Scientometric insights into the trends and evolution of the underwater sensing technologies and their applications

From National Research Council Canada

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DOI	Resolve DOI: https://doi.org/10.5530/jscires.13.2.44
Author	Search for: Ebadi, Ashkan¹ORCID identifier: https://orcid.org/0000-0002-4542-9105; Search for: Auger, Alain; Search for: Gauthier, Yvan¹
Affiliation	National Research Council of Canada. Digital Technologies
Format	Text, Article
Subject	technology trends; research topic evolution; underwater sensing; machine learning; topic modeling.
Abstract	Underwater sensing has a wide range of applications including the location and monitoring of subsurface infrastructures such as cables and pipelines, mapping undersea terrain, the study of marine life, pollution and salinity monitoring and detection of seismic activities. Underwater sensing technologies are also essential to maritime surveillance, detection and tracking of submarines and other underwater objects. Using natural language processing and machine learning, we mined scientific publications to gain insight into underwater sensing research evolution during the 21st century, in terms of technology development and applications. This study offers a comprehensive analysis of the underwater sensing research landscape as well as the temporal evolution of its main research topics. We identified 18 key research topics that offer comprehensive and logical coverage of the research field. Nearly half of them are on a decreasing trend, despite an overall increase in the number of scientific publications. These findings and the extracted patterns can provide researchers and decision-makers with new insights into the field, its characteristics and its development. Our proposed algorithmic approach can also be applied to other areas for technologies of a disruptive nature.
Publication date	2024-08-19
Publisher	EManuscript Technologies Phcog.Net
Licence	Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/
In	Journal of Scientometric Research 13, no. 2 (19 August 2024): 562–574.
Language	English
Peer reviewed	Yes
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Record identifier	ca53a183-cd99-45be-a5f2-2f63cc963752
Record created	2024-08-27
Record modified	2024-08-27

Date modified:: 2024-10-09