{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T10:01:05Z","timestamp":1780999265022,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":46,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819573936","type":"print"},{"value":"9789819573943","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-7394-3_4","type":"book-chapter","created":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T23:45:01Z","timestamp":1778456701000},"page":"42-60","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Adaptive Resonance Theory Model for\u00a0Online Noisy Data Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5383-6375","authenticated-orcid":false,"given":"Michael","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0269-2624","authenticated-orcid":false,"given":"Jiao","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8465-0996","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4191-9083","authenticated-orcid":false,"given":"Chee Peng","family":"Lim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0221-6361","authenticated-orcid":false,"given":"Jinli","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-9215","authenticated-orcid":false,"given":"Zhonglong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1023\/A:1013689704352","volume":"47","author":"P Auer","year":"2002","unstructured":"Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47, 235\u2013256 (2002)","journal-title":"Mach. Learn."},{"key":"4_CR2","doi-asserted-by":"publisher","unstructured":"Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 245\u2013250. KDD \u201901, Association for Computing Machinery, New York, NY, USA (2001). https:\/\/doi.org\/10.1145\/502512.502546","DOI":"10.1145\/502512.502546"},{"issue":"5","key":"4_CR3","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1109\/72.159059","volume":"3","author":"G Carpenter","year":"1992","unstructured":"Carpenter, G., Grossberg, S., Markuzon, N., Reynolds, J., Rosen, D.: Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Netw. 3(5), 698\u2013713 (1992). https:\/\/doi.org\/10.1109\/72.159059","journal-title":"IEEE Trans. Neural Netw."},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Chen, G., Wang, M., Han, S., Yin, J., Wang, H., Cao, J.: Deep reinforcement learning-based cloud-edge offloading for wbans. IEEE Trans. Consum. Electron. PP, 1 (2024). https:\/\/doi.org\/10.1109\/TCE.2024.3504545","DOI":"10.1109\/TCE.2024.3504545"},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"7435","DOI":"10.1007\/s10586-018-1772-4","volume":"22","author":"Y Chen","year":"2019","unstructured":"Chen, Y., Xiong, J., Xu, W., Zuo, J.: A novel online incremental and decremental learning algorithm based on variable support vector machine. Clust. Comput. 22, 7435\u20137445 (2019)","journal-title":"Clust. Comput."},{"key":"4_CR6","unstructured":"Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551\u2013585 (2006)"},{"key":"4_CR7","doi-asserted-by":"publisher","unstructured":"Dredze, M., Crammer, K., Pereira, F.: Confidence-weighted linear classification. In: Proceedings of the 25th International Conference on Machine Learning, pp. 264\u2013271. ICML \u201908, Association for Computing Machinery, New York, NY, USA (2008). https:\/\/doi.org\/10.1145\/1390156.1390190","DOI":"10.1145\/1390156.1390190"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Freund, Y., Schapire, R.E.: Large margin classification using the perceptron algorithm. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 209\u2013217 (1998)","DOI":"10.1145\/279943.279985"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Furao, S., Sudo, A., Hasegawa, O.: An online incremental learning pattern-based reasoning system. Neural Netw. 23(1), 135\u2013143 (2010)","DOI":"10.1016\/j.neunet.2009.06.002"},{"issue":"8","key":"4_CR10","doi-asserted-by":"publisher","first-page":"3584","DOI":"10.1109\/TCYB.2025.3573292","volume":"55","author":"YF Ge","year":"2025","unstructured":"Ge, Y.F., Wang, H., Bertino, E., Cao, J., Zhang, Y.: Multiobjective privacy-preserving task assignment in spatial crowdsourcing. IEEE Trans. Cybern. 55(8), 3584\u20133597 (2025). https:\/\/doi.org\/10.1109\/TCYB.2025.3573292","journal-title":"IEEE Trans. Cybern."},{"issue":"4","key":"4_CR11","doi-asserted-by":"publisher","first-page":"2296","DOI":"10.1109\/TDSC.2023.3302284","volume":"21","author":"YF Ge","year":"2024","unstructured":"Ge, Y.F., et al.: Evolutionary dynamic database partitioning optimization for privacy and utility. IEEE Trans. Dependable Secure Comput. 21(4), 2296\u20132311 (2024). https:\/\/doi.org\/10.1109\/TDSC.2023.3302284","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"4_CR12","doi-asserted-by":"publisher","unstructured":"He, J., Mao, R., Shao, Z., Zhu, F.: Incremental learning in online scenario. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13923\u201313932 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01394","DOI":"10.1109\/CVPR42600.2020.01394"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Hu, C., Chen, Y., Hu, L., Peng, X.: A novel random forests based class incremental learning method for activity recognition. Pattern Recogn. 78, 277\u2013290 (2018)","DOI":"10.1016\/j.patcog.2018.01.025"},{"issue":"11","key":"4_CR14","doi-asserted-by":"publisher","first-page":"7308","DOI":"10.1109\/TKDE.2024.3418098","volume":"36","author":"CQ Huang","year":"2024","unstructured":"Huang, C.Q., et al.: XKT: toward explainable knowledge tracing model with cognitive learning theories for questions of multiple knowledge concepts. IEEE Trans. Knowl. Data Eng. 36(11), 7308\u20137325 (2024). https:\/\/doi.org\/10.1109\/TKDE.2024.3418098","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"Jin, M., Xu, Z., Li, R., Wu, D.: Fuzzy artmap ensemble based decision making and application. Math. Probl. Eng. 124263 (2013). https:\/\/doi.org\/10.1155\/2013\/124263","DOI":"10.1155\/2013\/124263"},{"issue":"6","key":"4_CR16","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TMI.2022.3141425","volume":"41","author":"L Ju","year":"2022","unstructured":"Ju, L., et al.: Improving medical images classification with label noise using dual-uncertainty estimation. IEEE Trans. Med. Imaging 41(6), 1533\u20131546 (2022). https:\/\/doi.org\/10.1109\/TMI.2022.3141425","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR17","doi-asserted-by":"publisher","unstructured":"Kaur, R., Gulia, P., Gill, N., Chatterjee, J.: Big data privacy preservation using principal component analysis and random projection in healthcare. Math. Probl. Eng. 2022, 1\u201312 (2022). https:\/\/doi.org\/10.1155\/2022\/6402274","DOI":"10.1155\/2022\/6402274"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521\u20133526 (2017)","DOI":"10.1073\/pnas.1611835114"},{"issue":"8","key":"4_CR19","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1109\/TSP.2004.830991","volume":"52","author":"J Kivinen","year":"2004","unstructured":"Kivinen, J., Smola, A., Williamson, R.: Online learning with kernels. IEEE Trans. Signal Process. 52(8), 2165\u20132176 (2004). https:\/\/doi.org\/10.1109\/TSP.2004.830991","journal-title":"IEEE Trans. Signal Process."},{"key":"4_CR20","doi-asserted-by":"publisher","unstructured":"Li, R., et al.: Open-vocabulary multi-object tracking with domain generalized and temporally adaptive features. IEEE Trans. Multimed. 27, 3009\u20133022 (2025). https:\/\/doi.org\/10.1109\/TMM.2025.3557619","DOI":"10.1109\/TMM.2025.3557619"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, S., Xu, S., Yin, J.: Trustworthy semi-supervised anomaly detection for online-to-offline logistics business in merchant identification. CAAI Trans. Intell. Technol. (2024)","DOI":"10.1049\/cit2.12301"},{"key":"4_CR22","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1023\/A:1022869011914","volume":"2","author":"N Littlestone","year":"1988","unstructured":"Littlestone, N.: Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Mach. Learn. 2, 285\u2013318 (1988)","journal-title":"Mach. Learn."},{"key":"4_CR23","unstructured":"Liu, Y.: Understanding instance-level label noise: Disparate impacts and treatments. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 6725\u20136735. PMLR (2021). https:\/\/proceedings.mlr.press\/v139\/liu21a.html"},{"key":"4_CR24","doi-asserted-by":"publisher","unstructured":"Manapragada, C., Salehi, M., Webb, G.I.: Extremely fast hoeffding adaptive tree. In: 2022 IEEE International Conference on Data Mining (ICDM), pp. 319\u2013328 (2022). https:\/\/doi.org\/10.1109\/ICDM54844.2022.00042","DOI":"10.1109\/ICDM54844.2022.00042"},{"key":"4_CR25","doi-asserted-by":"publisher","unstructured":"Manapragada, C., Webb, G.I., Salehi, M.: Extremely fast decision tree. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1953\u20131962. KDD \u201918, Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3219819.3220005, https:\/\/doi.org\/10.1145\/3219819.3220005","DOI":"10.1145\/3219819.3220005"},{"key":"4_CR26","doi-asserted-by":"publisher","first-page":"99129","DOI":"10.1109\/ACCESS.2022.3207287","volume":"10","author":"ID Mienye","year":"2022","unstructured":"Mienye, I.D., Sun, Y.: A survey of ensemble learning: concepts, algorithms, applications, and prospects. IEEE Access 10, 99129\u201399149 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3207287","journal-title":"IEEE Access"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-S\u00e1nchez, B., Fontenla-Romero, O., Guijarro-Berdi\u00f1as, B.: A review of adaptive online learning for artificial neural networks. Artif. Intell. Rev. 49(2), 281\u2013299 (2018)","DOI":"10.1007\/s10462-016-9526-2"},{"key":"4_CR28","doi-asserted-by":"publisher","unstructured":"Polikar, R., Upda, L., Upda, S., Honavar, V.: Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 31(4), 497\u2013508 (2001). https:\/\/doi.org\/10.1109\/5326.983933","DOI":"10.1109\/5326.983933"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: ICARL: incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"4_CR30","doi-asserted-by":"publisher","unstructured":"Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386\u2013408 (1958). https:\/\/doi.org\/10.1037\/h0042519","DOI":"10.1037\/h0042519"},{"key":"4_CR31","doi-asserted-by":"publisher","unstructured":"Ruping, S.: Incremental learning with support vector machines. In: Proceedings 2001 IEEE International Conference on Data Mining, pp. 641\u2013642 (2001). https:\/\/doi.org\/10.1109\/ICDM.2001.989589","DOI":"10.1109\/ICDM.2001.989589"},{"key":"4_CR32","doi-asserted-by":"publisher","unstructured":"Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 1393\u20131400 (2009). https:\/\/doi.org\/10.1109\/ICCVW.2009.5457447","DOI":"10.1109\/ICCVW.2009.5457447"},{"key":"4_CR33","doi-asserted-by":"publisher","unstructured":"Sarwar, S.S., Panda, P., Roy, K.: Gabor filter assisted energy efficient fast learning convolutional neural networks. In: 2017 IEEE\/ACM International Symposium on Low Power Electronics and Design (ISLPED), pp.\u00a01\u20136 (2017). https:\/\/doi.org\/10.1109\/ISLPED.2017.8009202","DOI":"10.1109\/ISLPED.2017.8009202"},{"key":"4_CR34","doi-asserted-by":"crossref","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"4_CR35","unstructured":"Sutton, R.S., Barto, A.G., et\u00a0al.: Reinforcement Learning: An Introduction, vol.\u00a01. MIT Press, Cambridge (1998)"},{"key":"4_CR36","doi-asserted-by":"publisher","unstructured":"Tang, Z., Yan, X.: Voting algorithm of fuzzy artmap and its application to fault diagnosis. In: Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), vol.\u00a04, pp. 535\u2013538 (2007). https:\/\/doi.org\/10.1109\/FSKD.2007.611","DOI":"10.1109\/FSKD.2007.611"},{"issue":"5","key":"4_CR37","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1109\/TCDS.2024.3386364","volume":"16","author":"MNA Tawhid","year":"2024","unstructured":"Tawhid, M.N.A., Siuly, S., Wang, K., Wang, H.: Genet: a generic neural network for detecting various neurological disorders from EEG. IEEE Trans. Cogn. Dev. Syst. 16(5), 1829\u20131842 (2024). https:\/\/doi.org\/10.1109\/TCDS.2024.3386364","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"4_CR38","doi-asserted-by":"publisher","unstructured":"Triguero, I., et al.: Keel 3.0: an open source software for multi-stage analysis in data mining. Int. J. Comput. Intell. Syst. 10, 1238\u20131249 (2017). https:\/\/doi.org\/10.2991\/ijcis.10.1.82","DOI":"10.2991\/ijcis.10.1.82"},{"key":"4_CR39","doi-asserted-by":"publisher","unstructured":"Vermorel, J., Mohri, M.: Multi-armed bandit algorithms and empirical evaluation. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) Machine Learning: ECML 2005. ECML 2005. LNCS, vol 3720, pp. 437\u2013448. Springer, Berlin, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564096_42","DOI":"10.1007\/11564096_42"},{"issue":"03","key":"4_CR40","first-page":"471","volume":"33","author":"L Wang","year":"2018","unstructured":"Wang, L., Meng, J.: Incremental clustering algorithm based on bayesian adaptive resonance theory based on local distribution. Control Decision 33(03), 471\u2013478 (2018)","journal-title":"Control Decision"},{"key":"4_CR41","doi-asserted-by":"publisher","unstructured":"Wang, M., Han, S., Chen, G., Yin, J., Cao, J.: Blockchain-empowered resource allocation and data security for efficient vehicular edge computing. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds.) Web Information Systems Engineering \u2013 WISE 2023. WISE 2023. LNCS, vol. 14306, pp. 205\u2013219. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-99-7254-8_16","DOI":"10.1007\/978-981-99-7254-8_16"},{"key":"4_CR42","doi-asserted-by":"crossref","unstructured":"Xie, T., Peng, Y., Wang, C.: HI-RF: incremental learning random forest for large-scale multi-class data classification (2016). https:\/\/arxiv.org\/abs\/1608.08761","DOI":"10.2991\/aiie-16.2016.72"},{"key":"4_CR43","doi-asserted-by":"publisher","unstructured":"Yin, J., Chen, G., Hong, W., Wang, H., Cao, J., Miao, Y.: Empowering vulnerability prioritization: a heterogeneous graph-driven framework for exploitability prediction. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds.) Web Information Systems Engineering \u2013 WISE 2023. WISE 2023. LNCS, vol. 14306, pp. 289\u2013299. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-99-7254-8_23","DOI":"10.1007\/978-981-99-7254-8_23"},{"key":"4_CR44","doi-asserted-by":"publisher","unstructured":"Yin, J., Tang, M., Cao, J., Wang, H., You, M., Lin, Y.: Adaptive online learning for vulnerability exploitation time prediction. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds.) Web Information Systems Engineering \u2013 WISE 2020. WISE 2020. LNCS, vol. 12343, pp. 252\u2013266. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-62008-0_18, http:\/\/dx.doi.org\/10.1007\/978-3-030-62008-0_18","DOI":"10.1007\/978-3-030-62008-0_18"},{"key":"4_CR45","doi-asserted-by":"publisher","unstructured":"You, M., Yin, J., Wang, H., Cao, J., Miao, Y.: A minority class boosted framework for adaptive access control decision-making. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds.) Web Information Systems Engineering \u2013 WISE 2021. WISE 2021. LNCS, vol. 13080, pp. 143\u2013157. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-90888-1_12","DOI":"10.1007\/978-3-030-90888-1_12"},{"key":"4_CR46","doi-asserted-by":"crossref","unstructured":"You, M., et al.: A knowledge graph empowered online learning framework for access control decision-making. World Wide Web 26(2), 827\u2013848 (2022)","DOI":"10.1007\/s11280-022-01076-5"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering - WISE 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7394-3_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T09:29:45Z","timestamp":1780997385000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7394-3_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819573936","9789819573943"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7394-3_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakech","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wise2025.ficloud.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}