{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T11:49:05Z","timestamp":1782215345899,"version":"3.54.5"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032123848","type":"print"},{"value":"9783032123855","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\/978-3-032-12385-5_19","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:16:50Z","timestamp":1767320210000},"page":"308-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feasibility Study to Adapt Online Deep Learning Models for Immersive Environments"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1133-9096","authenticated-orcid":false,"given":"Agust\u00edn Alejandro","family":"Ortiz D\u00edaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2025-1638","authenticated-orcid":false,"given":"Sergio","family":"Cleger Tamayo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0562-2388","authenticated-orcid":false,"given":"Gabriel","family":"Dos Santos Lima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4288-8085","authenticated-orcid":false,"given":"Geovana Amorim","family":"Abensur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0517-3479","authenticated-orcid":false,"given":"Delrick Nunes","family":"De Oliveira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"8447","DOI":"10.1007\/s11042-022-13555-y","volume":"82","author":"Y Gupta","year":"2023","unstructured":"Gupta, Y., et al.: Deep learning model-based multimedia retrieval and its optimization in augmented reality applications. Multimed. Tools Appl. 82, 8447\u20138466 (2023). https:\/\/doi.org\/10.1007\/s11042-022-13555-y","journal-title":"Multimed. Tools Appl."},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Suzuki, K., et al.: Recognition and mapping of facial expressions to the avatar by embedded photo reflective sensors in head-mounted display. In: 2017 IEEE Virtual Reality (VR), USA (2017)","DOI":"10.1109\/VR.2017.7892245"},{"key":"19_CR3","unstructured":"Kwon, H., et al.: XRBench: An Extended Reality (XR) machine learning benchmark suite for the metaverse. In: Proceedings of the 6th MLSys Conference, Miami Beach, FL, USA, 2023.2211.08675, arXiv. http:\/\/arxiv.org\/abs\/2211.08675\/ (2023)"},{"key":"19_CR4","doi-asserted-by":"publisher","unstructured":"Alaskar, H., Saba, T.: Machine learning and deep learning: a comparative review. In: Singh Mer, K.K., Semwal, V.B., Bijalwan, V., Crespo, R.G. (eds.) Proceedings of Integrated Intelligence Enable Networks and Computing. Algorithms for Intelligent Systems. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-33-6307-6_15","DOI":"10.1007\/978-981-33-6307-6_15"},{"key":"19_CR5","doi-asserted-by":"publisher","unstructured":"Sahoo, D., et al.: Online Deep Learning: Learning Deep Neural Networks on the Fly (2018). https:\/\/doi.org\/10.48550\/arXiv.1711.03705","DOI":"10.48550\/arXiv.1711.03705"},{"issue":"4","key":"19_CR6","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama, J., et al.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"19_CR7","doi-asserted-by":"publisher","unstructured":"Bloch, A.: Online deep learning for behavior prediction. In: Proc. SPIE 12119, Open Architecture\/Open Business Model Net-Centric Systems and Defense Transformation (2022). https:\/\/doi.org\/10.1117\/12.2619359","DOI":"10.1117\/12.2619359"},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Yang, Z., et al.: Online deep learning for high-speed train traction motor temperature prediction. IEEE Trans. Transp. Electrification 10(1), 608\u2013622. https:\/\/doi.org\/10.1109\/TTE.2023","DOI":"10.1109\/TTE.2023"},{"key":"19_CR9","unstructured":"Amorim, G., et al.: Study and development of machine learning models designed for extended reality interactivity in real-time. In: HCII-2024 Conference, vol. 66. LNCS, vol. 15377, Late Breaking Work (2024)"},{"issue":"16","key":"19_CR10","doi-asserted-by":"publisher","first-page":"4903","DOI":"10.1080\/00207543.2020.1859636","volume":"59","author":"C Sahu","year":"2021","unstructured":"Sahu, C., Young, C., Rai, R.: Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review. Int. J. Prod. Res. 59(16), 4903\u20134959 (2021). https:\/\/doi.org\/10.1080\/00207543.2020.1859636","journal-title":"Int. J. Prod. Res."},{"key":"19_CR11","doi-asserted-by":"publisher","unstructured":"Cao, J., Lam, K., Lee, L., Liu, X., Hui, P., Su, X.: Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence (2021). https:\/\/doi.org\/10.1145\/3557999","DOI":"10.1145\/3557999"},{"key":"19_CR12","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1007\/s10055-020-00492-0","volume":"25","author":"M Liberatore","year":"2021","unstructured":"Liberatore, M., Wagner, W.: Virtual, mixed, and augmented reality: a systematic review for immersive systems research. Virtual Reality 25, 773\u2013799 (2021). https:\/\/doi.org\/10.1007\/s10055-020-00492-0","journal-title":"Virtual Reality"},{"key":"19_CR13","doi-asserted-by":"publisher","unstructured":"Orji, J., Chan, G., Orji, R.: Augmented Reality and Machine Learning in Health: A Systematic Review, pp. 59\u201367 (2023). https:\/\/doi.org\/10.1145\/3603421.3603430","DOI":"10.1145\/3603421.3603430"},{"key":"19_CR14","unstructured":"ML Kit|Google for Developers (2024). https:\/\/developers.google.com\/ml-kit"},{"key":"19_CR15","first-page":"1","volume":"2015","author":"A Ortiz","year":"2015","unstructured":"Ortiz, A., et al.: Fast adapting ensemble: a new algorithm for mining data streams with concept drift. Scientifiworld J. 2015, 1\u201314 (2015)","journal-title":"Scientifiworld J."},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1007\/s11063-019-09999-3","volume":"50","author":"C Zhang","year":"2019","unstructured":"Zhang, C., Zhang, Y., Shi, X., et al.: On incremental learning for gradient boosting decision trees. Neural. Process. Lett. 50, 957\u2013987 (2019). https:\/\/doi.org\/10.1007\/s11063-019-09999-3","journal-title":"Neural. Process. Lett."},{"key":"19_CR17","doi-asserted-by":"publisher","first-page":"1190","DOI":"10.3390\/e22111190","volume":"22","author":"Y Luo","year":"2020","unstructured":"Luo, Y., Yin, L., Bai, W., Mao, K.: An appraisal of incremental learning methods. Entropy 22, 1190 (2020). https:\/\/doi.org\/10.3390\/e22111190","journal-title":"Entropy"},{"key":"19_CR18","unstructured":"Lomonaco, V., Maltoni, D.: CORe50. a new dataset and benchmark for continuous object recognition. In: 1st Conference on Robot Learning (CoRL), Mountain View, United States (2017). http:\/\/vlomonaco.github.io\/core50"},{"key":"19_CR19","unstructured":"Tsymbal, A.: The problem of concept drift: definitions and related work, Tech. Rep. TCD-CS-2004-15, Department of Computer Science, Trinity College, Dublin, Ireland (2003)"},{"key":"19_CR20","doi-asserted-by":"publisher","unstructured":"Mari\u00f1o, L., Ortiz, A., Vasconcelos, G.: Comparative study of fast stacking ensembles families algorithms. Intelligent systems. BRACIS 2020. Lecture Notes in Computer Science, vol. 12320. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-61380-8_31","DOI":"10.1007\/978-3-030-61380-8_31"},{"issue":"1","key":"19_CR21","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1109\/TNN.2008.2008326","volume":"20","author":"M Muhlbaier","year":"2009","unstructured":"Muhlbaier, M., Topalis, A., Polikar, R.: Learn++. NC: combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes. IEEE Trans. Neural Networks 20(1), 152\u2013168 (2009). https:\/\/doi.org\/10.1109\/TNN.2008.2008326","journal-title":"IEEE Trans. Neural Networks"},{"key":"19_CR22","doi-asserted-by":"publisher","unstructured":"Liu, Y., Wang, Y., Zhang, J.: New machine learning algorithm: random forest. In: Liu, B., Ma, M., Chang, J. (eds.) Information Computing and Applications. ICICA 2012. LNCS, vol. 7473. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-34062-8_32","DOI":"10.1007\/978-3-642-34062-8_32"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Shahraki, A., Abbasi, M., and Haugen, \u00d8.: Boosting algorithms for network intrusion detection: A comparative evaluation of real AdaBoost gentle AdaBoost and modest AdaBoost.\u00a0Eng. Appl. Artif. Intell., vol. 94, Sep. 2020","DOI":"10.1016\/j.engappai.2020.103770"},{"key":"19_CR24","doi-asserted-by":"publisher","unstructured":"Ortiz, A., et al.: Fast adaptive stacking of ensembles adaptation for supporting active learning. a real case application. In: 4th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Huangshan, China, pp. 732\u2013738 (2018). https:\/\/doi.org\/10.1109\/FSKD.2018.8686851","DOI":"10.1109\/FSKD.2018.8686851"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Oza, N.: Online bagging and boosting. In: IEEE Int. Conf. Syst. Man Cybern., Waikoloa, HI, vol. 3, pp. 2340\u20132345 (2005)","DOI":"10.1109\/ICSMC.2005.1571498"},{"key":"19_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNN.2002.806953","volume":"14","author":"D Chakraborty","year":"2003","unstructured":"Chakraborty, D., Pal, N.: A novel training scheme for multilayered perceptrons to realize proper generalization and incremental learning. IEEE Trans. Neural Netw. 14, 1\u201314 (2003)","journal-title":"IEEE Trans. Neural Netw."},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Street, W., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: 7th ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining, pp. 377\u2013382 (2001)","DOI":"10.1145\/502512.502568"},{"key":"19_CR28","first-page":"2755","volume":"8","author":"J Kolter","year":"2007","unstructured":"Kolter, J., Maloof, M.: Dynamic weighted majority: an ensemble method for drifting concepts. J. Mach. Learn. Res. 8, 2755\u20132790 (2007)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR29","doi-asserted-by":"publisher","unstructured":"Liu, H., et al.: Incremental learning with neural networks for computer vision: a survey. Artif. Intell. Rev. 56, 4557\u20134589 (2023). https:\/\/doi.org\/10.1007\/s10462-022-10294-2","DOI":"10.1007\/s10462-022-10294-2"},{"key":"19_CR30","unstructured":"Ramasamy, S., et al.: Online Deep Learning: Growing RBM on the fly. 1803.02043 (2018). https:\/\/arxiv.org\/abs\/1803.02043"},{"key":"19_CR31","unstructured":"Uziel, G.: Deep Online Learning with Stochastic Constraints, 1905.10817 (2019). https:\/\/arxiv.org\/abs\/1905.10817"},{"key":"19_CR32","doi-asserted-by":"publisher","first-page":"5420","DOI":"10.1007\/s10489-020-02058-8","volume":"51","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Liu, J., Zuo, X., et al.: Online deep learning based on auto-encoder. Appl. Intell. 51, 5420\u20135439 (2021). https:\/\/doi.org\/10.1007\/s10489-020-02058-8","journal-title":"Appl. Intell."},{"key":"19_CR33","unstructured":"Valkanas, A., Oreshkin, B., Coates, M.: MODL: Multilearner Online Deep Learning, 2405.18281 (2024). https:\/\/arxiv.org\/abs\/2405.18281"},{"key":"19_CR34","unstructured":"Abdelfattah, M., Mehrotra, A., Dudziak, \u0141., Lane, N.: Zero-cost proxies for lightweight NAS. In: International Conference on Learning Representations (2021)"},{"key":"19_CR35","doi-asserted-by":"crossref","unstructured":"Cai, H., et al.: Efficient architecture search by network transformation. In: AAAI Conference on Artificial Intelligence (AAAI) (2018)","DOI":"10.1609\/aaai.v32i1.11709"},{"key":"19_CR36","unstructured":"Liu, H., Simonyan, K., Yang, Y.: DARTS: Differentiable architecture search. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"19_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, D., et al.: Econas: finding proxies for economical neural architecture search. In: Conference on Computer Vision and Pattern Recognition (CVPR), June 2020","DOI":"10.1109\/CVPR42600.2020.01141"},{"key":"19_CR38","unstructured":"Mellor, J., Turner, J., Storkey, A., Crowley, E.: Neural architecture search without training. In: Proceedings of the 38th International Conference on Machine Learning, PMLR 139 (2021)"},{"key":"19_CR39","unstructured":"Lee, N., et al.: Snip: single-shot network pruning based on connection sensitivity. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"19_CR40","unstructured":"Wang, Ch., Zhang, G., Grosse, R.: Picking winning tickets before training by preserving gradient flow. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"19_CR41","unstructured":"Tanaka, H., et al.: Pruning neural networks without any data by iteratively conserving synaptic flow. arXiv preprint arXiv:2006.05467 (2020)"},{"key":"19_CR42","unstructured":"Theis, L., et al.: Faster gaze prediction with dense networks and fisher pruning. arXiv:1801.05787 (2018)"},{"key":"19_CR43","doi-asserted-by":"crossref","unstructured":"Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. In: AAAI Conference on Artificial Intelligence (AAAI) (2019)","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"19_CR44","unstructured":"Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"19_CR45","unstructured":"Dudziak, L., Chau, T., Abdelfattah, M.S., Lee, R., Kim, H., Lane, N.D.: BRP-NAS: prediction-based NAS using GCNs. In: Neural Information Processing Systems (NeurIPS) (2020)"}],"container-title":["Lecture Notes in Computer Science","HCI International 2025 \u2013 Late Breaking Papers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-12385-5_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T11:00:22Z","timestamp":1782212422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-12385-5_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032123848","9783032123855"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-12385-5_19","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":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gothenburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","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":"22 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2025.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}