The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation
In this paper, the authors draw attention to the significant impact of the human factor during collision situations in maritime navigation. The problems in the communication process between navigators are so excessive that the authors propose automatic communication. This is an alternative method to...
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2797 |
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| author | Leszek Misztal Paulina Hatlas-Sowinska |
| author_facet | Leszek Misztal Paulina Hatlas-Sowinska |
| author_sort | Leszek Misztal |
| collection | DOAJ |
| description | In this paper, the authors draw attention to the significant impact of the human factor during collision situations in maritime navigation. The problems in the communication process between navigators are so excessive that the authors propose automatic communication. This is an alternative method to the current one. The presented system comprehensively performs communication tasks during a sea voyage. To reach the mentioned goal, AI methods of natural language processing and additional properties of metaontology (ontology supplemented with objective functions) are applied. Dedicated to maritime transport applications, the model for translating a natural language into an ontology consists of multiple steps and uses AI methods of classification for the recognition of a message from the ship’s bridge. The reverse model is also multi-stage and uses a created rule-based knowledge base to create natural-language sentences built on the basis of the ontology. Validation of the model’s accuracy results was conducted through accuracy assessment coefficients for information classification, commonly used in science. Receiver operating characteristic (ROC) curves represent the results in the datasets. The presented solution of the designed architecture of the system as well as algorithms developed in the software prototype confirmed the correctness of the assumptions in the described study. The authors demonstrated that it is feasible to successfully apply metaontology and machine learning methods in the proposed prototype software for ship-to-ship communication. |
| format | Article |
| id | doaj-art-93c06547f2ca45339f1946a4ff369b8f |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-93c06547f2ca45339f1946a4ff369b8f2025-08-20T02:57:41ZengMDPI AGApplied Sciences2076-34172025-03-01155279710.3390/app15052797The Impact of the Human Factor on Communication During a Collision Situation in Maritime NavigationLeszek Misztal0Paulina Hatlas-Sowinska1Faculty of Computer Science and Telecommunication, Maritime University of Szczecin, 70-500 Szczecin, PolandDepartment of Mathematics, The Institute of Mathematics, Physics and Chemistry, Maritime University of Szczecin, 70-500 Szczecin, PolandIn this paper, the authors draw attention to the significant impact of the human factor during collision situations in maritime navigation. The problems in the communication process between navigators are so excessive that the authors propose automatic communication. This is an alternative method to the current one. The presented system comprehensively performs communication tasks during a sea voyage. To reach the mentioned goal, AI methods of natural language processing and additional properties of metaontology (ontology supplemented with objective functions) are applied. Dedicated to maritime transport applications, the model for translating a natural language into an ontology consists of multiple steps and uses AI methods of classification for the recognition of a message from the ship’s bridge. The reverse model is also multi-stage and uses a created rule-based knowledge base to create natural-language sentences built on the basis of the ontology. Validation of the model’s accuracy results was conducted through accuracy assessment coefficients for information classification, commonly used in science. Receiver operating characteristic (ROC) curves represent the results in the datasets. The presented solution of the designed architecture of the system as well as algorithms developed in the software prototype confirmed the correctness of the assumptions in the described study. The authors demonstrated that it is feasible to successfully apply metaontology and machine learning methods in the proposed prototype software for ship-to-ship communication.https://www.mdpi.com/2076-3417/15/5/2797safetymetaontologyautomation of communicationdata miningnatural language processinghuman factor |
| spellingShingle | Leszek Misztal Paulina Hatlas-Sowinska The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation Applied Sciences safety metaontology automation of communication data mining natural language processing human factor |
| title | The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation |
| title_full | The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation |
| title_fullStr | The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation |
| title_full_unstemmed | The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation |
| title_short | The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation |
| title_sort | impact of the human factor on communication during a collision situation in maritime navigation |
| topic | safety metaontology automation of communication data mining natural language processing human factor |
| url | https://www.mdpi.com/2076-3417/15/5/2797 |
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