A bibliometric analysis on the relevancies of artificial neural networks (ANN) techniques in offshore engineering
AbstractRecently, the use of artificial neural networks (ANN) in the offshore exploration and production industry to optimize decision-making and reduce costs and non-productive time have been increasing. Despite this trend, there have been only 11 reviews of ANN in offshore engineering published on...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2023.2241729 |
| _version_ | 1827344481637105664 |
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| author | Muhammad Daniel Abdul Shahid Mohd Hisbany Mohd Hashim Najwa Mohd Fadzil Muhd Hariz Ahmad Rushdi Amin Al-Fakih Mohd Fakri Muda |
| author_facet | Muhammad Daniel Abdul Shahid Mohd Hisbany Mohd Hashim Najwa Mohd Fadzil Muhd Hariz Ahmad Rushdi Amin Al-Fakih Mohd Fakri Muda |
| author_sort | Muhammad Daniel Abdul Shahid |
| collection | DOAJ |
| description | AbstractRecently, the use of artificial neural networks (ANN) in the offshore exploration and production industry to optimize decision-making and reduce costs and non-productive time have been increasing. Despite this trend, there have been only 11 reviews of ANN in offshore engineering published on the Scopus database. Therefore, this article aims to provide an update on the relevance of ANN in offshore engineering over the past 18 years (2005–2023) through a bibliometric analysis using Excel and VOS Viewer software. This analysis highlights the yearly increase in publications related to ANN implementations in offshore engineering and identifies the most cited publications, citation network analysis, authors, keywords, journals, institutions, and leading countries. The objective of this bibliometric analysis is to assist subsequent research and collaboration in this field by shedding light on ANN’s potential and identifying areas for further application. The identified cluster area publications encompass a range of topics, including drilling systems and the assessment of pipes. Furthermore, the significant fourfold increase in publications since 2005 indicates a growing interest among researchers in adapting ANN for various applications within this field. This could lead to further advancements, innovations, and improved solutions to promote collaboration and knowledge-sharing among researchers in this domain. |
| first_indexed | 2024-03-07T22:47:56Z |
| format | Article |
| id | doaj.art-c74849c89e2d4318a151dbfa1b1566ac |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-07T22:47:56Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-c74849c89e2d4318a151dbfa1b1566ac2024-02-23T15:01:39ZengTaylor & Francis GroupCogent Engineering2331-19162023-12-0110110.1080/23311916.2023.2241729A bibliometric analysis on the relevancies of artificial neural networks (ANN) techniques in offshore engineeringMuhammad Daniel Abdul Shahid0Mohd Hisbany Mohd Hashim1Najwa Mohd Fadzil2Muhd Hariz Ahmad Rushdi3Amin Al-Fakih4Mohd Fakri Muda5School of Civil Engineering, College of Engineering, Universiti Teknologi MARA Shah Alam, Selangor, MalaysiaSchool of Civil Engineering, College of Engineering, Universiti Teknologi MARA Shah Alam, Selangor, MalaysiaSchool of Civil Engineering, College of Engineering, Universiti Teknologi MARA Shah Alam, Selangor, MalaysiaDepartment of Maritime Technology, Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu Kuala Nerus, Terengganu, MalaysiaInterdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaCivil Engineering Studies, College of Engineering, Universiti Teknologi MARA, Pahang, MalaysiaAbstractRecently, the use of artificial neural networks (ANN) in the offshore exploration and production industry to optimize decision-making and reduce costs and non-productive time have been increasing. Despite this trend, there have been only 11 reviews of ANN in offshore engineering published on the Scopus database. Therefore, this article aims to provide an update on the relevance of ANN in offshore engineering over the past 18 years (2005–2023) through a bibliometric analysis using Excel and VOS Viewer software. This analysis highlights the yearly increase in publications related to ANN implementations in offshore engineering and identifies the most cited publications, citation network analysis, authors, keywords, journals, institutions, and leading countries. The objective of this bibliometric analysis is to assist subsequent research and collaboration in this field by shedding light on ANN’s potential and identifying areas for further application. The identified cluster area publications encompass a range of topics, including drilling systems and the assessment of pipes. Furthermore, the significant fourfold increase in publications since 2005 indicates a growing interest among researchers in adapting ANN for various applications within this field. This could lead to further advancements, innovations, and improved solutions to promote collaboration and knowledge-sharing among researchers in this domain.https://www.tandfonline.com/doi/10.1080/23311916.2023.2241729artificial neural networkoffshorebibliometricVOS Viewer |
| spellingShingle | Muhammad Daniel Abdul Shahid Mohd Hisbany Mohd Hashim Najwa Mohd Fadzil Muhd Hariz Ahmad Rushdi Amin Al-Fakih Mohd Fakri Muda A bibliometric analysis on the relevancies of artificial neural networks (ANN) techniques in offshore engineering Cogent Engineering artificial neural network offshore bibliometric VOS Viewer |
| title | A bibliometric analysis on the relevancies of artificial neural networks (ANN) techniques in offshore engineering |
| title_full | A bibliometric analysis on the relevancies of artificial neural networks (ANN) techniques in offshore engineering |
| title_fullStr | A bibliometric analysis on the relevancies of artificial neural networks (ANN) techniques in offshore engineering |
| title_full_unstemmed | A bibliometric analysis on the relevancies of artificial neural networks (ANN) techniques in offshore engineering |
| title_short | A bibliometric analysis on the relevancies of artificial neural networks (ANN) techniques in offshore engineering |
| title_sort | bibliometric analysis on the relevancies of artificial neural networks ann techniques in offshore engineering |
| topic | artificial neural network offshore bibliometric VOS Viewer |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2023.2241729 |
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