{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:10:38Z","timestamp":1771657838880,"version":"3.50.1"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032179142","type":"print"},{"value":"9783032179159","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\/978-3-032-17915-9_1","type":"book-chapter","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:36:42Z","timestamp":1771655802000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Online Learning Framework for\u00a0Optimizing Ballast Water Management Systems in\u00a0Maritime Environmental Protection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4872-8546","authenticated-orcid":false,"given":"Nadeem","family":"Iftikhar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5133-6688","authenticated-orcid":false,"given":"Xiufeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yi-Chen","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Finn Ebertsen","family":"Nordbjerg","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,22]]},"reference":[{"key":"1_CR1","unstructured":"Aden, A.: The Impact of the Mount Polley Tailings Pond Failure on the Sedimentary Record of Quesnel Lake, British Columbia. University of Toronto (Canada) (2018)"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"de\u00a0Ara\u00fajo, \u00c1.S., Rocha, O.P., Santos, A.\u00c1.B.: Computational model for condition analysis and equipment monitoring. In: 6th International Symposium on Innovation and Technology (SIINTEC) (2020)","DOI":"10.5151\/siintec2020-COMPUTATIONALMODEL"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Bakalar, G., Baggini, M.B., Bakalar, S.G.: Remote alarm reporting system responsive to stoppage of ballast water management operation on ships. In: 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1038\u20131043. IEEE (2017)","DOI":"10.23919\/MIPRO.2017.7973577"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Bifet, A., Gavald\u00e0, R., Holmes, G., Pfahringer, B.: Machine Learning for Data Streams. The MIT Press (2018)","DOI":"10.7551\/mitpress\/10654.001.0001"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: 21st International Conference on Machine Learning, p.\u00a018 (2004)","DOI":"10.1145\/1015330.1015432"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Cesa-Bianchi, N., Lugosi, G.: Prediction, Learning, and Games. Cambridge University Press (2006)","DOI":"10.1017\/CBO9780511546921"},{"issue":"3","key":"1_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1\u201327 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"1_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114060","volume":"166","author":"A Dogan","year":"2021","unstructured":"Dogan, A., Birant, D.: Machine learning and data mining in manufacturing. Expert Syst. Appl. 166, 114060 (2021)","journal-title":"Expert Syst. Appl."},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Domingos, P., Hulten, G.: Mining high-speed data streams. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71\u201380 (2000)","DOI":"10.1145\/347090.347107"},{"issue":"7","key":"1_CR10","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi, J., Hasan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(7), 2121\u20132159 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"1_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/978-3-030-91608-4_26","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2021","author":"L Ferreira","year":"2021","unstructured":"Ferreira, L., Pilastri, A., Sousa, V., Romano, F., Cortez, P.: Prediction of maintenance equipment failures using automated machine learning. In: Yin, H., et al. (eds.) IDEAL 2021. LNCS, vol. 13113, pp. 259\u2013267. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-91608-4_26"},{"issue":"1","key":"1_CR12","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119\u2013139 (1997)","journal-title":"J. Comput. Syst. Sci."},{"key":"1_CR13","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.envint.2018.12.038","volume":"124","author":"WA Gerhard","year":"2019","unstructured":"Gerhard, W.A., Gunsch, C.K.: Metabarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports. Environ. Int. 124, 312\u2013319 (2019)","journal-title":"Environ. Int."},{"issue":"2","key":"1_CR14","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.4249\/scholarpedia.1888","volume":"8","author":"S Grossberg","year":"2013","unstructured":"Grossberg, S.: Recurrent neural networks. Scholarpedia 8(2), 1888 (2013)","journal-title":"Scholarpedia"},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s13437-014-0067-8","volume":"14","author":"FT Hansen","year":"2015","unstructured":"Hansen, F.T., Potthoff, M., Uhrenholdt, T., Vo, H.D., Linden, O., Andersen, J.H.: Development of a prototype tool for ballast water risk management using a combination of hydrodynamic models and agent-based modeling. WMU J. Marit. Aff. 14, 219\u2013245 (2015)","journal-title":"WMU J. Marit. Aff."},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process. Mag. 29(6), 82\u201397 (2012)","DOI":"10.1109\/MSP.2012.2205597"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Hopkinson, J., Perera, N., Kiazim, E.: Investigating reliability centered maintenance (RCM) for public road mass transportation vehicles. In: MATEC Web of Conferences, vol.\u00a081, p. 08006. EDP Sciences (2016)","DOI":"10.1051\/matecconf\/20168108006"},{"issue":"2","key":"1_CR18","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/72.991427","volume":"13","author":"CW Hsu","year":"2002","unstructured":"Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 13(2), 415\u2013425 (2002)","journal-title":"IEEE Trans. Neural Networks"},{"issue":"4","key":"1_CR19","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1080\/00224065.1986.11979014","volume":"18","author":"JS Hunter","year":"1986","unstructured":"Hunter, J.S.: The exponentially weighted moving average. J. Qual. Technol. 18(4), 203\u2013210 (1986)","journal-title":"J. Qual. Technol."},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Iftikhar, N., Dohot, A.M.: Condition based maintenance on data streams in industry 4.0. In: 3rd International Conference on Innovative Intelligent Industrial Production and Logistics, pp. 173\u2013144. SCITEPRESS (2022)","DOI":"10.5220\/0011553500003329"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Iftikhar, N., Lin, Y.C., Liu, X., Nordbjerg, F.E.: Online machine learning for adaptive ballast water management. In: 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA, pp. 27\u201338. SCITEPRESS (2024)","DOI":"10.5220\/0012728700003756"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Iftikhar, N., Lin, Y.C., Nordbjerg, F.E.: Machine learning based predictive maintenance in manufacturing industry. In: 3rd International Conference on Innovative Intelligent Industrial Production and Logistics, pp. 85\u201393. SCITEPRESS (2022)","DOI":"10.5220\/0011537300003329"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Iv\u010de, R., Zeki\u0107, A., Mohovi\u0107, D., Kri\u0161kovi\u0107, A.: Review of ballast water management. In: International Symposium ELMAR, pp. 189\u2013192. IEEE (2021)","DOI":"10.1109\/ELMAR52657.2021.9551002"},{"issue":"10","key":"1_CR24","doi-asserted-by":"publisher","first-page":"817","DOI":"10.3390\/jmse8100817","volume":"8","author":"PG Jang","year":"2020","unstructured":"Jang, P.G., Hyun, B., Shin, K.: Ballast water treatment performance evaluation under real changing conditions. J. Mar. Sci. Eng. 8(10), 817 (2020)","journal-title":"J. Mar. Sci. Eng."},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press (2011)","DOI":"10.1017\/CBO9780511921803"},{"key":"1_CR26","unstructured":"Karomah, A., Silviany, I., Puspitasari, N., Azhar, N.R., Kholiq, A.: Online versus offline learning based on students\u2019 subjective appraisement. In: 3rd International Seminar on Education and Human Technology (ISEHT) (2022)"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Katsikas, S., et al.: Challenges to taking advantage of high frequency data analytics to address environmental challenges in maritime sector. In: SNAME International Symposium on Ship Operations, Management and Economics (2023)","DOI":"10.5957\/SOME-2023-006"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Kim, J., Woo, S.S.: Efficient two-stage model retraining for machine unlearning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 4361\u20134369 (2022)","DOI":"10.1109\/CVPRW56347.2022.00482"},{"issue":"1","key":"1_CR29","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.renene.2010.05.014","volume":"36","author":"A Kusiak","year":"2011","unstructured":"Kusiak, A., Li, W.: The prediction and diagnosis of wind turbine faults. Renewable Energy 36(1), 16\u201323 (2011)","journal-title":"Renewable Energy"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Lea, C., Flynn, M.D., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks for action segmentation and detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 156\u2013165 (2017)","DOI":"10.1109\/CVPR.2017.113"},{"issue":"4","key":"1_CR31","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","volume":"37","author":"B Lim","year":"2021","unstructured":"Lim, B., Ar\u0131k, S.\u00d6., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int. J. Forecast. 37(4), 1748\u20131764 (2021)","journal-title":"Int. J. Forecast."},{"key":"1_CR32","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.jcp.2016.05.003","volume":"318","author":"J Ling","year":"2016","unstructured":"Ling, J., Jones, R., Templeton, J.: Machine learning strategies for systems with invariance properties. J. Comput. Phys. 318, 22\u201335 (2016)","journal-title":"J. Comput. Phys."},{"key":"1_CR33","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.is.2018.05.007","volume":"77","author":"X Liu","year":"2018","unstructured":"Liu, X., Nielsen, P.S.: Scalable prediction-based online anomaly detection for smart meter data. Inf. Syst. 77, 34\u201347 (2018)","journal-title":"Inf. Syst."},{"issue":"24","key":"1_CR34","doi-asserted-by":"publisher","first-page":"17389","DOI":"10.1007\/s00521-021-06326-7","volume":"33","author":"P Majumder","year":"2021","unstructured":"Majumder, P., Baidya, D., Majumder, M.: Application of novel intuitionistic fuzzy bwahp process for analysing the efficiency of water treatment plant. Neural Comput. Appl. 33(24), 17389\u201317405 (2021)","journal-title":"Neural Comput. Appl."},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Onyena, A.P., Nwaogbe, O.R.: Assessment of water quality and heavy metal contamination in ballast water: implications for marine ecosystems and human health. Maritime Technol. Res. 6(4) (2024)","DOI":"10.33175\/mtr.2024.270227"},{"issue":"1\/2","key":"1_CR36","doi-asserted-by":"publisher","first-page":"100","DOI":"10.2307\/2333009","volume":"41","author":"ES Page","year":"1954","unstructured":"Page, E.S.: Continuous inspection schemes. Biometrika 41(1\/2), 100\u2013115 (1954)","journal-title":"Biometrika"},{"key":"1_CR37","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"1_CR38","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s41549-022-00066-w","volume":"18","author":"JM Pinto","year":"2022","unstructured":"Pinto, J.M., Castle, J.L.: Machine learning dynamic switching approach to forecasting in the presence of structural breaks. J. Bus. Cycle Res. 18(2), 129\u2013157 (2022)","journal-title":"J. Bus. Cycle Res."},{"issue":"3","key":"1_CR39","doi-asserted-by":"publisher","first-page":"324","DOI":"10.3390\/w10030324","volume":"10","author":"F Pontius","year":"2018","unstructured":"Pontius, F.: Treatability of a highly-impaired, saline surface water for potential urban water use. Water 10(3), 324 (2018)","journal-title":"Water"},{"key":"1_CR40","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s13222-021-00386-8","volume":"21","author":"I Prapas","year":"2021","unstructured":"Prapas, I., Derakhshan, B., Mahdiraji, A.R., Markl, V.: Continuous training and deployment of deep learning models. Datenbank-Spektrum 21, 203\u2013212 (2021)","journal-title":"Datenbank-Spektrum"},{"issue":"2","key":"1_CR41","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3390\/jmse6020069","volume":"6","author":"V Rata","year":"2018","unstructured":"Rata, V., Gasparotti, C., Rusu, L.: Ballast water management in the black sea\u2019s ports. J. Mar. Sci. Eng. 6(2), 69 (2018)","journal-title":"J. Mar. Sci. Eng."},{"issue":"2","key":"1_CR42","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1061\/(ASCE)0733-9496(1996)122:2(137)","volume":"122","author":"LA Rossman","year":"1996","unstructured":"Rossman, L.A., Boulos, P.F.: Numerical methods for modeling water quality in distribution systems: a comparison. J. Water Resour. Plan. Manag. 122(2), 137\u2013146 (1996)","journal-title":"J. Water Resour. Plan. Manag."},{"key":"1_CR43","unstructured":"Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson (2016)"},{"key":"1_CR44","unstructured":"Schelter, S., Biessmann, F., Januschowski, T., Salinas, D., Seufert, S., Szarvas, G.: On challenges in machine learning model management. IEEE Data Engineering Bulletin (2018). https:\/\/www.amazon.science\/publications\/on-challenges-in-machine-learning-model-management"},{"key":"1_CR45","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.marpol.2015.05.014","volume":"59","author":"DR Scriven","year":"2015","unstructured":"Scriven, D.R., DiBacco, C., Locke, A., Therriault, T.W.: Ballast water management in Canada: a historical perspective and implications for the future. Mar. Policy 59, 121\u2013133 (2015)","journal-title":"Mar. Policy"},{"key":"1_CR46","doi-asserted-by":"crossref","unstructured":"Simeonova, A., Kralev, P.: Onshore ballast water management systems: National perspectives. Annual Journal of Technical University of Varna, Bulgaria 7(1), 1\u201311 (2023)","DOI":"10.29114\/ajtuv.vol7.iss1.288"},{"key":"1_CR47","doi-asserted-by":"crossref","unstructured":"Singh, K., Selvanathan, B., Zope, K., Nistala, S.H., Runkana, V.: Concurrent estimation of remaining useful life for multiple faults in an ion etch mill: a data-driven approach. In: Annual Conference of the PHM Society, vol.\u00a010 (2018)","DOI":"10.36001\/phmconf.2018.v10i1.591"},{"key":"1_CR48","doi-asserted-by":"publisher","first-page":"978","DOI":"10.4028\/www.scientific.net\/AMM.687-691.978","volume":"687","author":"YP Tian","year":"2014","unstructured":"Tian, Y.P., Ye, X.H., Yin, M.: Electronic equipment combination fault prediction technology research based on LSSVM-HMM. Appl. Mech. Mater. 687, 978\u2013983 (2014)","journal-title":"Appl. Mech. Mater."},{"key":"1_CR49","unstructured":"Vaswani, A., et al.: Attention is all you need. In: 31st Conference on Neural Information Processing Systems (NIPS 2017) (2017)"},{"key":"1_CR50","doi-asserted-by":"crossref","unstructured":"Weiwei, W., Pratama, M., Ashfahani, A., Yapp Kien\u00a0Yee, E.: Online semisupervised learning approach for quality monitoring of complex manufacturing process. Complexity 2021(1), 3005276 (2021)","DOI":"10.1155\/2021\/3005276"},{"key":"1_CR51","unstructured":"Wu, Y., Dobriban, E., Davidson, S.: Deltagrad: rapid retraining of machine learning models. In: International Conference on Machine Learning, pp. 10355\u201310366 (2020)"},{"key":"1_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.marpolbul.2023.115825","volume":"198","author":"A Zendehboudi","year":"2024","unstructured":"Zendehboudi, A., et al.: Analysis of microplastics in ships ballast water and its ecological risk assessment studies from the persian gulf. Mar. Pollut. Bull. 198, 115825 (2024)","journal-title":"Mar. Pollut. Bull."},{"key":"1_CR53","unstructured":"Zhang, J., Elnikety, S., Zarar, S., Gupta, A., Garg, S.: Model-switching: dealing with fluctuating workloads in machine-learning-as-a-service systems. In: 12th USENIX Workshop on Hot Topics in Cloud Computing (2020)"}],"container-title":["Communications in Computer and Information Science","Data Management Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-17915-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:36:48Z","timestamp":1771655808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-17915-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032179142","9783032179159"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-17915-9_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"22 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DATA 2024","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Management Technologies and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dijon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"data2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/data.scitevents.org\/?y=2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}