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Scientific and Technical Information Processing

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01-06-2023

Needs of Scientometry and Possibilities of Modern Machine Learning as a Field of Artificial Intelligence

Author: E. V. Melnikova

Published in: Scientific and Technical Information Processing | Issue 2/2023

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Abstract—

A general description of modern scientometry, its main tasks, and its research methods is presented. The issues of the application of conventional machine learning and deep learning algorithms as tools of artificial intelligence in the thematic classification of scientific literature are considered. The problems and limitations of the classification of literature by sections of science in the systems of indexing and citing of scientific information are outlined. The author presents a specific example of a deep learning application for by-article thematic classification based on convolutional neural networks that was designed by scientists from the United Arab Emirates and Jordan. The article emphasizes the importance of the use of deep learning applications and models for creating correct classifications of the scientific literature that correspond to the realities of the development of science and that are capable of increasing the accuracy of calculating scientometric indicators.

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Title: Needs of Scientometry and Possibilities of Modern Machine Learning as a Field of Artificial Intelligence
Author: E. V. Melnikova
Publication date: 01-06-2023
Publisher: Pleiades Publishing
Published in: Scientific and Technical Information Processing / Issue 2/2023
Print ISSN: 0147-6882
Electronic ISSN: 1934-8118
DOI: https://doi.org/10.3103/S0147688223020089

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