{"id":2015,"date":"2025-05-13T09:43:46","date_gmt":"2025-05-13T08:43:46","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=2015"},"modified":"2026-03-26T11:51:43","modified_gmt":"2026-03-26T11:51:43","slug":"26-3-5-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/26-3-5-en\/","title":{"rendered":"26.3.5 en"},"content":{"rendered":"\n<h1 class=\"wp-block-heading has-lora-font-family\" style=\"font-size:24px\"><strong>Development of a student employment forecasting service<\/strong><\/h1>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-medium-font-size wp-elements-fa724cd08cf64f3d2ec3dd484dd3d936 wp-block-paragraph\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\"><strong><strong><strong><strong>K.Ch. Bzhikhatlov, A.D. Mutlu, L.S. Mangusheva<\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\"\/>\n\n\n\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-b184d647 wp-block-group-is-layout-flex\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\">\n<p class=\"has-text-color has-link-color has-lora-font-family has-medium-font-size wp-elements-53f47014b6d7b0bdfff77f9db451e97c wp-block-paragraph\" style=\"color:#5b1919;text-decoration:underline\"><strong>Upload the full text<\/strong><\/p>\n\n\n\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-6b221332 wp-block-group-is-layout-flex\" style=\"min-height:0px;margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\">\n<div class=\"wp-block-buttons is-content-justification-left is-layout-flex wp-container-core-buttons-is-layout-856cf56e wp-block-buttons-is-layout-flex\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-100 is-style-outline is-style-outline--1\"><a class=\"wp-block-button__link has-background-background-color has-text-color has-background has-link-color has-border-color has-small-font-size has-custom-font-size wp-element-button\" href=\"http:\/\/izvestiyakbncran.ru\/wp-content\/uploads\/2025\/05\/bzhih-5.pdf\" style=\"border-color:#5b1919;border-style:solid;border-width:2px;border-radius:8px;color:#5b1919;padding-top:0.4rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:0.4rem;padding-left:var(--wp--preset--spacing--40)\">PDF<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:0px;width:0px\" aria-hidden=\"true\" class=\"wp-block-spacer wp-container-content-273e683f\"><\/div>\n<\/div>\n<\/div>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-1114bb4f9ee216df9fbff27dd16f3274 wp-block-paragraph\" style=\"line-height:1.4\"><strong><em>Abstract<\/em><\/strong>. The article presents the architecture and software implementation of a service for collecting data and predicting student employment based on a fully connected neural network using the example of graduates of Kabardino-Balkarian State University in areas related to information technology. The service operates as a website and allows to collect, store and analyse data on academic performance, activity, employment and learning conditions of graduates. The presented neural network model predicts the main parameters associated with employment based on the results of a student survey, including the expected level of remuneration, job search time and employee workload. At the same time, the set of input parameters used makes it possible to take into account not only data on academic performance, but also demographic indicators and conditions in the region of the student\u2019s studies. Forecasting employment conditions can be used not only to select the direction of training for applicants and build educational trajectories for students, but also to plan changes to the university curriculum. The work provides the structure of the site, the architecture of the neural network and a description of the software implementation of the service.<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-849dd9d6376376d3d460db3c46a0a838 wp-block-paragraph\" style=\"line-height:1.4\"><strong><em>Keywords<\/em><\/strong>: employment, university graduates, web service, neural network, data collection<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family wp-elements-4d9a8140c9d9900f317b15b5de4ead06 wp-block-paragraph\" style=\"font-size:12px;line-height:1.4\"><strong>For citation<\/strong>. Bzhikhatlov K.Ch., Mutlu A.D., Mangusheva L.S. Development of a student employment forecasting service. <em>News of the Kabardino-Balkarian Scientific Center of RAS.<\/em><strong> <\/strong>2024. Vol. 26. No. 3. Pp. 55\u201367. DOI: 10.35330\/1991-6639-2024-26-3-55-67<\/p>\n\n\n\n<details class=\"wp-block-details has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-e851df02d5e7c7ec589f213e4da205b1 is-layout-flow wp-block-details-is-layout-flow\"><summary><strong>References<\/strong><\/summary>\n<ol class=\"wp-block-list\">\n<li>Polozhentseva Yu.S., Sogacheva O.V., Yaroshenko A.A. Forecasting the dynamics of the gross regional product using neural networks. <em>Yestestvenno-gumanitarnyye issledovaniya<\/em> [Natural-humanitarian studies]. 2021. No. 4(36). Pp. 235\u2013240. DOI: 10.24412\/2309-4788-2021-11303. (In Russian)<\/li>\n\n\n\n<li>Churaev E.N., Savchenko A.V. Recognition of facial expressions based on adaptation of the user video data classifier. <em>Computer optics.<\/em> 2023. No. 5. Pp. 806\u2013815. DOI: 10.18287\/2412-6179-CO-1269. (In Russian)<\/li>\n\n\n\n<li>Zavodchikov N.D., Speshilova N.V., Taspaev S.S. The use of neural network technologies in predicting the efficiency of grain production. <em>Izvestiya Orenburgskogo gosudarstvennogo agrarnogo universiteta<\/em> [Proceedings of the Orenburg State Agrarian University]. 2015. No. 1(51). Pp. 216\u2013219. EDN: TKKYMT. (In Russian)<\/li>\n\n\n\n<li>Gaisina A.Sh., Minyazev A.I., Migranova L.I. Digital model for predicting the employment of a university graduate taking into account behavioral aspects. <em>Ars administrandi.<\/em> 2023. Vol. 15(3). Pp. 406\u2013425. DOI: 10.17072\/2218-9173-2023-3-406-425. (In Russian)<\/li>\n\n\n\n<li>Hinton G.E., Osindero S., Teh Y.W. A fast-learning algorithm for deep belief nets. <em>Neural Computation.<\/em> 2006. Vol. 18(7). Pp. 1527\u20131554. DOI: 10.1162\/neco.2006.18.7.1527<\/li>\n\n\n\n<li>Kosykh N.E., Khomonenko A.D., Kuranova O.N. Development of a web application for analyzing the sentiment of text using the flask framework and the Python language. <em>High technologies in earth space research. H&amp;es research<\/em>. 2022. No. 1. Pp. 45\u201352. DOI: 10.36724\/2409-5419-2022-14-1-45-52. (In Russian)<\/li>\n\n\n\n<li>Liang Zu., Liang Zo., Zheng Y. et al. Data analysis and visualization platform design for batteries using flask-based Python Web service. World electric vehicle journal. 2021. Vol. 12. I. 4. P. 187. DOI: 10.3390\/wevj12040187.<\/li>\n\n\n\n<li>Aakash B., Srilakshmi A. MAGE: An efficient deployment of Python flask Web application to app engine flexible using Google Cloud platform. Lecture Notes in Networks and Systems. 2020. Pp. 59\u201368. DOI: 10.1007\/978-981-15-7345-3_5.<\/li>\n\n\n\n<li>Kachkov M.S. Creation of a neural network for solving various applied problems. <em>Izvestiya TulGU. Tekhnicheskiye nauki<\/em> [News of Tula State University. Technical science]. 2023. No. 2. Pp. 339\u2013343. DOI: 10.24412\/2071-6168-2023-2-339-343. (In Russian)<\/li>\n\n\n\n<li>Fradj W.B., Turki M., Gargouri F. Deep learning based on TensorFlow and Keras for predictive monitoring of business process execution delays. <em>Model and Data Engineering.<\/em> 2023. Pp. 156\u2013169. DOI: 10.1007\/978-3-031-49333-1_12.<\/li>\n\n\n\n<li>Balan K., Santora M., Faied M. et al. Study of evolution by automating Hardy Weinberg equilibrium with machine learning Techniques In TensorFlow And Keras. <em>2020 Advanced Computing and Communication Technologies for High Performance Applications <\/em>\/ Cochin, India. 2020. Pp. 14\u201319. DOI: 10.1109\/ accthpa49271.2020.9213202.<\/li>\n\n\n\n<li>Fartushnov N.S. Python language libraries for machine learning, their capabilities and advantages. <em>Teoriya i praktika sovremennoy nauki <\/em>[Theory and practice of modern science]. 2020. No. 5(59). Pp. 397\u2013403. EDN: NXQWXV. (In Russian)<\/li>\n\n\n\n<li>Vagizov M., Potapov A., Konzhgoladze K. et al. Prepare and analyze taxation data using the Python Pandas library. <em>IOP Conference Series: Earth and Environmental Science. IOP Publishing<\/em>. 2021. Vol. 876. No. 1. P. 012078. DOI: 10.1088\/1755-1315\/876\/1\/012078<\/li>\n\n\n\n<li>Sychugov A.N. Application of machine learning for the analysis of technical characteristics and parameters of operating rolling stock on high-speed railways. <em>Byulleten&#8217; rezul&#8217;tatov nauchnykh issledovaniy<\/em> [Bulletin of Scientific Research Results]. 2023. No. 2. Pp. 171\u2013180. DOI: 10.20295\/2223-9987-2023-2-171-180. (In Russian)<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n<\/details>\n\n\n\n<details class=\"wp-block-details has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-768ae4d582350761c217ec6457857a4e is-layout-flow wp-block-details-is-layout-flow\"><summary><strong>Information about the authors<\/strong><\/summary>\n<p class=\"wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\"><strong>Kantemir Ch. Bzhikhatlov<\/strong>, Candidate of Physical-Mathematical Sciences, Head of the Laboratory \u201cNeurocognitive Autonomous Intelligent Systems\u201d, Kabardino-Balkarian Scientific Center of the Russian<br>Academy of Sciences;<br>360010, Russia, Nalchik, 2 Balkarov street;<br>haosit13@mail.ru, ORCID: https:\/\/orcid.org\/0000-0003-0924-0193, SPIN-code: 9551-5494<br><strong>Aidan D. Mutlu<\/strong>, 2nd year Student in the Field of \u201cInformatics and Computer Science\u201d, KabardinoBalkarian State University named after Kh.M. Berbekov;<br>360004, Russia, Nalchik, 173 Chernyshevsky street;<br>leroy357wall@gmail.com, ORCID: https:\/\/orcid.org\/0009-0007-3792-2788<br><strong>Lyaylya S. Mangusheva<\/strong>, Candidate of Economic Sciences, Associate Professor of the Department of Informatics, Plekhanov Russian University of Economics;<br>117997, Russia, Moscow, 36 Stremyannyy lane;<br>klyalya80@mail.ru, ORCID: https:\/\/orcid.org\/0000-0002-2331-8308<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n<\/details>\n","protected":false},"excerpt":{"rendered":"<p>Development of a student employment forecasting service K.Ch. Bzhikhatlov, A.D. Mutlu, L.S. Mangusheva Upload the full text Abstract. The article presents the architecture and software implementation of a service for collecting data and predicting student employment based on a fully connected neural network using the example of graduates of Kabardino-Balkarian State University in areas related [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2015","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>26.3.5 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/26-3-5-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"26.3.5 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Development of a student employment forecasting service K.Ch. 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Bzhikhatlov, A.D. Mutlu, L.S. Mangusheva Upload the full text Abstract. 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