{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T07:19:40Z","timestamp":1778829580543,"version":"3.51.4"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032156372","type":"print"},{"value":"9783032156389","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\/978-3-032-15638-9_43","type":"book-chapter","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T12:38:00Z","timestamp":1770727080000},"page":"723-741","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From High-Frequency Sensors to\u00a0Noon Reports: Using Transfer Learning for\u00a0Shaft Power Prediction in\u00a0Maritime"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4623-7938","authenticated-orcid":false,"given":"Akriti","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5053-4954","authenticated-orcid":false,"given":"Dogan","family":"Altan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9345-5431","authenticated-orcid":false,"given":"Dusica","family":"Marijan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arnbj\u00f8rn","family":"Maressa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"43_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jclepro.2017.12.247","volume":"178","author":"R Adland","year":"2018","unstructured":"Adland, R., Cariou, P., Jia, H., Wolff, F.C.: The energy efficiency effects of periodic ship hull cleaning. J. Clean. Prod. 178, 1\u201313 (2018)","journal-title":"J. Clean. Prod."},{"key":"43_CR2","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.oceaneng.2015.05.043","volume":"110","author":"L Aldous","year":"2015","unstructured":"Aldous, L., Smith, T., Bucknall, R., Thompson, P.: Uncertainty analysis in ship performance monitoring. Ocean Eng. 110, 29\u201338 (2015)","journal-title":"Ocean Eng."},{"key":"43_CR3","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.cor.2015.04.004","volume":"66","author":"E Bal Be\u015fik\u00e7i","year":"2016","unstructured":"Bal Be\u015fik\u00e7i, E., Arslan, O., Turan, O., \u00d6l\u00e7er, A.: An artificial neural network based decision support system for energy efficient ship operations. Comput. Oper. Res. 66, 393\u2013401 (2016)","journal-title":"Comput. Oper. Res."},{"issue":"6","key":"43_CR4","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1080\/17445302.2023.2214490","volume":"19","author":"M Bayraktar","year":"2023","unstructured":"Bayraktar, M., Sokukcu, M.: Marine vessel energy efficiency performance prediction based on daily reported noon reports. Ships Offshore Struct. 19(6), 831\u2013840 (2023)","journal-title":"Ships Offshore Struct."},{"key":"43_CR5","volume-title":"Marine Propellers and Propulsion","author":"J Carlton","year":"2012","unstructured":"Carlton, J.: Marine Propellers and Propulsion, 3rd edn. Butterworth-Heinemann, Oxford, England (2012)","edition":"3"},{"key":"43_CR6","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.oceaneng.2016.11.058","volume":"130","author":"A Coraddu","year":"2017","unstructured":"Coraddu, A., Oneto, L., Baldi, F., Anguita, D.: Vessels fuel consumption forecast and trim optimisation: a data analytics perspective. Ocean Eng. 130, 351\u2013370 (2017)","journal-title":"Ocean Eng."},{"key":"43_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.commtr.2022.100072","volume":"2","author":"Y Du","year":"2022","unstructured":"Du, Y., Chen, Y., Li, X., Sch\u00f6nborn, A., Sun, Z.: Data fusion and machine learning for ship fuel efficiency modeling: part III \u2013 sensor data and meteorological data. Commun. Transp. Res. 2, 100072 (2022)","journal-title":"Commun. Transp. Res."},{"key":"43_CR8","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.trb.2019.02.004","volume":"122","author":"Y Du","year":"2019","unstructured":"Du, Y., Meng, Q., Wang, S., Kuang, H.: Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data. Transp. Res. Part B Methodological 122, 88\u2013114 (2019)","journal-title":"Transp. Res. Part B Methodological"},{"key":"43_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2019.106282","volume":"188","author":"C Gkerekos","year":"2019","unstructured":"Gkerekos, C., Lazakis, I., Theotokatos, G.: Machine learning models for predicting ship main engine fuel oil consumption: a comparative study. Ocean Eng. 188, 106282 (2019)","journal-title":"Ocean Eng."},{"issue":"363","key":"43_CR10","first-page":"272","volume":"31","author":"J Holtrop","year":"1984","unstructured":"Holtrop, J.: A statistical re-analysis of resistance and propulsion data. Int. Shipbuild. Prog. 31(363), 272\u2013276 (1984)","journal-title":"Int. Shipbuild. Prog."},{"key":"43_CR11","unstructured":"Lakshmynarayanana, P.A., Hudson, D.: Estimating added power in waves for ships through analysis of operational data. In: Bertram, V. (ed.) 2nd Hull Performance and Insight Conference (HullPIC\u201917), pp. 253\u2013264 (2017)"},{"key":"43_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.108886","volume":"234","author":"A Laurie","year":"2021","unstructured":"Laurie, A., Anderlini, E., Dietz, J., Thomas, G.: Machine learning for shaft power prediction and analysis of fouling related performance deterioration. Ocean Eng. 234, 108886 (2021)","journal-title":"Ocean Eng."},{"key":"43_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.commtr.2022.100074","volume":"2","author":"X Li","year":"2022","unstructured":"Li, X.: Data fusion and machine learning for ship fuel efficiency modeling: part I \u2013 voyage report data and meteorological data. Commun. Transp. Res. 2, 100074 (2022)","journal-title":"Commun. Transp. Res."},{"key":"43_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2020.107357","volume":"206","author":"S Liu","year":"2020","unstructured":"Liu, S., Papanikolaou, A.: Regression analysis of experimental data for added resistance in waves of arbitrary heading and development of a semi-empirical formula. Ocean Eng. 206, 107357 (2020). https:\/\/doi.org\/10.1016\/j.oceaneng.2020.107357","journal-title":"Ocean Eng."},{"key":"43_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109769","volume":"141","author":"X Luo","year":"2025","unstructured":"Luo, X., Zhang, M., Han, Y., Yan, R., Wang, S.: Ship fuel consumption prediction based on transfer learning: models and applications. Eng. Appl. Artif. Intell. 141, 109769 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"43_CR16","doi-asserted-by":"crossref","unstructured":"Man, Y., Sturm, T., Lundh, M., MacKinnon, S.N.: From ethnographic research to big data analytics\u2014a case of maritime energy-efficiency optimization. Appl. Sci. 10(6) (2020), https:\/\/www.mdpi.com\/2076-3417\/10\/6\/2134","DOI":"10.3390\/app10062134"},{"key":"43_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2025.120540","volume":"323","author":"S Mavroudis","year":"2025","unstructured":"Mavroudis, S., Tinga, T.: Application of transfer learning on physics-based models to enhance vessel shaft power predictions. Ocean Eng. 323, 120540 (2025)","journal-title":"Ocean Eng."},{"key":"43_CR18","unstructured":"Organization, I.M.: IMO\u2019s work to cut GHG emissions from ships \u2014 imo.org. https:\/\/www.imo.org\/en\/MediaCentre\/HotTopics\/Pages\/Cutting-GHG-emissions.aspx, Accessed 14 June 2025"},{"issue":"4","key":"43_CR19","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1007\/s00773-017-0511-5","volume":"23","author":"H Orihara","year":"2017","unstructured":"Orihara, H., Tsujimoto, M.: Performance prediction of full-scale ship and analysis by means of on-board monitoring. part\u00a02: validation of full-scale performance predictions in actual seas. J. Mar. Sci. Technol. 23(4), 782\u2013801 (2017). https:\/\/doi.org\/10.1007\/s00773-017-0511-5","journal-title":"J. Mar. Sci. Technol."},{"key":"43_CR20","doi-asserted-by":"crossref","unstructured":"Parkes, A.I., Savasta, T.D., Sobey, A.J., Hudson, D.A.: Efficient Vessel Power Prediction in Operational Conditions Using Machine Learning, pp. 350\u2013367. Springer Singapore (2020)","DOI":"10.1007\/978-981-15-4624-2_21"},{"key":"43_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115971","volume":"187","author":"A Parkes","year":"2022","unstructured":"Parkes, A., Savasta, T., Sobey, A., Hudson, D.: Power prediction for a vessel without recorded data using data fusion from a fleet of vessels. Expert Syst. Appl. 187, 115971 (2022)","journal-title":"Expert Syst. Appl."},{"key":"43_CR22","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.oceaneng.2018.07.060","volume":"166","author":"A Parkes","year":"2018","unstructured":"Parkes, A., Sobey, A., Hudson, D.: Physics-based shaft power prediction for large merchant ships using neural networks. Ocean Eng. 166, 92\u2013104 (2018)","journal-title":"Ocean Eng."},{"key":"43_CR23","unstructured":"Pedersen, B., Larsen, J.: Prediction of full-scale propulsion power using artificial neural networks. In: 8th International Conference on Computer and IT Applications in the Maritime Industries, pp. 537\u2013550. TuTech, Budapest (May 10-12 2009)"},{"issue":"1","key":"43_CR24","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s00773-011-0151-0","volume":"17","author":"JP Petersen","year":"2011","unstructured":"Petersen, J.P., Jacobsen, D.J., Winther, O.: Statistical modelling for ship propulsion efficiency. J. Mar. Sci. Technol. 17(1), 30\u201339 (2011)","journal-title":"J. Mar. Sci. Technol."},{"key":"43_CR25","unstructured":"P\u00e9tursson, S.: Predicting Optimal Trim Configuration of Marine Vessels with Respect to Fuel Usage. Master\u2019s thesis, University of Iceland (2009)"},{"issue":"1","key":"43_CR26","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/s00773-014-0273-2","volume":"20","author":"A Radonjic","year":"2014","unstructured":"Radonjic, A., Vukadinovic, K.: Application of ensemble neural networks to prediction of towboat shaft power. J. Mar. Sci. Technol. 20(1), 64\u201380 (2014)","journal-title":"J. Mar. Sci. Technol."},{"key":"43_CR27","unstructured":"Smith, T., Aldous, L., Bucknall, R.: Noon report data uncertainty (2013)"},{"key":"43_CR28","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.oceaneng.2018.07.061","volume":"166","author":"O Soner","year":"2018","unstructured":"Soner, O., Akyuz, E., Celik, M.: Use of tree-based methods in ship performance monitoring under operating conditions. Ocean Eng. 166, 302\u2013310 (2018)","journal-title":"Ocean Eng."},{"key":"43_CR29","doi-asserted-by":"crossref","unstructured":"Uyan\u0131k, T., Karatu\u011f, u., Arslano\u011flu, Y.: Machine learning approach to ship fuel consumption: a case of container vessel. Transp. Res. Part D Transp. Environ. 84, 102389 (2020)","DOI":"10.1016\/j.trd.2020.102389"},{"issue":"1","key":"43_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big Data 3(1), 1\u201340 (2016). https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J. Big Data"},{"issue":"1","key":"43_CR31","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3233\/ISP-220009","volume":"70","author":"RH Zwart","year":"2023","unstructured":"Zwart, R.H., Bogaard, J., Kana, A.A.: A grey-box model approach using noon report data for trim optimization. Int. Shipbuild. Prog. 70(1), 41\u201363 (2023)","journal-title":"Int. Shipbuild. Prog."}],"container-title":["Communications in Computer and Information Science","Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15638-9_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T12:25:05Z","timestamp":1775132705000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15638-9_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032156372","9783032156389"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15638-9_43","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":"11 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IJCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Computational Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marbella","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcci2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ijcci.scitevents.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}