{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T21:19:01Z","timestamp":1779916741905,"version":"3.53.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Aiming at the current situation where the evolution path of ship fire accidents is fuzzy and the evolution process is difficult to predict, making it difficult for fire rescue commanders to make efficient decisions as soon as possible, based on the investigation of the evolution rules of ship fire accident scenarios and three scenario elements: scenario events (I), objective environment (E), and emergency measures (M), combined with dynamic Bayesian networks, a scenario deduction model for ship fire accidents is obtained. Taking the \u201cXie Chuan Lun\u201d fire accident as an example, the model was deduced through five steps: accident overview, scenario element determination, scenario network drawing, node variable probability determination, and probability calculation. The final result was obtained and analyzed, fully verifying the scientific and effective nature of the model.<\/jats:p>","DOI":"10.3233\/faia230833","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:54:14Z","timestamp":1701446054000},"source":"Crossref","is-referenced-by-count":1,"title":["Research on Ship Fire Scenario Inference Based on Dynamic Bayesian Network"],"prefix":"10.3233","author":[{"given":"Shuhao","family":"Xie","sequence":"first","affiliation":[{"name":"Navigation Institute, Dalian Maritime University, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Yin","sequence":"additional","affiliation":[{"name":"Navigation Institute, Dalian Maritime University, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230833","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:54:17Z","timestamp":1701446057000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230833"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230833","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}