{"title":"Relevance of Application of Artificial Intelligence Toolkit in Modern Scientometric Research","authors":"E. V. Melnikova","doi":"10.3103/s014768822401009x","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>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.</p>","PeriodicalId":43962,"journal":{"name":"Scientific and Technical Information Processing","volume":"54 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific and Technical Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s014768822401009x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
Scientific and Technical Information Processing is a refereed journal that covers all aspects of management and use of information technology in libraries and archives, information centres, and the information industry in general. Emphasis is on practical applications of new technologies and techniques for information analysis and processing.
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