Geodesic
Research on main methods of automatic processing, grouping
News of the Kabardin-Balkar scientific center of RAS, Tome 27 (2025) no. 1, pp. 111-119.

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The article examines main methods of automatic processing, grouping and annotation of information. It is shown that methods of automatic analysis of Data Mining are based on the use of certain statistical patterns (classification, regression), search for keywords, but do not use algorithms for linguistic processing of texts. Thus, automatic analysis of text information carried out by modern means of analytical processing is not able to process texts content. To compare two simple sentences by content, the resolution method was used. Studies have shown the unification algorithm does not take into account © Тихонов Д. В., 2025 INFORMATICS AND INFORMATION PROCESSES 112 News of the Kabardino-Balkarian Scientific Center of RAS Vol. 27 No. 1 2025 content of sentences. Because the solution to the problem of comparative analysis of text information by content, a new algorithm for working with logical and linguistic models is proposed. The scientific novelty of the results obtained lies in the method of quickly extracting a set of local descriptors describing all parts of the image, which allows us to significantly speed up the annotation process and form a more complete global visual descriptor of the image.
Keywords: methods, automatic processing, grouping, Data Mining, resolution method
Mots-clés : annotation, information 
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D. V. Tikhonov. Research on main methods of automatic processing, grouping. News of the Kabardin-Balkar scientific center of RAS, Tome 27 (2025) no. 1, pp. 111-119. http://geodesic.mathdoc.fr/item/IZKAB_2025_27_1_a1/

[1] A. Hammar, “Automatic Information Processing”, In: Encyclopedia of the Sciences of Learning, 2012, Springer, Boston, MA | DOI

[2] E. Khazaei, A. Alimohammadi, “An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks”, ISPRS Int. J. Geo-Inf, 7:2 (2018), 67 | DOI

[3] V. O. Yachnaya, V. R. Lutsiv, R. O. Malashin, “Modern technologies for automatic recognition of means of communication based on visual data”, KO, 47:2 (2023), 287–305 (In Russian)

[4] T. R. Nazarov, N. A. Mamedova, “Automated solution to the problem of detecting industrial objects on an orthomosaic using a neural network”, Software products and systems, 36:1 (2023), 144–158 (In Russian) | DOI

[5] S. O. Vlasov, A. I. Gladyshev, A. A. Boguslavsky, S. M. Sokolov, “Solving the problem of object detection using neural network technologies”, Preprints of the Keldysh IPM, 2023, no. 16, 27 pp. (In Russian) | DOI

[6] A. E. Gaisin, “Analysis of existing methods of automatic text analysis”, Bulletin of Science, 4:6 (63) (2023), 254–258 (In Russian)

[7] N. D. Prigodich, S. S. Korobko, “Application of software methods for automated processing of sources of personal origin”, Historical informatics, 2023, no. 1 (43), 1–9 (In Russian) | DOI

[8] H. Li, D. Yuan, X. Ma, D. Cui, L. Cao, “Genetic algorithm for the optimization of features and neural networks in ECG signals classification”, Scientific Reports, 7:1 (2017), 1–12 | DOI

[9] X. He, K. Zhao, X. Chu, “AutoML: A Survey of the State-of-the-Art”, Knowledge-Based Systems, 212 (2021), 106622 | DOI

[10] H. Jin, Q. Song, X. Hu, “Auto-keras: An efficient neural architecture search system”, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019, 1946–1956 | DOI

[11] E. Real, A. Aggarwal, Y. Huang, Q.V. Le, “Regularized evolution for image classifier architecture search”, Proceedings of the AAAI Conference on Artificial Intelligence, 33 (2018), 4780–4789 | DOI