Mapping the Frontier: A Bibliometric Analysis of Artificial Intelligence Applications in Local and Regional Studies DOI Creative Commons
Camelia Delcea, Ionuț Nica, Ștefan Ionescu

et al.

Algorithms, Journal Year: 2024, Volume and Issue: 17(9), P. 418 - 418

Published: Sept. 20, 2024

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The covers period year 2002 2023, utilizing data sourced from Web of Science database. Employing Bibliometrix package within RStudio VOSviewer software, identifies significant increase in AI-related publications, with an annual growth rate 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, Sustainability rank among top contributing sources. From perspective prominent affiliations, institutions like Duy Tan University, Ton Duc Thang Chinese Academy Sciences emerge leading contributors, Vietnam, Portugal, China being countries highest citation counts. Furthermore, word cloud is able highlight recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, “deep learning”. co-occurrence network reveals five clusters, amongst them neural network”, “regional development”, “climate change”, economy”, “management”, “technology”, “risk”, “fuzzy inference system”. Our findings support fact that AI increasingly employed address complex challenges, resource management urban planning. applications, machine learning algorithms networks, have become essential for optimizing processes decision-making at level. concludes while holds vast potential transforming research, ongoing international collaboration development adaptable models are maximizing benefits these technologies. Such efforts will ensure effective implementation diverse contexts, thereby supporting sustainable development.

Language: Английский

Navigating the Disinformation Maze: A Bibliometric Analysis of Scholarly Efforts DOI Creative Commons

George-Cristian Tătaru,

Adrian Domenteanu, Camelia Delcea

et al.

Information, Journal Year: 2024, Volume and Issue: 15(12), P. 742 - 742

Published: Nov. 21, 2024

The increasing prevalence of disinformation has become a global challenge, exacerbated by the rapid dissemination information in online environments. present study conducts bibliometric analysis scholarly efforts made over time research papers associated with field. Thus, this paper aims to understand and help combat focusing on methodologies, datasets, key metadata. Through approach, identifies leading authors, affiliations, journals examines collaboration networks field disinformation. This highlights significant growth disinformation, particularly response events such as 2016 U.S. election, Brexit, COVID-19 pandemic, an overall rate 15.14% entire analyzed period. results underscore role social media artificial intelligence spread well importance fact-checking technologies. Findings reveal that most prolific contributions come from universities United States America (USA), Kingdom (UK), Spain, other institutions, notable increase publications since 2018. thematic maps, keyword analysis, networks, provides comprehensive overview evolving research, offering valuable insights for future investigations policy development.

Language: Английский

Citations

3
Mapping the Frontier: A Bibliometric Analysis of Artificial Intelligence Applications in Local and Regional Studies DOI Creative Commons
Camelia Delcea, Ionuț Nica, Ștefan Ionescu

et al.

Algorithms, Journal Year: 2024, Volume and Issue: 17(9), P. 418 - 418

Published: Sept. 20, 2024

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The covers period year 2002 2023, utilizing data sourced from Web of Science database. Employing Bibliometrix package within RStudio VOSviewer software, identifies significant increase in AI-related publications, with an annual growth rate 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, Sustainability rank among top contributing sources. From perspective prominent affiliations, institutions like Duy Tan University, Ton Duc Thang Chinese Academy Sciences emerge leading contributors, Vietnam, Portugal, China being countries highest citation counts. Furthermore, word cloud is able highlight recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, “deep learning”. co-occurrence network reveals five clusters, amongst them neural network”, “regional development”, “climate change”, economy”, “management”, “technology”, “risk”, “fuzzy inference system”. Our findings support fact that AI increasingly employed address complex challenges, resource management urban planning. applications, machine learning algorithms networks, have become essential for optimizing processes decision-making at level. concludes while holds vast potential transforming research, ongoing international collaboration development adaptable models are maximizing benefits these technologies. Such efforts will ensure effective implementation diverse contexts, thereby supporting sustainable development.

Language: Английский

Citations

0