{"type":"ScholarlyArticle","title":"Logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification (Extended Abstract)","authors":[{"name":"Manolis Pitsikalis","slug":"manolis-pitsikalis","given":"Manolis","family":"Pitsikalis","affiliation":"Department of Computer Science , University of Liverpool"},{"name":"Thanh-Toan Do","slug":"thanh-toan-do","given":"Thanh-Toan","family":"Do","affiliation":"Department of Data Science and AI , Monash University"},{"name":"Alexei Lisitsa","slug":"alexei-lisitsa","given":"Alexei","family":"Lisitsa","affiliation":"Department of Computer Science , University of Liverpool"},{"name":"Shan Luo","slug":"shan-luo","given":"Shan","family":"Luo","affiliation":"Department of Computer Science , University of Liverpool; Department of Engineering , King's College London"}],"abstract":"The shipping industry is an important component of the global trade and economy. In order to ensure law compliance and safety, it needs to be monitored. In this paper, we present a novel ship type classification model that combines vessel transmitted data from the Automatic Identification System, with vessel imagery. The main components of our approach are the Faster R-CNN Deep Neural Network and a Neuro-Fuzzy system with IF-THEN rules. We evaluate our model using real world data and showcase the advantages of this combination while also compare it with other methods. Results show that our model can increase prediction scores by up to 15.4% when compared with the next best model we considered, while also maintaining a level of explainability as opposed to common black box approaches.","doi":"10.24963/ijcai.2022/744","url":"https://ijcai.org/proceedings/2022/744","pdf":"https://ijcai.org/proceedings/2022/0744.pdf","markdown":"https://ijcai.org/proceedings/2022/744.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/744","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":744,"pages":{"first":5324,"last":5328},"keywords":["Artificial Intelligence: General"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987269","length_sec":86,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985412","length_sec":675,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Breiman et al., 1984] Leo Breiman, Jerome Friedman, R.A. 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