The present study identifies and assesses the bibliographic trend in
Artificial Intelligence (AI) research for the years 2015-2020 using the science
mapping method of bibliometric study. The required data has been collected from
the Scopus database. To make the collected data analysis-ready, essential data
transformation was performed manually and with the help of a tool viz.
OpenRefine. For determining the trend and performing the mapping techniques,
top five open access and commercial journals of AI have been chosen based on
their citescore driven ranking. The work includes 6880 articles published in
the specified period for analysis. The trend is based on Country-wise
publications, year-wise publications, topical terms in AI, top-cited articles,
prominent authors, major institutions, involvement of industries in AI and
Indian appearance. The results show that compared to open access journals;
commercial journals have a higher citescore and number of articles published
over the years. Additionally, IEEE is the prominent publisher which publishes
84% of the top-cited publications. Further, China and the United States are the
major contributors to literature in the AI domain. The study reveals that
neural networks and deep learning are the major topics included in top AI
research publications. Recently, not only public institutions but also private
bodies are investing their resources in AI research. The study also
investigates the relative position of Indian researchers in terms of AI
research. Present work helps in understanding the initial development, current
stand and future direction of AI.

通过科学映射方法的文献计量研究，本研究确定和评估了 2015-2020 年间人工智能研究的参考文献趋势。使用 Scopus 数据库收集了所需的数据，并进行数据转换和映射技术分析，结果显示神经网络和深度学习是顶尖人工智能研究论文的主题之一。