INTELLIGENT SYSTEM OF PERSONNEL MANAGEMENT

Authors

  • V. M. Sineglazov National Aviation University, Kyiv

DOI:

https://doi.org/10.18372/1990-5548.59.13634

Keywords:

Human resource management, neural network, machine learning algorithm, genetic algorithm, chromosome structure, optimal choice

Abstract

It is considered the problem of Human Resources Management with help of Artificial Neural Networks in area of desired job search and help of recruiters that use this resource to find the best candidate for a given job. It is proposed to use neural networks for view CVs, ranking candidates according to their skill level and create machine learning algorithms to automate processes for checking resumes. The convolutional neural network is used for the problems solution. As a learning algorithms is used  a genetic algorithm. It is developed an approach for chromosome structure optimal choice.

Author Biography

V. M. Sineglazov, National Aviation University, Kyiv

Aviation Computer-Integrated Complexes Department

Doctor of Engineering Science. Professor. Head of the Department

orcid.org/0000-0002-3297-9060

References

30+ Talent Acquisition Technologies That Use #ArtificialIntelligence https://workology.com/30-talent-acquisition-technologies-that-use-artificialintelligence/

Xiang Zhang, Junbo Zhao, and Yann LeCun, “Character-level convolutional networks for text classification,” in Advances in Neural Information Processing Systems. pp. 649–657, Feb. 2015.

Yoon Kim, “Convolutional neural networks for sentence classification,” IEMNLP, pp. 1746–1751, Sep. 2014.

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, “Efficient estimation of word representations in vector space,” ICLR, 2013. arXiv:1301.3781v3 [cs.CL] 7 Sep 2013.

Jeffrey Pennington, Richard Socher, and Christopher D. Manning, “Glove: Global vectors for word representation,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543, October 25-29, 2014, Doha, Qatar.

Yoshua Bengio Yann LeCun, Leon Bottou, and Patrick Haffner, “Gradient-based learning applied to document recognition,” Proc. of the IEEE November 1998, pp. 1–46.

Zellig Harris, Distributional structure, Word, 1954.

Jones K. S., “A statistical interpretation of term specificity and its application in retrieval,” Journal of Documentation. 1972.

Marc Damashek, Gauging similarity with n-grams: Language-independent categorization of text. Science, New Series, 1995.

V. M. Sineglazov and O. I. Chumachenko, "Structural and Parametric Synthesis of Packing Neural Networks," Materials of the International Scientific and Practical Conference "Information Technologies and Computer Modeling," Ivano-Frankivsk–Yaremche, Ukraine (May 14-19, 2018), pp. 225–228. (in Ukrainian)

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Issue

Vol. 1 No. 59 (2019)

Section

THEORY AND METHODS OF SIGNAL PROCESSING

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