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

• Published in: Archives of computational methods in engineering 2023-09, Vol.30 (7), p.4177-4207
• Main Authors: Ezugwu, Absalom E., Oyelade, Olaide N., Ikotun, Abiodun M., Agushaka, Jeffery O., Ho, Yuh-Shan
• Format: Article
• Language: English
• Subjects: Bibliometrics; Documents; Engineering; Literature reviews; Machine learning; Mathematical and Computational Engineering; Natural language processing; Object recognition; Pattern recognition; Research facilities; Review; Review Article; Trends
• ISSN: 1134-3060; 1886-1784; 1886-1784
• DOI: 10.1007/s11831-023-09930-z

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.