\u0423\u0414\u041a 303.4<\/p>\n\n\n\n
Machine learning methods in human sciences<\/strong><\/p>\n\n\n\n Boris A. Shpak1<\/sup>, Kseniya A. Degtyarenko2<\/sup>, Anna A. Shpak3<\/sup><\/strong><\/p>\n\n\n\n 1<\/sup> orcid.org\/0000-0002-3262-1886<\/a>, shpakb@gmail.com<\/a><\/p>\n\n\n\n 2<\/sup> \u0421\u0438\u0431\u0438\u0440\u0441\u043a\u0438\u0439 \u0444\u0435\u0434\u0435\u0440\u0430\u043b\u044c\u043d\u044b\u0439 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442. 3<\/sup> \u0421\u0438\u0431\u0438\u0440\u0441\u043a\u0438\u0439 \u0444\u0435\u0434\u0435\u0440\u0430\u043b\u044c\u043d\u044b\u0439 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442. Abstract.<\/strong> This article is devoted to the study of methods of statistical analysis in the field of humanities. The basic algorithms used for data analysis, which considered training with a teacher and training without a teacher. The fields of application in the humanities are quite extensive and include the processing of any data to predict the necessary factors. In the humanities, machine learning identifies cultural patterns, cultural processes, systematizes archaeological data, classifies works of art by genre based on the identification of iconography, allows you to model some social processes using machine learning algorithms.<\/p>\n\n\n\n Keywords<\/em><\/strong>: machine-learning, humanities, information technologies.<\/em><\/p>\n\n\n\n 1. Alsayat A., El-Sayed H. Social media analysis using optimized K-Means clustering \/\/ IEEE 14th International Conference on Software Engineering Research, Management and Applications (SERA), 2016. \u0420\u0440. 61-66. DOI: 10.1109\/SERA.2016.7516129 <\/p>\n\n\n\n 2. Arseniev-Koehler A., Foster G. J., Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What It Means to Be Fat \/\/ Preprint, submitted March 24, 2020. – URL: https:\/\/arxiv.org\/abs\/2003.12133. <\/p>\n\n\n\n 3. Bickler S.H. Machine Learning Arrives in Archaeology \/\/ Advances in Archaeological Practice, 2021, \u21169 (2), 2100006. \u0420\u0440. 186-191. EDN: DUPJHL <\/p>\n\n\n\n 4. Carah N, Angus D. Algorithmic brand culture: participatory labour, machine learning and branding on social media \/\/ Media, Culture & Society, 2018, 40 (2). \u0420\u0440. 178-194. DOI: 10.1177\/0163443718754648 <\/p>\n\n\n\n 5. Davis D.S., Caspari, G., Lipo, C.P., Sanger, M.C. Deep learning reveals extent of Archaic Native American shell-ring building practices \/\/ Journal of Archaeological Science, 2021, \u2116132. \u0420\u0440. 105433. DOI: 10.1016\/j.jas.2021.105433 EDN: WIFRWG <\/p>\n\n\n\n 6. DenhamW. W. Artificial Intelligence\/ machine learning research using the Australian Aborigenal Alyawarra kinship dataset: partial bibliography 2004-2020 \/\/ Mathematical Anthropology and Cultural Theory, 2020, 15(1). \u0420p. 1-16. <\/p>\n\n\n\n 7. Fiorucci M., Khoroshiltseva M., Pontil M., Traviglia A., Bue Del A., Stuart James S., Machine Learning for Cultural Heritage: A Survey \/\/ Pattern Recognition Letters, 2020, \u2116 133. \u0420p. 102-108. DOI: 10.1016\/j.patrec.2020.02.017 EDN: LTLIUX <\/p>\n\n\n\n 8. Hentschel Ch., Wiradarma T. P., Sack H. Fine tuning CNNS with scarce training data-Adapting ImageNet to art epoch classification \/\/ 2016 IEEE International Conference on Image Processing (ICIP), 2016. \u0420p. 3693-3697. <\/p>\n\n\n\n 9. Injadat M.N., Moubayed A., Nassif A.B., Shami A. Machine learning towards intelligent systems: applications, challenges, and opportunities \/\/ Artificial Intelligence Review, 2021, \u211654(5). \u0420\u0440. 3299-3348. EDN: ZJDFLR <\/p>\n\n\n\n 10. JudithL.L. Machine learning algorythms for analysis and data prediction \/\/ 2017 IEEE 37th Central America and Panama Convention, CONCAPAN 2017, 2018. \u0420p. 1. <\/p>\n\n\n\n 11. Jumper, J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O., Tunyasuvunakool K. et al. “Highly accurate protein structure prediction with AlphaFold \/\/ Nature, 2021. \u0420p. 1-11. EDN: GEXUCN <\/p>\n\n\n\n 12. Kohavi R; Provost F. Glossary of Terms \/\/ Machine Learning, 1998, \u2116 30, pp. 271-274. Available at: https:\/\/ai.stanford.edu\/~ronnyk\/glossary.html (accessed 29 July 2021). EDN: AJYLVJ <\/p>\n\n\n\n 13. Lecoutre A., Negrevergne B., Yger F. “Recognizing art style automatically in painting with deep learning”. \/\/ Asian conference on machine learning. \u0420p. 327-342. PMLR, 2017. <\/p>\n\n\n\n 14. Meng, T., Huang, R., Lu, Y. et al. Highly sensitive terahertz non-destructive testing technology for stone relics deterioration prediction using SVM-based machine learning models \/\/ Heritage Science, 2021, \u21169 (1), 24. DOI: 10.1186\/s40494-021-00502-7 EDN: SKEWZJ <\/p>\n\n\n\n 15. Simons-Morton B.G., Crump A.D. Association of parental involvement and social competence with school adjustment and engagement among sixth graders \/\/ Journal of School Health, 2003, \u2116 3 (73). \u0420p. 121-126. EDN: GMPTLB <\/p>\n\n\n\n 16. Singh Sh. P., Ajai K., Hemant D., Lenali S., Anshika R., Shikha J. Machine translation using deep learning: An overview \/\/ 2017 international conference on computer, communications and electronics (comptelix), 2017. \u0420p. 162-167. <\/p>\n<\/details>\n\n\n\n Get access to the full version of this article<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" \u0423\u0414\u041a 303.4 Machine learning methods in human sciences Boris A. Shpak1, Kseniya A. Degtyarenko2, Anna A. Shpak3 1 orcid.org\/0000-0002-3262-1886, shpakb@gmail.com 2 \u0421\u0438\u0431\u0438\u0440\u0441\u043a\u0438\u0439 \u0444\u0435\u0434\u0435\u0440\u0430\u043b\u044c\u043d\u044b\u0439 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442.orcid.org\/0000-0002-6398-0259, akseniya.krupkina@mail.ru 3 \u0421\u0438\u0431\u0438\u0440\u0441\u043a\u0438\u0439 \u0444\u0435\u0434\u0435\u0440\u0430\u043b\u044c\u043d\u044b\u0439 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442.orcid.org\/0000-0002-2948-8762, annaheyy@gmail.com Abstract. This article is devoted to the study of methods of statistical analysis in the field of humanities. The basic algorithms used for data analysis, which considered training with a teacher and … Read more<\/a><\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"cybocfi_hide_featured_image":"","_locale":"en_US","_original_post":"http:\/\/intelsiberia.ru\/?page_id=6481","footnotes":""},"wf_page_folders":[299],"class_list":["post-6483","page","type-page","status-publish","en-US"],"acf":[],"_links":{"self":[{"href":"http:\/\/intelsiberia.ru\/wp-json\/wp\/v2\/pages\/6483","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/intelsiberia.ru\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/intelsiberia.ru\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/intelsiberia.ru\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"http:\/\/intelsiberia.ru\/wp-json\/wp\/v2\/comments?post=6483"}],"version-history":[{"count":1,"href":"http:\/\/intelsiberia.ru\/wp-json\/wp\/v2\/pages\/6483\/revisions"}],"predecessor-version":[{"id":6484,"href":"http:\/\/intelsiberia.ru\/wp-json\/wp\/v2\/pages\/6483\/revisions\/6484"}],"wp:attachment":[{"href":"http:\/\/intelsiberia.ru\/wp-json\/wp\/v2\/media?parent=6483"}],"wp:term":[{"taxonomy":"wf_page_folders","embeddable":true,"href":"http:\/\/intelsiberia.ru\/wp-json\/wp\/v2\/wf_page_folders?post=6483"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
orcid.org\/0000-0002-6398-0259<\/a>, akseniya.krupkina@mail.ru<\/a><\/p>\n\n\n\n
orcid.org\/0000-0002-2948-8762<\/a>, annaheyy@gmail.com<\/a><\/p>\n\n\n\nReferences<\/strong><\/summary>\n