Description

• Title:
  The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
• Creator:
  Di Ciaccio, Fabiana
• Description:
  The widespread interest towards a sustainable and effective monitoring of the environment is increasingly demanding the development of modern and more affordable technologies to support or even replace the traditional time-consuming, high-cost sampling surveys at a multi-scale level. Researchers are highly benefitting from the recent enormous progresses achieved in the Artificial Intelligence (AI) field, with Machine/Deep Learning (ML/DL) applications increasing at sight. This gives a remarkable contribution to the environmental monitoring at sea, further allowing to develop efficient, smart and low-cost solutions to support the wide variety of tasks dealing with this objective. This study explores the global scientific literature on AI and ML/DL applications for the environmental monitoring over the last years. The VOSviewer software has been used to create maps based on the bibliographic network data: this allowed to display the relationships among scientific journals, researchers, and countries and to analyze the co-occurrence of different terms connected to the research. The resulting bibliometric analysis aims at verifying the major research interests and at providing the community with interesting findings and new perspectives on this very important topic, highlighting the great potential and flexibility of these methodologies and the excellent achievements they obtained in the last years.
• Object availability:
  free access
• Rights:
  Prawa autorskie (c) 2024 Fabiana Di Ciaccio;
  https://creativecommons.org/licenses/by-nd/4.0
• Date:
  2024.03.21
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Language:
  English
• Source:
  Ecological Questions; Vol. 35 No. 1 (2024): Special Issue “Exploring the scientific literature on controversial and timely ecological questions”; 1-30;
  Ecological Questions; Tom 35 Nr 1 (2024): Special Issue “Exploring the scientific literature on controversial and timely ecological questions”; 1-30;
  1644-7298
• Format:
  application/pdf
• Publisher:
  Uniwersytet Mikołaja Kopernika w Toruniu
• Relation: https://apcz.umk.pl/EQ/article/view/49576/38093
• Subject:
  Artificial Intelligence;
  Bibliometric analysis;
  Deep Learning;
  Environmental monitoring;
  Machine Learning;
  Marine environment;
  Social network analysis;
  VOSviewer
• Identifier: https://apcz.umk.pl/EQ/article/view/49576
• Data provider:
  Akademicka Platforma Czasopism
• Can I use it?:
  seek permission
• Type:
  text