{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T04:37:14Z","timestamp":1779338234833,"version":"3.51.4"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"European Union through the Horizon 2020 Research and Innovation Program, in the Context of the VesselAI Project","doi-asserted-by":"publisher","award":["GA 957237"],"award-info":[{"award-number":["GA 957237"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3335905","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T14:52:50Z","timestamp":1700578370000},"page":"132576-132589","source":"Crossref","is-referenced-by-count":19,"title":["A Multitask Learning Framework for Predicting Ship Fuel Oil Consumption"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4483-4264","authenticated-orcid":false,"given":"Loukas","family":"Ilias","sequence":"first","affiliation":[{"name":"Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panagiotis","family":"Kapsalis","sequence":"additional","affiliation":[{"name":"Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9616-447X","authenticated-orcid":false,"given":"Spiros","family":"Mouzakitis","sequence":"additional","affiliation":[{"name":"Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2618-5715","authenticated-orcid":false,"given":"Dimitris","family":"Askounis","sequence":"additional","affiliation":[{"name":"Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.trc.2012.09.012","article-title":"Speed models for energy-efficient maritime transportation: A taxonomy and survey","volume":"26","author":"Psaraftis","year":"2013","journal-title":"Transp. Res. C, Emerg. Technol."},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1016\/j.marpol.2020.103818","article-title":"Maritime transport in the French economy and its impact on air pollution: An input\u2013output analysis","volume":"116","author":"Bagoulla","year":"2020","journal-title":"Mar. Policy"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.21278\/brod72102"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2023.114143","article-title":"Benefits of slow steaming in realistic sailing conditions along different sailing routes","volume":"275","author":"Farkas","year":"2023","journal-title":"Ocean Eng."},{"key":"ref5","first-page":"8131","article-title":"The impact of slow steaming on reducing CO2 emissions in the Mediterranean Sea","volume-title":"Energy Rep.","volume":"7","author":"Degiuli","year":"2021"},{"key":"ref6","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2022.133915","article-title":"Is slow steaming a viable option to meet the novel energy efficiency requirements for containerships?","volume":"374","author":"Farkas","year":"2022","journal-title":"J. Cleaner Prod."},{"key":"ref7","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.jclepro.2019.03.030","article-title":"Load optimization of central cooling system pumps of a container ship for the slow steaming conditions to enhance the energy efficiency","volume":"222","author":"Dere","year":"2019","journal-title":"J. Cleaner Prod."},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2023.113991","article-title":"The effect of loading conditions and ship speed on the wind and air resistance of a containership","volume":"273","author":"Grlj","year":"2023","journal-title":"Ocean Eng."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.21278\/brod73205"},{"issue":"8","key":"ref10","doi-asserted-by":"crossref","first-page":"588","DOI":"10.3390\/jmse8080588","article-title":"Fuel consumption and emissions of ocean-going cargo ship with hybrid propulsion and different fuels over voyage","volume":"8","author":"Sui","year":"2020","journal-title":"J. Mar. Sci. Eng."},{"key":"ref11","first-page":"344","article-title":"Chapter 6\u2014Marine engines and auxiliary machinery","author":"Molland","year":"2008","journal-title":"The Maritime Engineering Reference Book"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3940\/rina.pass.2013.01"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s12206-018-1126-4"},{"key":"ref14","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2021.108616","article-title":"Data-driven modelling of ship propulsion and the effect of data pre-processing on the prediction of ship fuel consumption and speed loss","volume":"222","author":"Karagiannidis","year":"2021","journal-title":"Ocean Eng."},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-15919-0_31"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3172479"},{"key":"ref17","first-page":"4270","article-title":"Joint modelling of emotion and abusive language detection","volume-title":"Proc. 58th Annu. Meeting Assoc. Comput. Linguistics","author":"Rajamanickam"},{"key":"ref18","first-page":"2264","article-title":"Modeling the severity of complaints in social media","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics, Human Lang. Technol.","author":"Jin"},{"key":"ref19","first-page":"951","article-title":"Exploiting unrelated tasks in multi-task learning","volume-title":"Proc. 15th Int. Conf. Artif. Intell. Statist.","volume":"22","author":"Paredes"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2019.106282","article-title":"Machine learning models for predicting ship main engine fuel oil consumption: A comparative study","volume":"188","author":"Gkerekos","year":"2019","journal-title":"Ocean Eng."},{"key":"ref21","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.cor.2015.04.004","article-title":"An artificial neural network based decision support system for energy efficient ship operations","volume":"66","author":"Be\u015fik\u00e7i","year":"2016","journal-title":"Comput. Oper. Res."},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1179\/str.2012.59.1.007"},{"issue":"5","key":"ref23","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.3390\/en11051059","article-title":"Model for estimation of fuel consumption of cruise ships","volume":"11","author":"Simonsen","year":"2018","journal-title":"Energies"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.20967\/jcscm.2018.02.002"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/qre.2171"},{"key":"ref26","doi-asserted-by":"crossref","DOI":"10.1016\/j.trd.2020.102389","article-title":"Machine learning approach to ship fuel consumption: A case of container vessel","volume":"84","author":"Uyan\u0131k","year":"2020","journal-title":"Transp. Res. D, Transp. Environ."},{"issue":"5","key":"ref27","doi-asserted-by":"crossref","first-page":"776","DOI":"10.3390\/electronics9050776","article-title":"Forecasting the fuel consumption of passenger ships with a combination of shallow and deep learning","volume":"9","author":"Panapakidis","year":"2020","journal-title":"Electronics"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2933630"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.trd.2017.09.014","article-title":"Predicting ship fuel consumption based on LASSO regression","volume":"65","author":"Wang","year":"2018","journal-title":"Transp. Res. D, Transp. Environ."},{"issue":"3","key":"ref30","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.joes.2021.08.007","article-title":"An adaptive hyper parameter tuning model for ship fuel consumption prediction under complex maritime environments","volume":"7","author":"Zhou","year":"2022","journal-title":"J. Ocean Eng. Sci."},{"key":"ref31","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2020.121564","article-title":"Machine learning method for energy consumption prediction of ships in port considering green ports","volume":"264","author":"Peng","year":"2020","journal-title":"J. Cleaner Prod."},{"key":"ref32","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2020.108530","article-title":"Prediction and optimisation of fuel consumption for inland ships considering real-time status and environmental factors","volume":"221","author":"Yuan","year":"2021","journal-title":"Ocean Eng."},{"issue":"2","key":"ref33","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3390\/jmse9020137","article-title":"Development of a fuel consumption prediction model based on machine learning using ship in-service data","volume":"9","author":"Kim","year":"2021","journal-title":"J. Mar. Sci. Eng."},{"key":"ref34","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2022.110904","article-title":"A two-step strategy for fuel consumption prediction and optimization of ocean-going ships","volume":"249","author":"Hu","year":"2022","journal-title":"Ocean Eng."},{"issue":"4","key":"ref35","doi-asserted-by":"crossref","first-page":"449","DOI":"10.3390\/jmse9040449","article-title":"A novel hybrid fuel consumption prediction model for ocean-going container ships based on sensor data","volume":"9","author":"Hu","year":"2021","journal-title":"J. Mar. Sci. Eng."},{"key":"ref36","article-title":"Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship","volume":"138","author":"Yan","year":"2020","journal-title":"Transp. Res. E, Logistics Transp. Rev."},{"key":"ref37","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2020.106972","article-title":"The development of a ship performance model in varying operating conditions based on ANN and regression techniques","volume":"198","author":"Farag","year":"2020","journal-title":"Ocean Eng."},{"key":"ref38","first-page":"88","article-title":"Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data","volume-title":"Transp. Res. B, Methodol.","volume":"122","author":"Du","year":"2019"},{"key":"ref39","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2019.05.053","article-title":"Data-driven ship energy efficiency analysis and optimization model for route planning in ice-covered Arctic waters","volume":"186","author":"Zhang","year":"2019","journal-title":"Ocean Eng."},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref41","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref42","article-title":"Sensor data from three different fishing ships for a period of one month","author":"Siltanen","year":"2019"},{"key":"ref43","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108980","article-title":"CAAN: Context-aware attention network for visual question answering","volume":"132","author":"Chen","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"ref45","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref46","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10325522.pdf?arnumber=10325522","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T05:59:42Z","timestamp":1769493582000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10325522\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3335905","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}