A Review of Artificial Neural Networks Applications in Maritime Industry
Artificial neural networks (ANN) are a data driven tool that has been used for modeling, prediction, optimization, classification, diagnostics, decision-making, etc., in various systems where measurements are available to produce significant amount of data. Ship processes are constantly monitored in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10353919/ |
| _version_ | 1797376351863308288 |
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| author | Nur Assani Petar Matic Nediljko Kastelan Ivan R. Cavka |
| author_facet | Nur Assani Petar Matic Nediljko Kastelan Ivan R. Cavka |
| author_sort | Nur Assani |
| collection | DOAJ |
| description | Artificial neural networks (ANN) are a data driven tool that has been used for modeling, prediction, optimization, classification, diagnostics, decision-making, etc., in various systems where measurements are available to produce significant amount of data. Ship processes are constantly monitored in order to control the operation of the ship and to ensure efficient and safe environment, generating large amount of data. Those data are increasingly being exploited by ANNs and the number of applications is growing. The aim of this paper is to analyze the applications of ANNs in maritime industry, and especially on ships. Based on the review analysis of the sixty-nine papers found published on this topic over the last 10 years in relevant databases, applications have been classified into eight categories in this paper. ANN types, training algorithms, activation functions, as well as measures used to evaluate the performance of the ANN models, have been analyzed for each application category. ANNs rely on data, therefore data acquisition, data processing, organization of the data for training ANN models, their validation and testing have also been addressed in this paper. The conclusions from the review analysis presented should be useful for future work in the area of ANN applications on ships and in maritime industry. |
| first_indexed | 2024-03-08T19:37:18Z |
| format | Article |
| id | doaj.art-655288e2bce24dd283e4dde0c725600a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:37:18Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-655288e2bce24dd283e4dde0c725600a2023-12-26T00:03:32ZengIEEEIEEE Access2169-35362023-01-011113982313984810.1109/ACCESS.2023.334169010353919A Review of Artificial Neural Networks Applications in Maritime IndustryNur Assani0https://orcid.org/0000-0003-0427-5186Petar Matic1https://orcid.org/0000-0002-1799-5257Nediljko Kastelan2Ivan R. Cavka3Faculty of Maritime Studies, University of Split, Split, CroatiaFaculty of Maritime Studies, University of Split, Split, CroatiaFaculty of Maritime Studies, University of Split, Split, CroatiaFaculty of Maritime Studies, University of Split, Split, CroatiaArtificial neural networks (ANN) are a data driven tool that has been used for modeling, prediction, optimization, classification, diagnostics, decision-making, etc., in various systems where measurements are available to produce significant amount of data. Ship processes are constantly monitored in order to control the operation of the ship and to ensure efficient and safe environment, generating large amount of data. Those data are increasingly being exploited by ANNs and the number of applications is growing. The aim of this paper is to analyze the applications of ANNs in maritime industry, and especially on ships. Based on the review analysis of the sixty-nine papers found published on this topic over the last 10 years in relevant databases, applications have been classified into eight categories in this paper. ANN types, training algorithms, activation functions, as well as measures used to evaluate the performance of the ANN models, have been analyzed for each application category. ANNs rely on data, therefore data acquisition, data processing, organization of the data for training ANN models, their validation and testing have also been addressed in this paper. The conclusions from the review analysis presented should be useful for future work in the area of ANN applications on ships and in maritime industry.https://ieeexplore.ieee.org/document/10353919/Applications of artificial neural networks (ANN)ANN typesactivation functionsevaluation measuresmaritime industryreview analysis |
| spellingShingle | Nur Assani Petar Matic Nediljko Kastelan Ivan R. Cavka A Review of Artificial Neural Networks Applications in Maritime Industry IEEE Access Applications of artificial neural networks (ANN) ANN types activation functions evaluation measures maritime industry review analysis |
| title | A Review of Artificial Neural Networks Applications in Maritime Industry |
| title_full | A Review of Artificial Neural Networks Applications in Maritime Industry |
| title_fullStr | A Review of Artificial Neural Networks Applications in Maritime Industry |
| title_full_unstemmed | A Review of Artificial Neural Networks Applications in Maritime Industry |
| title_short | A Review of Artificial Neural Networks Applications in Maritime Industry |
| title_sort | review of artificial neural networks applications in maritime industry |
| topic | Applications of artificial neural networks (ANN) ANN types activation functions evaluation measures maritime industry review analysis |
| url | https://ieeexplore.ieee.org/document/10353919/ |
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