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Modelling and Data Analysis
2022. Vol. 12, no. 2, 103–109
https://doi.org/10.17759/mda.2022120205
ISSN: 2219-3758 / 2311-9454 (online)

Using a Self-Learning Algorithm with Elements of Artificial Intelligence Based on Markov Chains to Correct the Semantic Core

 
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Abstract

The article discusses the effective use of a software tool with elements of artificial intelligence based on Markov chains. Based on the rich personal experience of the author, a description of typical problems arising in this area is given. This article describes only recommendations for using the algorithm.

General Information

Keywords: semantic core, Markov chains, key phrases, artificial intelligence

Journal rubric: Short Messages

OpenAlex citations: 1

OpenAlex trends: Advanced Data Processing Techniques, Engineering Education and Technology, Technology and Human Factors in Education and Health

Information about the work in OpenAlex

Number of citations: 1

Topics

Advanced Data Processing Techniques

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Engineering Education and Technology

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Technology and Human Factors in Education and Health

This cluster of papers explores the future of personalized medicine in healthcare, focusing on topics such as artificial intelligence, healthcare technology, medical innovation, ergonomics, biomedical signal analysis, digital health, cyber-physical systems, healthcare automation, and clinical decision support. The papers cover a wide range of interdisciplinary research related to advancing personalized medical treatments and technologies.

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Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2022120205

Received 03.06.2022

Accepted

Published

For citation: Kolotovkin, I.S. (2022). Using a Self-Learning Algorithm with Elements of Artificial Intelligence Based on Markov Chains to Correct the Semantic Core. Modelling and Data Analysis, 12(2), 103–109. (In Russ.). https://doi.org/10.17759/mda.2022120205

© Kolotovkin I.S., 2022

License: CC BY-NC 4.0

References

  1. Rykov A.S. Methods of system analysis: Multicriteria and fuzzy optimization, modeling and expert evaluations. — M.: Economics, 1999.
  2. Search Engine Optimization (SEO) Secrets / Danny Dover, Erik Dafforn. — Indianapolis: John Wiley & Sons Limited, 2011.
  3. Kolotovkin I.S., Kulik S.D., Shevchenko A.A., Neural networks in the problems of query clustering when compiling a semantic core //Abstracts. XVII All-Russian Scientific Conference "Neurocomputers and their applications" NKP-2019. Moscow, March 19, 2019 — M .: FGBOU VO MGPPU, 2019. — P. 262-263.
  4. Kelbert M. Ya., Sukhov Yu. M. Probability and statistics in examples and problems. Vol. II: Markov chains as a starting point for the theory of random processes and their applications. — M.: MTSNMO, 2009.

Information About the Authors

Igor S. Kolotovkin, Junior Researcher, Center for Information Technologies for Psychological Research, Moscow State University of psychology and education (MSUPE), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-6126-4849, e-mail: is@kolotovkin.pro

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