134465","journal-title":"Pattern Recogn"},{"issue":"7","key":"2499_CR11","first-page":"3069","volume":"29","author":"Z Xiao","year":"2017","unstructured":"Xiao Z, Luo Z, Zhong B, Dang X (2017) Robust and efficient boosting method using the conditional risk. IEEE Trans Neural Netw Learn Syst 29(7):3069\u20133083","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2499_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105705","volume":"195","author":"Z Chen","year":"2020","unstructured":"Chen Z, Duan J, Yang C, Kang L, Qiu G (2020) SMLboost-adopting a soft-margin like strategy in boosting. Knowl-Based Syst 195:105705","journal-title":"Knowl-Based Syst"},{"issue":"7","key":"2499_CR13","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.1007\/s13042-022-01766-6","volume":"14","author":"W Chao","year":"2023","unstructured":"Chao W, Bo L, Lei W, Pai P (2023) Improving boosting methods with a stable loss function handling outliers. Int J Mach Learn Cyb 14(7):2333\u20132352","journal-title":"Int J Mach Learn Cyb"},{"issue":"15","key":"2499_CR14","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.1080\/00207721.2015.1110212","volume":"47","author":"Y Xu","year":"2016","unstructured":"Xu Y, Chen M, Li G (2016) Least squares twin support vector machine with universum data for classification. Int J Syst Sci 47(15):3637\u20133645","journal-title":"Int J Syst Sci"},{"issue":"6","key":"2499_CR15","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1080\/00207721.2023.2169059","volume":"54","author":"H Khan","year":"2023","unstructured":"Khan H, Wang X, Liu H (2023) A study on relationship between prediction uncertainty and robustness to noisy data. Int J Syst Sci 54(6):1243\u20131258","journal-title":"Int J Syst Sci"},{"issue":"4","key":"2499_CR16","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/s13042-021-01423-4","volume":"13","author":"J Bootkrajang","year":"2022","unstructured":"Bootkrajang J, Chaijaruwanich J (2022) Towards an improved label noise proportion estimation in small data: a Bayesian approach. Int J Mach Learn Cyb 13(4):851\u2013867","journal-title":"Int J Mach Learn Cyb"},{"issue":"1\u20132","key":"2499_CR17","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/S0004-3702(00)00085-0","volume":"125","author":"M Ramoni","year":"2001","unstructured":"Ramoni M, Sebastiani P (2001) Robust Bayes classifiers. Artif Intell 125(1\u20132):209\u2013226","journal-title":"Artif Intell"},{"issue":"1","key":"2499_CR18","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s00357-019-9306-1","volume":"36","author":"MM Rahaman","year":"2019","unstructured":"Rahaman MM, Mollah MNH (2019) Robustification of gaussian bayes Classifier by the minimum $$\\beta$$-divergence method. J Classie 36(1):113\u2013139","journal-title":"J Classie"},{"key":"2499_CR19","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s13042-014-0318-1","volume":"9","author":"J Wang","year":"2018","unstructured":"Wang J, Zhang L, Cao JJ, Han D (2018) NBWELM: naive Bayesian based weighted extreme learning machine. Int J Mach Learn Cyb 9:21\u201335","journal-title":"Int J Mach Learn Cyb"},{"key":"2499_CR20","doi-asserted-by":"publisher","first-page":"22724","DOI":"10.1109\/ACCESS.2022.3155231","volume":"10","author":"T Kim","year":"2022","unstructured":"Kim T, Lee JS (2022) Exponential loss minimization for learning weighted naive bayes classifiers. IEEE Access 10:22724\u201322736","journal-title":"IEEE Access"},{"issue":"1","key":"2499_CR21","first-page":"161","volume":"1","author":"YC Chen","year":"2017","unstructured":"Chen YC (2017) A tutorial on kernel density estimation and recent advances. Bio Epid 1(1):161\u2013187","journal-title":"Bio Epid"},{"issue":"11","key":"2499_CR22","doi-asserted-by":"publisher","first-page":"3009","DOI":"10.1016\/j.csda.2005.06.019","volume":"50","author":"XB Zhang","year":"2006","unstructured":"Zhang XB, King ML, Hyndman RJ (2006) A Bayesian approach to bandwidth selection for multivariate kernel density estimation. Compu Stat Data Ana 50(11):3009\u20133031","journal-title":"Compu Stat Data Ana"},{"key":"2499_CR23","doi-asserted-by":"crossref","unstructured":"Wahbah M, Mohandes B, EL-Fouly THM, Moursi MS, (2022) Unbiased cross-validation kernel density estimation for wind and PV probabilistic modelling. Ener Con Man 266:115811","DOI":"10.1016\/j.enconman.2022.115811"},{"key":"2499_CR24","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10115-013-0712-0","volume":"42","author":"AP Boedihardjo","year":"2015","unstructured":"Boedihardjo AP, Lu CT, Chen F (2015) Fast adaptive kernel density estimator for data streams. Knowl Inf Syst 42:285\u2013317","journal-title":"Knowl Inf Syst"},{"key":"2499_CR25","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.swevo.2016.09.002","volume":"33","author":"R Pratyusha","year":"2017","unstructured":"Pratyusha R, Amit K, Swagatam D (2017) Noisy evolutionary optimization algorithms-A comprehensive survey. Swarm Evol Comput 33:18\u201345","journal-title":"Swarm Evol Comput"},{"key":"2499_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.103249","volume":"87","author":"H Vahideh","year":"2020","unstructured":"Vahideh H, Ali APK (2020) Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artie Intel 87:103249","journal-title":"Eng Appl Artie Intel"},{"key":"2499_CR27","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"key":"2499_CR28","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","volume":"22","author":"DS Wang","year":"2018","unstructured":"Wang DS, Tan DP, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22:387\u2013408","journal-title":"Soft Comput"},{"key":"2499_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113370","volume":"152","author":"XG Zhao","year":"2020","unstructured":"Zhao XG, Ji L, Jin M, Ying Z (2020) An improved quantum particle swarm optimization algorithm for environmental economic dispatch. Exp Syst Appl 152:113370","journal-title":"Exp Syst Appl"},{"key":"2499_CR30","doi-asserted-by":"crossref","unstructured":"Ye ZW, Ma L, Chen HW (2016) A hybrid rice optimization algorithm. In:The 11th International Conference on Computer Science and Education (ICCSE), pp. 169-174","DOI":"10.1109\/ICCSE.2016.7581575"},{"key":"2499_CR31","doi-asserted-by":"publisher","first-page":"67205","DOI":"10.1109\/ACCESS.2020.2985498","volume":"8","author":"YH Liu","year":"2022","unstructured":"Liu YH, Cao BY (2022) A novel ant colony optimization algorithm with Levy flight. IEEE Access 8:67205\u201367213","journal-title":"IEEE Access"},{"key":"2499_CR32","doi-asserted-by":"crossref","unstructured":"Liu TQ, Yang H, Yu J, Zhou K, Jiang F (2022) A global harmony search algorithm based on tent chaos map and elite reverse learning. In: The 14th International Conference on Advanced Computational Intelligence (ICACI), pp 320-325","DOI":"10.1109\/ICACI55529.2022.9837636"},{"key":"2499_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2020.109138","volume":"191","author":"SP Wang","year":"2020","unstructured":"Wang SP, Li A, Wen KY, Wu XM (2020) Robust kernels for kernel density estimation. Econ Lett 191:109138","journal-title":"Econ Lett"},{"key":"2499_CR34","first-page":"1","volume":"18","author":"XY Zhang","year":"2017","unstructured":"Zhang XY, Himel M, Tang ZX, Zhang L, Cui XQ, Andrew KB, Yi NJ (2017) Negative binomial mixed models for analyzing microbiome count data. BMC Bioinf 18:1\u201310","journal-title":"BMC Bioinf"},{"key":"2499_CR35","doi-asserted-by":"crossref","unstructured":"Cao KY, Shi LX, Wang GR, Han DH, Bai M (2014) Density-based local outlier detection on uncertain data. In: International Conference on Web-Age Information Management (CWAIM), pp 67-71","DOI":"10.1007\/978-3-319-08010-9_9"},{"key":"2499_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2019.102581","volume":"95","author":"HK Kevin","year":"2019","unstructured":"Kevin HK (2019) Optimal data-based binning for histograms and histogram-based probability density models. Digital Signal Process 95:102581","journal-title":"Digital Signal Process"},{"key":"2499_CR37","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1016\/j.ins.2019.10.017","volume":"512","author":"F Kamalov","year":"2020","unstructured":"Kamalov F (2020) Kernel density estimation based sampling for imbalanced class distribution. Inform Sci 512:1192\u20131201","journal-title":"Inform Sci"},{"issue":"1","key":"2499_CR38","first-page":"2529","volume":"13","author":"J Kim","year":"2012","unstructured":"Kim J, Clayton DS (2012) Robust kernel density estimation. Inform Sci 13(1):2529\u20132565","journal-title":"Inform Sci"},{"issue":"20","key":"2499_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app122010443","volume":"12","author":"GL Ou","year":"2022","unstructured":"Ou GL, He YL, Philippe FV, Huang JZX (2022) A novel mixed-attribute fusion-based naive bayesian classifier. Appl Sci 12(20):1\u201316","journal-title":"Appl Sci"},{"issue":"400","key":"2499_CR40","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1080\/01621459.1987.10478550","volume":"82","author":"DW Scott","year":"1987","unstructured":"Scott DW, Terrell GR (1987) Biased and unbiased cross-validation in density estimation. J Am Stat Assoc 82(400):1131\u20131146","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"2499_CR41","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1111\/j.1467-9469.2005.00445.x","volume":"32","author":"T Duong","year":"2005","unstructured":"Duong T, Hazelton ML (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scand J Stat 32(3):485\u2013506","journal-title":"Scand J Stat"},{"issue":"5","key":"2499_CR42","first-page":"5751","volume":"52","author":"Z Shu","year":"2022","unstructured":"Shu Z, Ye ZW, Zong XL, Liu SQ, Zhang DD, Wang CZ, Wang MW (2022) A modified hybrid rice optimization algorithm for solving 0\u20131 knapsack problem. Appl Sci 52(5):5751\u20135769","journal-title":"Appl Sci"},{"issue":"5","key":"2499_CR43","first-page":"9277","volume":"40","author":"HM Jia","year":"2021","unstructured":"Jia HM, Lang CB (2021) Salp swarm algorithm with crossover scheme and Levy flight for global optimization. J Intell Fuzzy Syst 40(5):9277\u20139288","journal-title":"J Intell Fuzzy Syst"},{"key":"2499_CR44","doi-asserted-by":"publisher","first-page":"67205","DOI":"10.1109\/ACCESS.2020.2985498","volume":"8","author":"YH Liu","year":"2020","unstructured":"Liu YH, Cao BY (2020) A novel ant colony optimization algorithm with Levy flight. IEEE Access 8:67205\u201367213","journal-title":"IEEE Access"},{"key":"2499_CR45","doi-asserted-by":"publisher","first-page":"5277","DOI":"10.1007\/s00500-020-05527-x","volume":"25","author":"W Deng","year":"2021","unstructured":"Deng W, Shang SF, Cai X, Zhao HM, Song YJ, Xu JJ (2021) An improved differential evolution algorithm and its application in optimization problem. Soft Comput 25:5277\u20135298","journal-title":"Soft Comput"},{"key":"2499_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/2734362","volume":"2017","author":"CW Qu","year":"2017","unstructured":"Qu CW, Zhao SA, Fu YM, He W (2017) Chicken swarm optimization based on elite opposition-based learning. Math Probl Eng 2017:1\u201320","journal-title":"Math Probl Eng"},{"issue":"1","key":"2499_CR47","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1287\/opre.2020.2069","volume":"70","author":"B Dimitris","year":"2022","unstructured":"Dimitris B, Nihal K (2022) Data-driven optimization: A reproducing kernel Hilbert space approach. Oper Res 70(1):454\u2013471","journal-title":"Oper Res"},{"key":"2499_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2020.109138","volume":"191","author":"SP Wang","year":"2020","unstructured":"Wang SP, Li A, Wen KG, Wu XM (2020) Robust kernels for kernel density estimation. Econ Lett 191:109138","journal-title":"Econ Lett"},{"key":"2499_CR49","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.neucom.2014.09.024","volume":"151","author":"LR Ezequiel","year":"2015","unstructured":"Ezequiel LR, Esteban JP, Enrique D (2015) Robust self-organization with M-estimators. Neurocomputing 151:408\u2013423","journal-title":"Neurocomputing"},{"issue":"1","key":"2499_CR50","doi-asserted-by":"publisher","first-page":"3695","DOI":"10.1214\/22-EJS2030","volume":"16","author":"T Mathieu","year":"2022","unstructured":"Mathieu T (2022) Concentration study of M-estimators using the influence function. Electron J Stat 16(1):3695\u20133750","journal-title":"Electron J Stat"},{"issue":"10","key":"2499_CR51","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1016\/j.jspi.2007.11.008","volume":"138","author":"G Shevlyakov","year":"2008","unstructured":"Shevlyakov G, Morgenthaler S, Shurygin A (2008) Redescending M-estimators. J Stat Plan Infer 138(10):2906\u20132917","journal-title":"J Stat Plan Infer"},{"key":"2499_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2021.107254","volume":"147","author":"DQF Menezes","year":"2021","unstructured":"Menezes DQF, Prate DM, Secchi AR, Pinto JC (2021) A review on robust M-estimators for regression analysis. Comput Chem Eng 147:107254","journal-title":"Comput Chem Eng"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02499-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02499-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02499-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T04:28:31Z","timestamp":1766032111000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02499-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,7]]},"references-count":52,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2499"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02499-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4384312\/v1","asserted-by":"object"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,7]]},"assertion":[{"value":"7 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}