Development of intelligent electronic document management system model based on machine learning methods

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.251689

Keywords:

electronic document management system, machine learning, multi-agent technologies, topic modeling

Abstract

With the daily increase in document flow, as well as the transition to paperless document management around the world, the demand for electronic document management systems is increasing. This significantly requires optimization of these systems in terms of quality document information retrieval and document management. However, research based on statistical methods cannot effectively handle large amounts of data extracted from electronic documents. In this regard, machine learning methods can effectively solve this problem. This paper presents an approach to building a model of an intelligent document management system using machine learning techniques to ensure efficient employee performance in organizations. The authors have solved a number of problems to optimize each of the document management subsystems, resulting in the development of an intelligent document management system model, which can be effectively applied to enterprises, government and corporate institutions. The feasibility and effectiveness of the proposed model of intelligent document management system based on machine learning and multi-agent modeling of information retrieval processes provides maximum reliability and reduced time of work on documents. The obtained results show that with the help of the presented model it is possible to further develop an intelligent document management system that will allow an electronic document to qualitatively go through the whole life cycle of a document, starting from the moment of document registration and finishing with its closing, i.e. execution, which will greatly facilitate the daily work of users with large volumes of documents. At the same time, the paper considers the application of topic modeling methods and algorithms of text analysis based on a multi-agent approach, which can be used to build an intelligent document management system.

Supporting Agency

  • «This research has been funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No AP08857179)»

Author Biographies

Madina Sambetbayeva, L. N. Gumilyov Eurasian National University; Institute of Information and Computational Technologies

PhD, Associate Professor

Department of Information Systems

Senior Researcher

Inkarzhan Kuspanova, L. N. Gumilyov Eurasian National University

PhD Student

Department of Information Systems

Aigerim Yerimbetova, Institute of Information and Computational Technologies; Satbayev University

PhD, Associate Professor, Leading Researcher

Department of Software Engineering

Institute of Automation and Information Technologies

Sandugash Serikbayeva, L. N. Gumilyov Eurasian National University

Teacher

Department of Information Systems

Shynar Bauyrzhanova, L. N. Gumilyov Eurasian National University

PhD Student

Department of Information Systems

References

  1. Lapshina, S. N. (2012). Architecture of Enterprise. Yekaterinburg: UrFU.
  2. Alpaidin, E. (2017). Machine learning: the new artificial intelligence. Moscow: Alpina Publisher, Publishing Group "Tochka", 208. Available at: https://cdn1.ozone.ru/multimedia/1017469342.pdf
  3. Deelman, E., Mandal, A., Jiang, M., Sakellariou, R. (2019). The role of machine learning in scientific workflows. The International Journal of High Performance Computing Applications, 33 (6), 1128–1139. doi: https://doi.org/10.1177/1094342019852127
  4. Obukhov, A., Krasnyanskiy, M., Nikolyukin, M. (2019). Implementation of Decision Support Subsystem in Electronic Document Systems Using Machine Learning Techniques. 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). doi: https://doi.org/10.1109/fareastcon.2019.8934879
  5. Obukhov, A., Krasnyanskiy, M., Nikolyukin, M. (2020). Algorithm of adaptation of electronic document management system based on machine learning technology. Progress in Artificial Intelligence, 9 (4), 287–303. doi: https://doi.org/10.1007/s13748-020-00214-2
  6. Levina, T., Rodionov, A., Farkhutdinov, R. (2020). Software module for extracting data from electronic documents. 2020 International Conference on Electrotechnical Complexes and Systems (ICOECS). doi: https://doi.org/10.1109/icoecs50468.2020.9278492
  7. Goodrum, H., Roberts, K., Bernstam, E. V. (2020). Automatic classification of scanned electronic health record documents. International Journal of Medical Informatics, 144, 104302. doi: https://doi.org/10.1016/j.ijmedinf.2020.104302
  8. Kostkina, A., Bodunkov, D., Klimov, V. (2018). Document Categorization Based on Usage of Features Reduction with Synonyms Clustering in Weak Semantic Map. Procedia Computer Science, 145, 288–292. doi: https://doi.org/10.1016/j.procs.2018.11.061
  9. Chemchem, A., Alin, F., Krajecki, M. (2018). Deep Learning and Data Mining Classification through the Intelligent Agent Reasoning. 2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW). doi: https://doi.org/10.1109/w-ficloud.2018.00009
  10. Holzinger, A., Kieseberg, P., Tjoa, A. M., & Weippl, E. (Eds.) (2018). Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science. Springer, 372. doi: https://doi.org/10.1007/978-3-319-99740-7
  11. Edinaya sistema elektronnogo dokumentooborota gosudarstvennyh organov (ESEDO). Available at: https://www.nitec.kz/index.php/post/edinaya-sistema-elektronnogo-dokumentooborota-gosudarstvennyih-organov-esedo
  12. Aliev, V. S., Chistov, D. V. (2011). Business planning using the Project Expert program (full course). Moscow: INFRA-M, 432.
  13. Eremeev, M., Vorontsov, K. (2019). Lexical quantile-based text complexity measure. Proceedings of Recent Advances in Natural Language Processing. Varna, 270–275. Available at: https://aclanthology.org/R19-1031.pdf
  14. Ataeva, O. M. (2016). An information model of LibMeta semantic library. Software & Systems, 4, 36–44. doi: https://doi.org/10.15827/0236-235x.116.036-044
  15. Semantic Web. Available at: https://www.w3.org/standards/semanticweb/
  16. Weitzel, D., Bockelman, B., Brown, D. A., Couvares, P., Würthwein, F., Hernandez, E. F. (2017). Data Access for LIGO on the OSG. Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact. doi: https://doi.org/10.1145/3093338.3093363
  17. Linev, A. A. (2014). Modern EDMS: From Document Management to Efficiency Management. Deloproizvodstvo, 1. Available at: https://www.top-personal.ru/officeworkissue.html?314
  18. Challenger, M., Tezel, B., Alaca, O., Tekinerdogan, B., Kardas, G. (2018). Development of Semantic Web-Enabled BDI Multi-Agent Systems Using SEA_ML: An Electronic Bartering Case Study. Applied Sciences, 8 (5), 688. doi: https://doi.org/10.3390/app8050688
  19. Jensen, A. B., Villadsen, J. (2020). GOAL-DTU: Development of Distributed Intelligence for the Multi-Agent Programming Contest. Lecture Notes in Computer Science, 79–105. doi: https://doi.org/10.1007/978-3-030-59299-8_4

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Published

2022-02-25

How to Cite

Sambetbayeva, M., Kuspanova, I., Yerimbetova, A., Serikbayeva, S., & Bauyrzhanova, S. (2022). Development of intelligent electronic document management system model based on machine learning methods. Eastern-European Journal of Enterprise Technologies, 1(2(115), 68–76. https://doi.org/10.15587/1729-4061.2022.251689

Issue

Vol. 1 No. 2(115) (2022): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2022 Madina Sambetbayeva, Inkarzhan Kuspanova, Aigerim Yerimbetova, Sandugash Serikbayeva, Shynar Bauyrzhanova

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