{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:55:52Z","timestamp":1772909752713,"version":"3.50.1"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031434297","type":"print"},{"value":"9783031434303","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43430-3_14","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T06:02:16Z","timestamp":1694844136000},"page":"226-241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Data-Driven Explainable Artificial Intelligence for\u00a0Energy Efficiency in\u00a0Short-Sea Shipping"],"prefix":"10.1007","author":[{"given":"Mohamed","family":"Abuella","sequence":"first","affiliation":[]},{"given":"M. Amine","family":"Atoui","sequence":"additional","affiliation":[]},{"given":"Slawomir","family":"Nowaczyk","sequence":"additional","affiliation":[]},{"given":"Simon","family":"Johansson","sequence":"additional","affiliation":[]},{"given":"Ethan","family":"Faghani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"14_CR1","unstructured":"Copernicus Marine Service. https:\/\/marine.copernicus.eu"},{"key":"14_CR2","unstructured":"Engine Volvo Penta tier13 A2022 8398. https:\/\/www.volvopenta.com\/about-us\/news-page\/2022\/jun\/imo-tier-iii-range-expands-with-new-d13-solutions\/"},{"key":"14_CR3","unstructured":"Shap package. https:\/\/github.com\/slundberg\/shap"},{"key":"14_CR4","unstructured":"StormGlass API. https:\/\/stormglass.io"},{"key":"14_CR5","unstructured":"Marine Traffic (2022). https:\/\/www.marinetraffic.com\/en\/ais\/details\/ships\/shipid:1088282\/mmsi:265513810\/imo:8602713\/vessel:BURO"},{"key":"14_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2021.128871","volume":"320","author":"JD Ampah","year":"2021","unstructured":"Ampah, J.D., Yusuf, A.A., Afrane, S., Jin, C., Liu, H.: Reviewing two decades of cleaner alternative marine fuels: towards IMO\u2019s decarbonization of the maritime transport sector. J. Clean. Prod. 320, 128871 (2021)","journal-title":"J. Clean. Prod."},{"key":"14_CR7","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"key":"14_CR8","unstructured":"Bank, W.: Accelerating Digitalization: Critical Actions to Strengthen the Resilience of the Maritime Supply Chain. World Bank, Washington (2020)"},{"key":"14_CR9","unstructured":"Bellingmo, P.R., Pobitzer, A., J\u00f8rgensen, U., Berge, S.P.: Energy efficient and safe ship routing using machine learning techniques on operational and weather data. In: 20th International Conference on Computer Applications and Information Technology in the Maritime Industries (2021)"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Donner, P., Johansson, T.: Sulphur directive, short sea shipping and corporate social responsibility in a EU context. In: Corporate Social Responsibility in the Maritime Industry, pp. 149\u2013166 (2018)","DOI":"10.1007\/978-3-319-69143-5_9"},{"key":"14_CR11","unstructured":"Eurostat: Short sea shipping - country level - gross weight of goods transported to\/from main ports (2023). https:\/\/ec.europa.eu\/eurostat\/databrowser\/view\/mar_sg_am_cw\/default\/table?lang=en"},{"key":"14_CR12","unstructured":"Haranen, M., My\u00f6h\u00e4nen, S., Cristea, D.S.: The role of accurate now-cast data in shi p efficiency analysis. In: 2nd Hull Performance & Insight Conference, pp. 25\u201338 (2017)"},{"key":"14_CR13","unstructured":"Fourth IMO GHG study 2020 (2020)"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"J\u00f8rgensen, U., Belingmo, P.R., Murray, B., Berge, S.P., Pobitzer, A.: Ship route optimization using hybrid physics-guided machine learning. In: Journal of Physics: Conference Series, vol. 2311, p. 012037. IOP Publishing (2022)","DOI":"10.1088\/1742-6596\/2311\/1\/012037"},{"issue":"15","key":"14_CR15","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.3390\/s21155200","volume":"21","author":"D Kim","year":"2021","unstructured":"Kim, D., Antariksa, G., Handayani, M.P., Lee, S., Lee, J.: Explainable anomaly detection framework for maritime main engine sensor data. Sensors 21(15), 5200 (2021)","journal-title":"Sensors"},{"key":"14_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.110387","volume":"245","author":"X Lang","year":"2022","unstructured":"Lang, X., Wu, D., Mao, W.: Comparison of supervised machine learning methods to predict ship propulsion power at sea. Ocean Eng. 245, 110387 (2022)","journal-title":"Ocean Eng."},{"key":"14_CR17","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"3","key":"14_CR18","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1080\/03088831003700678","volume":"37","author":"F Medda","year":"2010","unstructured":"Medda, F., Trujillo, L.: Short-sea shipping: an analysis of its determinants. Maritime Policy Manag. 37(3), 285\u2013303 (2010)","journal-title":"Maritime Policy Manag."},{"issue":"2","key":"14_CR19","first-page":"158","volume":"68","author":"RW Sinnott","year":"1984","unstructured":"Sinnott, R.W.: Virtues of the haversine. Sky Telescope 68(2), 158 (1984)","journal-title":"Sky Telescope"},{"key":"14_CR20","unstructured":"Sugimoto, K.: Digital twin for monitoring remaining fatigue life of critical hull structures"},{"key":"14_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2021.101539","volume":"58","author":"M Veerappa","year":"2022","unstructured":"Veerappa, M., Anneken, M., Burkart, N., Huber, M.F.: Validation of XAI explanations for multivariate time series classification in the maritime domain. J. Comput. Sci. 58, 101539 (2022)","journal-title":"J. Comput. Sci."},{"key":"14_CR22","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-030-89988-2_7","volume-title":"Design in Maritime Engineering","author":"A Zakaria","year":"2022","unstructured":"Zakaria, A., Md Arof, A., Khabir, A.: Instruments utilized in short sea shipping research: a review. In: Ismail, A., Dahalan, W.M., \u00d6chsner, A. (eds.) Design in Maritime Engineering, pp. 83\u2013108. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-89988-2_7"},{"key":"14_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2020.107697","volume":"213","author":"TP Zis","year":"2020","unstructured":"Zis, T.P., Psaraftis, H.N., Ding, L.: Ship weather routing: a taxonomy and survey. Ocean Eng. 213, 107697 (2020)","journal-title":"Ocean Eng."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43430-3_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T21:32:19Z","timestamp":1701034339000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43430-3_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434297","9783031434303"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43430-3_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}