Abstract
The main tasks of modern scientometrics are considered, including monitoring the effectiveness of science, and the possibility of solving them with the use of high-performance artificial intelligence tools is analyzed. The characteristics of artificial intelligence as a branch of computer science are presented, and the contribution of neuroinformatics to its development is noted. The common features and differences of the main types of machine learning developed to date are considered: classical, deep, hybrid, and automatic learning. The features of the functioning of artificial neural networks are presented, including their internal structure, order of operation, distinctive features, areas, and conditions of application. Examples of the practical use of artificial intelligence tools in modern scientometric research are given: central attention is paid to the advanced developments of the Indian scientific school. The urgently demanded method of article-by-article classification of scientific literature, as proposed by Arab scientists, is also outlined. A conclusion is drawn about the great importance of artificial intelligence and the relevance of its application for the implementation of new opportunities in optimizing scientometric research.
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Notes
An artificial intelligence trainer is a person who, among other things, writes and checks texts for training a neural network, trains the neural network on new data, and corrects its work.
Analysis of association rules is a method that allows you to find patterns between events that are related in meaning or logic of action.
Regression is a method that relates a dependent variable to one or more independent (explanatory) variables. A regression model indicates whether changes in a dependent variable are associated with changes in one or more explanatory variables.
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The work was carried out as part of a study on the topic FFFU-2021-0007 of the State Assignment of the VINITI RAS.
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Melnikova, E.V. Relevance of Application of Artificial Intelligence Toolkit in Modern Scientometric Research. Sci. Tech. Inf. Proc. 51, 57–63 (2024). https://doi.org/10.3103/S014768822401009X
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DOI: https://doi.org/10.3103/S014768822401009X