Comments on: Li et al. (2019) “Bioelectrochemical Systems for Groundwater Remediation: The Development Trend and Research Front Revealed by Bibliometric Analysis” Water, 11, 1532

Ho, Yuh‐Shan

doi:10.3390/w12061586

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Ho’s group first proposed the “front page” as a filter to improve bias by using the data from SCI-EXPANDED directly, including the article title, abstract, and author keywords [ 41 ]. It has been pointed out that a significant difference was found by using the ‘front page’ as a filter in bibliometric research in wide journals classified in SCI-EXPANDED, for example, Frontiers in Pharmacology [ 42 ], Chinese Medical Journal [ 43 ], Environmental Science and Pollution Research [ 44 ], Water [ 45 ], Science of the Total Environment [ 46 ], and Journal of Foot and Ankle Surgery [ 47 ]. The ‘front page’ filter can avoid introducing unrelated publications for bibliometric analysis.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

Ezugwu

Oyelade

Ikotun

et al. 2023

Arch Computat Methods Eng

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The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa’s most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

Ezugwu

Oyelade

Ikotun

et al. 2023

Arch Computat Methods Eng

Self Cite

View full text Add to dashboard Cite

show abstract

Comments on: ‘Glyphosate and its toxicology: A scientometric review’, Sci. Total Environ., (2020) 733: 139359

2021

Science of The Total Environment

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Comments on: Li et al. (2019) “Bioelectrochemical Systems for Groundwater Remediation: The Development Trend and Research Front Revealed by Bibliometric Analysis” Water, 11, 1532

Abstract: Li et al. (2019) used inappropriate searching words and method to publish a bibliometric paper in Water. The results show a huge difference from the results by using an appropriate method. This comment points out each of problems.

Cited by 2 publications

References 25 publications

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

Comments on: ‘Glyphosate and its toxicology: A scientometric review’, Sci. Total Environ., (2020) 733: 139359

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