ANALYSIS OF THE USE OF MACHINE LEARNING TECHNOLOGY IN THE SOFTWARE OF THE ELECTRONIC LIBRARY HES

ilhomjonqodirov02

Authors

 

 Maqsudov H.T. – candidate of physical and mathematical sciences, professor, Department ofProgramming and Information Systems, Polytechnic Institute of Tajik Technical University,

 Khujand, Republic of Tajikistan, maksudov_kh@yahoo.com

Muzafarov M.P. – Master student of 2nd year, Department of Programming and Information Systems, Polytechnic Institute of Tajik Technical University, Khujand, Republic of Tajikistan,

 muzafarov@gmail.com

 

Annotation

 

 The article describes the work of the information system of the electronic library of the PITTU named after academician M.S. Osimi. The digital library plays a crucial role in raising the level of education and training of university students, and the effectiveness of its use depends on the library software. In order to further improve the level of service of the Institute’s library for users of the system, for example, students and teachers, are analyzed the methods of introducing machine learning technology. The results of the work of researchers in the field are also analysed. The main problems faced by the library staff during the operation of the information system of the electronic library are described in detail, and the ways to solve this problem using machine learning technology are analyzed. The conclusion notes that machine learning methods and algorithms in science can be used not only as a way of new research, but also as a way of introducing additional services, creating intelligent systems and improving service levels.

 

Keywords

 

 machine learning, information system, electronic library, automation, intelligent software.

 

References

 

  1. Library 4.0: technologies and services of the future / Sergey Nazarovets, Evgeniya Kulik // Bibliyatechny light, 2017, n. 6.
  2. Comparative analysis of the methods “decision tree” and “random forest” – in determining the specialty of scientific texts / Maksudov Kh.T., Inomov BB, Mullodzhanov NM // Bulletin of the Tajik National University. Series of natural sciences. – 2019. -No. 3. – Pp. 23-29.
  3. A Semi-supervised Approach for Improving Search, Navigation and Data Quality in Autonomous Digital Libraries / Mikalai Krapivin // International Doctorate School in Information and Communication Technologies – 2019.
  4. Extraction from Scientific Documents: Improving Machine Learning Approaches with Natural Language Processing / Mikalai Krapivin, Aliaksandr Autayeu, Maurizio Marchese, Enrico Blanzieri, and Nicola Segata, Keyphrases // DISI, University of Trento, Italy – 2010, 19 pages.
  5. Introduction to Machine Learning for Digital Library Applications. / Rodney D. Nielsen // In Proceedings of The 18th ACM/IEEE Joint Conference on Digital Libraries, Fort Worth, Texas USA, June 3-7 2018 (JCDL’18), 2 pages.
  6. Machine Learning+Online Libraries=IDL / Giovanni Semeraro // Dipartimento di Informatica–Universit degli Studi di Bad Via E. Orabona 4-70126 Bari, Italy, p 195-214.

 

 

Publication date

2023-10-27

 

 

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