{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:13:51Z","timestamp":1767917631151,"version":"3.49.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T00:00:00Z","timestamp":1642204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T00:00:00Z","timestamp":1642204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.18BJY033"],"award-info":[{"award-number":["No.18BJY033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"china jiliang university student research key funding project","award":["No. 2020X24060"],"award-info":[{"award-number":["No. 2020X24060"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s10489-021-02929-8","type":"journal-article","created":{"date-parts":[[2022,1,15]],"date-time":"2022-01-15T00:05:35Z","timestamp":1642205135000},"page":"10674-10691","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Classification algorithm for class imbalanced data based on optimized Mahalanobis-Taguchi system"],"prefix":"10.1007","volume":"52","author":[{"given":"Ting","family":"Mao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8627-2124","authenticated-orcid":false,"given":"Yueyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yefang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,15]]},"reference":[{"key":"2929_CR1","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s40747-017-0037-9","volume":"3","author":"A Fern\u00e1ndez","year":"2017","unstructured":"Fern\u00e1ndez A, del R\u00edo S, Chawla NV, Herrera F (2017) An insight into imbalanced Big Data classification: outcomes and challenges. Complex Intell Syst 3:105\u2013120. https:\/\/doi.org\/10.1007\/s40747-017-0037-9","journal-title":"Complex Intell Syst"},{"key":"2929_CR2","first-page":"673","volume":"34","author":"YX Li","year":"2019","unstructured":"Li YX, Chai Y, Hu YQ, Yin HP (2019) Review of imbalanced data classification methods. Control Decis 34:673\u2013688","journal-title":"Control Decis"},{"key":"2929_CR3","doi-asserted-by":"publisher","unstructured":"Priya S, Uthra RA (2020) Comprehensive analysis for class imbalance data with concept drift using ensemble based classification. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-020-01934-y","DOI":"10.1007\/s12652-020-01934-y"},{"key":"2929_CR4","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1177\/1460458218824711","volume":"26","author":"F Thabtah","year":"2020","unstructured":"Thabtah F, Peebles D (2020) A new machine learning model based on induction of rules for autism detection. Health Informatics J 26:264\u2013286. https:\/\/doi.org\/10.1177\/1460458218824711","journal-title":"Health Informatics J"},{"key":"2929_CR5","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/s10660-019-09383-2","volume":"20","author":"S Dhote","year":"2020","unstructured":"Dhote S, Vichoray C, Pais R et al (2020) Hybrid geometric sampling and AdaBoost based deep learning approach for data imbalance in E-commerce. Electron Commer Res 20:259\u2013274. https:\/\/doi.org\/10.1007\/s10660-019-09383-2","journal-title":"Electron Commer Res"},{"key":"2929_CR6","doi-asserted-by":"publisher","unstructured":"Hu Z, Chiong R, Pranata I et al (2018) Malicious web domain identification using online credibility and performance data by considering the class imbalance issue. Ind Manag Data Syst 119. https:\/\/doi.org\/10.1108\/IMDS-02-2018-0072","DOI":"10.1108\/IMDS-02-2018-0072"},{"key":"2929_CR7","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.neucom.2021.06.059","volume":"457","author":"Z Wang","year":"2021","unstructured":"Wang Z, Peng C, Zhang N et al (2021) Fully convolutional siamese networks based change detection for optical aerial images with focal contrastive loss. Neurocomputing 457:55\u2013167. https:\/\/doi.org\/10.1016\/j.neucom.2021.06.059https:\/\/doi.org\/10.1093\/mnras\/staa642","journal-title":"Neurocomputing"},{"key":"2929_CR8","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.03510.1016\/j.eswa.2016.12.035","volume":"73","author":"HX Guo","year":"2017","unstructured":"Guo HX, Li YJ, Shang J et al (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220\u2013239. https:\/\/doi.org\/10.1016\/j.eswa.2016.12.03510.1016\/j.eswa.2016.12.035","journal-title":"Expert Syst Appl"},{"key":"2929_CR9","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.knosys.2019.03.001","volume":"174","author":"C Zhang","year":"2019","unstructured":"Zhang C, Bi J, Xu S et al (2019) Multi-Imbalance: An open-source software for multi-class imbalance learning. Knowledge-Based Syst 174:137\u2013143. https:\/\/doi.org\/10.1016\/j.knosys.2019.03.001","journal-title":"Knowledge-Based Syst"},{"key":"2929_CR10","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409\u2013410","author":"WC Lin","year":"2017","unstructured":"Lin WC, Tsai CF, Hu YH, Jhang JS (2017) Clustering-based undersampling in class-imbalanced data. Inf Sci (Ny) 409\u2013410:17\u201326. https:\/\/doi.org\/10.1016\/j.ins.2017.05.008","journal-title":"Inf Sci (Ny)"},{"key":"2929_CR11","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/978-3-030-04663-7_4","volume":"807","author":"S Vluymans","year":"2019","unstructured":"Vluymans S (2019) Learning from imbalanced data. Stud Comput Intell 807:81\u2013110. https:\/\/doi.org\/10.1007\/978-3-030-04663-7_4","journal-title":"Stud Comput Intell"},{"key":"2929_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3778\/j.issn.1002-8331.1810-0420","volume":"55","author":"HX Xiang","year":"2019","unstructured":"Xiang HX, Yang Y (2019) Summarization of imbalanced Data Mining Methods. Computer Engineering and Applications 55:1\u20136. https:\/\/doi.org\/10.3778\/j.issn.1002-8331.1810-0420","journal-title":"Computer Engineering and Applications"},{"key":"2929_CR13","doi-asserted-by":"publisher","unstructured":"El-Banna M (2017) Modified Mahalanobis Taguchi System for Imbalance Data Classification Comput Intell Neurosci:2017. https:\/\/doi.org\/10.1155\/2017\/5874896","DOI":"10.1155\/2017\/5874896"},{"key":"2929_CR14","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1007\/s13042-019-01033-1","volume":"11","author":"YH Hsiao","year":"2020","unstructured":"Hsiao YH, Su CT, Fu PC (2020) Integrating MTS with bagging strategy for class imbalance problems. Int J Mach Learn Cybern 11:1217\u20131230. https:\/\/doi.org\/10.1007\/s13042-019-01033-1","journal-title":"Int J Mach Learn Cybern"},{"key":"2929_CR15","doi-asserted-by":"publisher","first-page":"2716","DOI":"10.3969\/j.issn.1004-132X.2019.22.011","volume":"30","author":"J Zhan","year":"2019","unstructured":"Zhan J, Cheng LS, Peng ZM et al (2019) Control chart pattern recognition based on hybrid model and improved multi-class Mahalanobis system. China Mechanical Engineering 30:2716\u20132724. https:\/\/doi.org\/10.3969\/j.issn.1004-132X.2019.22.011","journal-title":"China Mechanical Engineering"},{"key":"2929_CR16","doi-asserted-by":"publisher","unstructured":"Hayashi T, Fujita H (2021) One-class ensemble classifier for data imbalance problems. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-021-02671-1","DOI":"10.1007\/s10489-021-02671-1"},{"key":"2929_CR17","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.comcom.2016.05.010","volume":"102","author":"L Peng","year":"2017","unstructured":"Peng L, Zhang H, Chen Y, Yang B (2017) Imbalanced traffic identification using an imbalanced data gravitation-based classification model. Comput Commun 102:177\u2013189. https:\/\/doi.org\/10.1016\/j.comcom.2016.05.010","journal-title":"Comput Commun"},{"key":"2929_CR18","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-981-4585-18-7_2","volume":"285 LNEE","author":"BW Yap","year":"2014","unstructured":"Yap BW, Rani KA, Abd Rahman HA et al (2014) An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets. Lect Notes Electr Eng 285 LNEE:13\u201322. https:\/\/doi.org\/10.1007\/978-981-4585-18-7_2","journal-title":"Lect Notes Electr Eng"},{"key":"2929_CR19","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.patcog.2017.07.024","volume":"72","author":"T Zhu","year":"2017","unstructured":"Zhu T, Lin Y, Liu Y (2017) Synthetic minority oversampling technique for multiclass imbalance problems. Pattern Recogn 72:327\u2013340. https:\/\/doi.org\/10.1016\/j.patcog.2017.07.024","journal-title":"Pattern Recogn"},{"key":"2929_CR20","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.amc.2018.12.020","volume":"351","author":"X Zhang","year":"2019","unstructured":"Zhang X, Li R, Zhang B et al (2019) An instance-based learning recommendation algorithm of imbalance handling methods. Appl Math Comput 351:204\u2013218. https:\/\/doi.org\/10.1016\/j.amc.2018.12.020","journal-title":"Appl Math Comput"},{"key":"2929_CR21","doi-asserted-by":"publisher","first-page":"46886","DOI":"10.1109\/ACCESS.2018.2865383","volume":"6","author":"S Riaz","year":"2018","unstructured":"Riaz S, Arshad A, Jiao L (2018) Rough noise-filtered easy ensemble for software fault prediction. IEEE Access 6:46886\u201346899. https:\/\/doi.org\/10.1109\/ACCESS.2018.2865383","journal-title":"IEEE Access"},{"key":"2929_CR22","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1109\/TNNLS.2018.2878400","volume":"30","author":"M Bader-El-Den","year":"2019","unstructured":"Bader-El-Den M, Teitei E, Perry T (2019) Biased random forest for dealing with the class imbalance problem. IEEE Trans Neural Networks Learn Syst 30:2163\u20132172. https:\/\/doi.org\/10.1109\/TNNLS.2018.2878400","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"2929_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.05.004","volume":"456","author":"CK Maurya","year":"2018","unstructured":"Maurya CK, Toshniwal D (2018) Large-Scale Distributed Sparse Class-Imbalance Learning. Inf Sci (Ny) 456:1\u201312. https:\/\/doi.org\/10.1016\/j.ins.2018.05.004","journal-title":"Inf Sci (Ny)"},{"key":"2929_CR24","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ins.2017.10.017","volume":"425","author":"J Sun","year":"2018","unstructured":"Sun J, Lang J, Fujita H, Li H (2018) Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Inf Sci (Ny) 425:76\u201391. https:\/\/doi.org\/10.1016\/j.ins.2017.10.017","journal-title":"Inf Sci (Ny)"},{"key":"2929_CR25","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.future.2021.06.011","volume":"124","author":"B Wang","year":"2021","unstructured":"Wang B, Ding S, Liu X et al (2021) Predictive classification of ICU readmission using weight decay random forest. Futur Gener Comput Syst 124:351\u2013360. https:\/\/doi.org\/10.1016\/j.future.2021.06.011","journal-title":"Futur Gener Comput Syst"},{"key":"2929_CR26","doi-asserted-by":"publisher","first-page":"7760","DOI":"10.1109\/TII.2021.3058350","volume":"17","author":"A Diez-Olivan","year":"2021","unstructured":"Diez-Olivan A, Ortego P, Del Ser J et al (2021) Adaptive dendritic cell-deep learning approach for industrial prognosis under changing conditions. IEEE Trans Ind Informatics 17:7760\u20137770. https:\/\/doi.org\/10.1109\/TII.2021.3058350","journal-title":"IEEE Trans Ind Informatics"},{"key":"2929_CR27","doi-asserted-by":"crossref","unstructured":"Taguchi G, Chowdhury S, Wu Y (2001) The Mahalanobis-Taguchi System. McGraw-Hill Professional","DOI":"10.1002\/9780470172247"},{"key":"2929_CR28","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1016\/j.wear.2017.02.017","volume":"376\u2013377","author":"M Rizal","year":"2017","unstructured":"Rizal M, Ghani JA, Nuawi MZ, Haron CHC (2017) Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi System. Wear 376\u2013377:1759\u20131765","journal-title":"Wear"},{"key":"2929_CR29","doi-asserted-by":"publisher","first-page":"960","DOI":"10.3969\/j.issn.1001-506X.2020.04.30","volume":"42","author":"ZM Peng","year":"2020","unstructured":"Peng ZM, Cheng LS, Zhan J et al (2020) Complex system health evaluation based on improved Mahalanobis system. System Engineering and Electronic Technolog 42:960\u2013968. https:\/\/doi.org\/10.3969\/j.issn.1001-506X.2020.04.30","journal-title":"System Engineering and Electronic Technolog"},{"key":"2929_CR30","doi-asserted-by":"publisher","unstructured":"Sakeran H, Osman NAA, Majid MSA (2019) Gait classification using Mahalanobis-Taguchi system for health monitoring systems following anterior cruciate ligament reconstruction. Appl Sci 9. https:\/\/doi.org\/10.3390\/app9163306","DOI":"10.3390\/app9163306"},{"key":"2929_CR31","doi-asserted-by":"publisher","first-page":"29078","DOI":"10.1109\/ACCESS.2018.2839765","volume":"6","author":"H Wang","year":"2018","unstructured":"Wang H, Huo N, Li J et al (2018) A Road Quality Detection Method Based on the Mahalanobis-Taguchi System. IEEE Access 6:29078\u201329087. https:\/\/doi.org\/10.1109\/ACCESS.2018.2839765","journal-title":"IEEE Access"},{"key":"2929_CR32","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.neucom.2017.03.075","volume":"249","author":"S Yu","year":"2017","unstructured":"Yu S, Huang TZ (2017) Exponential weighted entropy and exponential weighted mutual information. Neurocomputing 249:86\u201394. https:\/\/doi.org\/10.1016\/j.neucom.2017.03.075","journal-title":"Neurocomputing"},{"key":"2929_CR33","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.asoc.2018.07.047","volume":"72","author":"NA Nor","year":"2018","unstructured":"Nor NA, Ibrahim Z, Mubin M et al (2018) Improving particle swarm optimization via adaptive switching asynchronous \u2013 synchronous update. Appl Soft Comput J 72:298\u2013311. https:\/\/doi.org\/10.1016\/j.asoc.2018.07.047","journal-title":"Appl Soft Comput J"},{"key":"2929_CR34","doi-asserted-by":"publisher","first-page":"32890","DOI":"10.1109\/ACCESS.2018.2845366","volume":"6","author":"Z Han","year":"2018","unstructured":"Han Z, Li Y, Liang J (2018) Numerical Improvement for the Mechanical Performance of Bikes Based on an Intelligent PSO-ABC Algorithm and WSN Technology. IEEE Access 6:32890\u201332898. https:\/\/doi.org\/10.1109\/ACCESS.2018.2845366","journal-title":"IEEE Access"},{"key":"2929_CR35","doi-asserted-by":"publisher","first-page":"123041","DOI":"10.1016\/j.jclepro.2020.123041","volume":"273","author":"X Qian","year":"2020","unstructured":"Qian X, Jia S, Huang K et al (2020) Optimal design of Kaibel dividing wall columns based on improved particle swarm optimization methods. J Clean Prod 273:123041. https:\/\/doi.org\/10.1016\/j.jclepro.2020.123041","journal-title":"J Clean Prod"},{"key":"2929_CR36","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neucom.2017.03.086","volume":"270","author":"K Mason","year":"2017","unstructured":"Mason K, Duggan J, Howley E (2017) Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants. Neurocomputing 270:188\u2013197. https:\/\/doi.org\/10.1016\/j.neucom.2017.03.086","journal-title":"Neurocomputing"},{"key":"2929_CR37","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s12293-020-00306-5","volume":"12","author":"M Usman","year":"2020","unstructured":"Usman M, Pang W, Coghill GM (2020) Inferring structure and parameters of dynamic system models simultaneously using swarm intelligence approaches. Memetic Comput 12:267\u2013282. https:\/\/doi.org\/10.1007\/s12293-020-00306-5","journal-title":"Memetic Comput"},{"key":"2929_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2017.08.043","volume":"420","author":"Y Djenouri","year":"2017","unstructured":"Djenouri Y, Comuzzi M (2017) Combining Apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem. Inf Sci (Ny) 420:1\u201315. https:\/\/doi.org\/10.1016\/j.ins.2017.08.043","journal-title":"Inf Sci (Ny)"},{"key":"2929_CR39","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.swevo.2016.11.005","volume":"34","author":"AA Yahya","year":"2017","unstructured":"Yahya AA, Osman A, El-Bashir MS (2017) Rocchio algorithm-based particle initialization mechanism for effective PSO classification of high dimensional data. Swarm Evol Comput 34:18\u201332. https:\/\/doi.org\/10.1016\/j.swevo.2016.11.005","journal-title":"Swarm Evol Comput"},{"key":"2929_CR40","doi-asserted-by":"publisher","unstructured":"Marq J (2000) A program for automated analysis of Cardiotocograms. J Motern Fetal Med 9:311\u2013318. https:\/\/doi.org\/10.3109\/14767050009053454","DOI":"10.3109\/14767050009053454"},{"key":"2929_CR41","first-page":"75","volume":"1","author":"TJ Sejnowski","year":"1988","unstructured":"Sejnowski TJ (1988) Analysis of Hidden Units in a Layered Network. Technology 1:75\u201389","journal-title":"Technology"},{"key":"2929_CR42","unstructured":"Connectionist Bench (Sonar, Mines vs. Rocks) Data Set [Online]. Available: https:\/\/www.kaggle.com\/mattcarter865\/mines-vs-rocks"},{"key":"2929_CR43","first-page":"1","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre G, Nogueira F, Aridas CK (2017) Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res 18:1\u20135","journal-title":"J Mach Learn Res"},{"key":"2929_CR44","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1109\/JBHI.2018.2798062","volume":"22","author":"A Alberdi","year":"2018","unstructured":"Alberdi A, Weakley A, Schmitter-Edgecombe M et al (2018) Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer\u2019s Disease. IEEE J Biomed Heal Informatics 22:1720\u20131731. https:\/\/doi.org\/10.1109\/JBHI.2018.2798062","journal-title":"IEEE J Biomed Heal Informatics"},{"key":"2929_CR45","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.neucom.2020.03.085","volume":"402","author":"H Qin","year":"2020","unstructured":"Qin H, Zhou H, Cao J (2020) Imbalanced learning algorithm based intelligent abnormal electricity consumption detection. Neurocomputing 402:112\u2013123. https:\/\/doi.org\/10.1016\/j.neucom.2020.03.085","journal-title":"Neurocomputing"},{"key":"2929_CR46","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s10614-020-09975-3","volume":"56","author":"X Huang","year":"2020","unstructured":"Huang X, Zhang CZ, Yuan J (2020) Predicting Extreme Financial Risks on Imbalanced Dataset: A Combined Kernel FCM and Kernel SMOTE Based SVM Classifier. Comput Econ 56:187\u2013216. https:\/\/doi.org\/10.1007\/s10614-020-09975-3","journal-title":"Comput Econ"},{"key":"2929_CR47","doi-asserted-by":"publisher","first-page":"107060","DOI":"10.1016\/j.ymssp.2020.107060","volume":"146","author":"L Cheng","year":"2021","unstructured":"Cheng L, Yaghoubi V, Van Paepegem W, Kersemans M (2021) Mahalanobis classification system (MCS) integrated with binary particle swarm optimization for robust quality classification of complex metallic turbine blades. Mech Syst Signal Process 146:107060. https:\/\/doi.org\/10.1016\/j.ymssp.2020.107060","journal-title":"Mech Syst Signal Process"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02929-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02929-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02929-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T07:18:54Z","timestamp":1655709534000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02929-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,15]]},"references-count":47,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["2929"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02929-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,15]]},"assertion":[{"value":"17 October 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}