Artificial Intelligent
Khairul Hafezad Abdullah; Davi Sofyan
Abstract
Safety and health are intricately interwoven and have become indispensable to the thriving business world and anthropology. It is concerned with ensuring employees’ physical, emotional, and mental well-being. Based on the Scopus and Web of Science databases, the current study intends to analyse ...
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Safety and health are intricately interwoven and have become indispensable to the thriving business world and anthropology. It is concerned with ensuring employees’ physical, emotional, and mental well-being. Based on the Scopus and Web of Science databases, the current study intends to analyse the global research output on machine learning in safety and health. This study utilized ScientoPy and VOSviewer to delve into the annual growth, patterns of research communication on source titles, international collaboration among countries, and authors’ keyword analysis. This study found that the Web of Science database tracks the evolution of publications throughout time. PLoS One has surpassed all other source titles in terms of publishing activity. Also, this study indicated that US researchers are constantly working on machine learning in safety and health research and have developed significant collaborations with China and Australia. Between 2020 and 2021, the University of Toronto published 86% of all papers, outpacing other institutions. The keywords “machine learning”, “artificial intelligence”, “electronic health records”, “deep learning”, and “mental health” were the most popular and trending keywords in 2020 and 2021, and “artificial intelligence” appeared in most publications among others. Future researchers should conduct scoping or systematic literature reviews to elucidate the relationships between these terms. This study may entice the curiosity of practitioners and researchers to advance new knowledge in this field by being devoted to cutting-edge research in the contemporary philosophy of science, cognitive, and cultural anthropology on machine learning in safety and health research. In conclusion, this scientometric analysis demonstrates that machine learning in safety and health is a study domain that requires further refinement in future research, as this technology has the potential to significantly improve workplace safety and health through targeted applications with clear benefits.
Mohammad Ebrahim Samie; Ali Biranvand; Sareh Rahmaniyan; Ebrahim Maleki Varnamkhasti
Volume 20, Issue 1 , January 2022
Abstract
This study analyzes the link between Mendeley indexes of scientific-citation networks and Scopus, taking into account the beneficial influence of researchers' actions in social networks on scientometric indices of works indexed in databases like Google scholar and WoS. In this basic/descriptive study, ...
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This study analyzes the link between Mendeley indexes of scientific-citation networks and Scopus, taking into account the beneficial influence of researchers' actions in social networks on scientometric indices of works indexed in databases like Google scholar and WoS. In this basic/descriptive study, we use the Altmetrics approach to describe Iranian researchers’ activities in industrial engineering in scientific-citation networks. In this study, researchers whose activities are recorded with Iranian affiliation in scientific-citation networks have been briefly named Iranian researchers. The corpus of the study included the works of 160 Iranian researchers in the field of industrial engineering, indexed in the Scopus in the period 2000-2019. To test the likely correlation between the measures of social networks (SN) activities with scientometric ones, simple and multiple correlation tests were carried out by Excel and SPSS software. The correlation between the number of times a document was read, the number of citations, and the measures in the Mendeley, Scopus, We of Science (WoS), and Google Scholar (GS) was very high. However, the correlation between the number of readers in the Mendeley and co-authorship in Scopus was low. There was a strong correlation between the number of citations in Mendeley and that in other databases. The correlation between the authors' H-index in the Mendeley database and other databases is positive and significant, stronger in Scopus and WoS than Google Scholar. It was finally concluded that researchers’ activities in social networks attract more readers, increase the number of citations and thus increase the H-index score in databases. Therefore, they need to be more active in social networks to increase their H-index score and promote academic publications.https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.14.7
