{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:57:13Z","timestamp":1772823433150,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030911669","type":"print"},{"value":"9783030911676","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-91167-6_14","type":"book-chapter","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T14:13:49Z","timestamp":1638454429000},"page":"203-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2959-2022","authenticated-orcid":false,"given":"Manolis","family":"Pitsikalis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6249-0848","authenticated-orcid":false,"given":"Thanh-Toan","family":"Do","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3820-643X","authenticated-orcid":false,"given":"Alexei","family":"Lisitsa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4760-0372","authenticated-orcid":false,"given":"Shan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"14_CR1","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J., Olshen, R., Stone, C.J.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)"},{"key":"14_CR2","unstructured":"Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A., Bengio, Y.: A recurrent latent variable model for sequential data. In: NIPS, pp. 2980\u20132988. MIT Press, Cambridge (2015)"},{"key":"14_CR3","doi-asserted-by":"publisher","unstructured":"Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: ICML, pp. 160\u2013167. Association for Computing Machinery, New York (2008). https:\/\/doi.org\/10.1145\/1390156.1390177","DOI":"10.1145\/1390156.1390177"},{"key":"14_CR4","doi-asserted-by":"publisher","unstructured":"Dujmovi\u0107, J.J., Larsen, H.L.: Generalized conjunction\/disjunction. Int. J. Approx. Reason. 423\u2013446 (2007). https:\/\/doi.org\/10.1016\/j.ijar.2006.12.011","DOI":"10.1016\/j.ijar.2006.12.011"},{"key":"14_CR5","series-title":"Intelligent Systems Reference Library","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-642-21004-4_7","volume-title":"Intelligent Systems","author":"C Grosan","year":"2011","unstructured":"Grosan, C., Abraham, A.: Rule-based expert systems. In: Grosan, C., Abraham, A. (eds.) Intelligent Systems. ISRL, vol. 17, pp. 149\u2013189. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21004-4_7"},{"key":"14_CR6","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1109\/TKDE.2008.181","volume":"21","author":"S Hashemi","year":"2009","unstructured":"Hashemi, S., Yang, Y., Mirzamomen, Z., Kangavari, M.: Adapted one-versus-all decision trees for data stream classification. IEEE Trans. Knowl. Data Eng. 21, 624\u2013637 (2009). https:\/\/doi.org\/10.1109\/TKDE.2008.181","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"14_CR7","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR8","doi-asserted-by":"publisher","unstructured":"Hu, Z., Ma, X., Liu, Z., Hovy, E., Xing, E.: Harnessing deep neural networks with logic rules. In: ACL, Berlin, Germany, pp. 2410\u20132420. Association for Computational Linguistics (2016). https:\/\/doi.org\/10.18653\/v1\/P16-1228","DOI":"10.18653\/v1\/P16-1228"},{"key":"14_CR9","doi-asserted-by":"publisher","unstructured":"Kanjir, U., Greidanus, H., O\u0161tir, K.: Vessel detection and classification from spaceborne optical images: a literature survey. Remote Sens. Environ. 1\u201326 (2018). https:\/\/doi.org\/10.1016\/j.rse.2017.12.033","DOI":"10.1016\/j.rse.2017.12.033"},{"key":"14_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)"},{"issue":"1","key":"14_CR11","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1002\/hast.973","volume":"49","author":"AJ London","year":"2019","unstructured":"London, A.J.: Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent. Rep. 49(1), 15\u201321 (2019). https:\/\/doi.org\/10.1002\/hast.973","journal-title":"Hastings Cent. Rep."},{"key":"14_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/978-3-030-46147-8_31","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"G Marra","year":"2020","unstructured":"Marra, G., Giannini, F., Diligenti, M., Gori, M.: Integrating learning and reasoning with deep logic models. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11907, pp. 517\u2013532. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46147-8_31"},{"key":"14_CR13","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111\u20133119. Curran Associates Inc., Red Hook (2013)"},{"key":"14_CR14","doi-asserted-by":"publisher","unstructured":"Nguyen, D., Vadaine, R., Hajduch, G., Garello, R., Fablet, R.: A multi-task deep learning architecture for maritime surveillance using AIS data streams. In: IEEE DSAA, pp. 331\u2013340 (2018). https:\/\/doi.org\/10.1109\/DSAA.2018.00044","DOI":"10.1109\/DSAA.2018.00044"},{"key":"14_CR15","doi-asserted-by":"publisher","unstructured":"Padilla, R., Netto, S.L., da Silva, E.A.B.: A survey on performance metrics for object-detection algorithms, pp. 237\u2013242 (2020). https:\/\/doi.org\/10.1109\/IWSSIP48289.2020.9145130","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"14_CR16","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch (2017)"},{"key":"14_CR17","doi-asserted-by":"publisher","unstructured":"Ray, C., Dr\u00e9o, R., Camossi, E., Jousselme, A.L., Iphar, C.: Heterogeneous integrated dataset for maritime intelligence, surveillance, and reconnaissance. Data in Brief, p. 104141 (2019). https:\/\/doi.org\/10.1016\/j.dib.2019.104141","DOI":"10.1016\/j.dib.2019.104141"},{"key":"14_CR18","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"14_CR19","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks, vol. 28 (2015)"},{"issue":"4","key":"14_CR20","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1109\/TITB.2007.907985","volume":"12","author":"M Tsipouras","year":"2008","unstructured":"Tsipouras, M., et al.: Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. IEEE Trans. Inf. Technol. Biomed. 12(4), 447\u2013458 (2008). https:\/\/doi.org\/10.1109\/TITB.2007.907985","journal-title":"IEEE Trans. Inf. Technol. Biomed."}],"container-title":["Lecture Notes in Computer Science","Rules and Reasoning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91167-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T14:29:45Z","timestamp":1638455385000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91167-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030911669","9783030911676"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91167-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RuleML+RR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Rules and Reasoning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leuven","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"rulemlrr2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/declarativeai2021.net\/ruleml-rr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","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":"17","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":"2","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":"44% - 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":"2.9","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":"3","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":"5 reviews were done by sub-reviewers, who were invited by individual PC members.","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)"}}]}}