Geodesic
Development of a student employment forecasting service
News of the Kabardin-Balkar scientific center of RAS, Tome 26 (2024) no. 3, pp. 55-67.

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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’s 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.
Keywords: employment, university graduates, web service, neural network
Mots-clés : data collection 
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K. Ch. Bzhikhatlov; A. D. Mutlu; L.S. Mangusheva. Development of a student employment forecasting service. News of the Kabardin-Balkar scientific center of RAS, Tome 26 (2024) no. 3, pp. 55-67. http://geodesic.mathdoc.fr/item/IZKAB_2024_26_3_a4/

[1] Yu. S. Polozhentseva, O. V. Sogacheva, A. A. Yaroshenko, “Forecasting the dynamics of the gross regional product using neural networks”, Natural-humanitarian studies, 2021, no. 4 (36), 235–240 (In Russian) | DOI

[2] E. N. Churaev, A. V. Savchenko, “Recognition of facial expressions based on adaptation of the user video data classifier”, Computer optics, 2023, no. 5, 806–815 (In Russian) | DOI | DOI

[3] N. D. Zavodchikov, N. V. Speshilova, S. S. Taspaev, “The use of neural network technologies in predicting the efficiency of grain production”, Proceedings of the Orenburg State Agrarian University, 2015, no. 1 (51), 216–219 (In Russian)

[4] A. Sh. Gaisina, A. I. Minyazev, L. I. Migranova, “Digital model for predicting the employment of a university graduate taking into account behavioral aspects”, Ars administrandi, 15(3) (2023), 406–425 (In Russian) | DOI

[5] G. E. Hinton, S. Osindero, Y. W. Teh, “A fast-learning algorithm for deep belief nets”, Neural computation, 18:7 (2006), 1527–1554 | DOI | MR | Zbl

[6] N. E. Kosykh, A. D. Khomonenko, O. N. Kuranova, “Development of a web application for analyzing the sentiment of text using the flask framework and the Python language”, High technologies in earth space research, 2022, no. 1, 45–52 (In Russian) | DOI | DOI

[7] Z. Liang, Z. Liang, Y. Zheng et al., “Data analysis and visualization platform design for batteries using flask-based Python Web service”, World electric vehicle journal, 12:4 (2021), 187 | DOI

[8] B. Aakash, A. Srilakshmi, “MAGE: An efficient deployment of Python flask Web application to appengine flexible using Google Cloud platform”, Lecture Notes in networks and systems, Singapore, Springer, 2020, 59–68 | DOI

[9] M. S. Kachkov, “Creation of a neural network for solving various applied problems”, News of Tula State University. Technical science, 2023, no. 2, 339–343 (In Russian) | DOI

[10] W. B. Fradj, M. Turki, F. Gargouri, “Deep learning based on TensorFlow and Keras for predictive monitoring of business process execution delays”, Model and Data Engineering, Springer Nature Switzerland, 2023, 156–169 | DOI

[11] K. Balan, M. Santora, M. Faied, V. D. Carmona-Galindo, “Study of evolution By automating hardy weinberg equilibrium with machine learning techniques in TensorFlow and Keras”, 2020 Advanced Computing and Communication Technologies for High Performance Applications (Cochin, India, 2020), 14–19 | DOI

[12] N. S. Fartushnov, “Python language libraries for machine learning, their capabilities and advantages”, Theory and practice of modern science, 2020, no. 5 (59), 397–403 (In Russian)

[13] M. Vagizov, A. Potapov, K. Konzhgoladze et al., “Prepare and analyze taxation data using the Python pandas library”, IOP Conference Series: Earth and Environmental Science, 876, no. 1, IOP Publishing, 2021, 012078 | DOI

[14] A. N. Sychugov, “Application of machine learning for the analysis of technical characteristics and parameters of operating rolling stock on high-speed railways”, Bulletin of Scientific Research Results, 2023, no. 2, 171–180 (In Russian) | DOI | DOI