phone +7 (3412) 91 60 92

Archive of Issues


Russia Tambov
Year
2021
Volume
31
Issue
1
Pages
149-164
<<
Section Computer science
Title Neural network method of data processing and transmission in adaptive information systems
Author(-s) Obukhov A.D.a, Krasnyansky M.N.a
Affiliations Tambov State Technical Universitya
Abstract The problem of automation of the processes of information transmission and processing in adaptive information systems is considered. An analysis of existing approaches to solving this problem showed the prospects of using neural network technologies. A neural network method for processing and transmitting information in adaptive information systems is formulated. The method includes a formalized description of a neural network data channel - a software tool for analysis, data processing and selection of data transfer protocol. The main stages of the proposed method are outlined: classification of the structures of the source data, their transformation, data processing, selection of the necessary protocol for transmitting information. Each of the stages is implemented through neural networks of various architectures. The theoretical rationale of the possibility of using the neural network method is given, obtained on the basis of the proof of a number of theorems. The novelty of the proposed method consists in the transition from an analytical solution of the problems of classification, processing and data transfer to an automated approach using machine learning technologies. The practical significance of the neural network method is to reduce the complexity of the implementation of information processing and transmission processes, to increase the level of automation in the organization of intermodular interaction. The implementation of the neural network method has been assessed using a number of software complexity assessment metrics. The application, virtues and failings of the developed method are analyzed.
Keywords data processing and transmission neural network method, neural network data channel, neural networks, adaptive information systems
UDC 004.89
MSC 68T01, 68T05
DOI 10.35634/vm210111
Received 20 July 2020
Language Russian
Citation Obukhov A.D., Krasnyansky M.N. Neural network method of data processing and transmission in adaptive information systems, Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki, 2021, vol. 31, issue 1, pp. 149-164.
References
  1. Keller A. Challenges and directions in service management automation, Journal of Network and Systems Management, 2017, vol. 25, issue 4, pp. 884-901. https://doi.org/10.1007/s10922-017-9437-9
  2. van Steen M., Tanenbaum A.S. A brief introduction to distributed systems, Computing, 2016, vol. 98, issue 10, pp. 967-1009. https://doi.org/10.1007/s00607-016-0508-7
  3. Zhang C., Wang X., Li F., Huang M. NNIRSS: neural network-based intelligent routing scheme for SDN, Neural Computing and Applications, 2019, vol. 31, issue 10, pp. 6189-6205. https://doi.org/10.1007/s00521-018-3427-z
  4. Genga L., Alizadeh M., Potena D., Diamantini C., Zannone N. Discovering anomalous frequent patterns from partially ordered event logs, Journal of Intelligent Information Systems, 2018, vol. 51, issue 2, pp. 257-300. https://doi.org/10.1007/s10844-018-0501-z
  5. Eremeev A.P., Kozhuhov A.A., Golenkov V.V, Gulyakina N.A. On the implementation of machine learning tools in real-time intelligent systems, Programmnye Produkty i Sistemy, 2018, no. 2, pp. 239-245 (in Russian). https://doi.org/10.15827/0236-235X.122.239-245
  6. Dong S., Li R. Traffic identification method based on multiple probabilistic neural network model, Neural Computing and Applications, 2019, vol. 31, issue 2, pp. 473-487. https://doi.org/10.1007/s00521-017-3081-x
  7. Kraus M., Feuerriegel S. Decision support from financial disclosures with deep neural networks and transfer learning, Decision Support Systems, 2017, vol. 104, pp. 38-48. https://doi.org/10.1016/j.dss.2017.10.001
  8. Weng Y.C., Hsieh S.L. Design and implementation of a web-based medical drawing management system, Journal of Intelligent Information Systems, 2017, vol. 49, issue 3, pp. 391-405. https://doi.org/10.1007/s10844-017-0452-9
  9. Zhang J., Ding G., Zou Y., Qin S., Fu J. Review of job shop scheduling research and its new perspectives under Industry 4.0, Journal of Intelligent Manufacturing, 2019, vol. 30, issue 4, pp. 1809-1830. https://doi.org/10.1007/s10845-017-1350-2
  10. Shokeen J., Rana C. Social recommender systems: techniques, domains, metrics, datasets and future scope, Journal of Intelligent Information Systems, 2020, vol. 54, issue 4, pp. 633-667. https://doi.org/10.1007/s10844-019-00578-5
  11. Wang T. Intelligent employment rate prediction model based on a neural computing framework and human-computer interaction platform, Neural Computing and Applications, 2020, vol. 32, issue 21, pp. 16413-16426. https://doi.org/10.1007/s00521-019-04019-w
  12. Dourish P. Algorithms and their others: Algorithmic culture in context, Big Data and Society, 2016, vol. 3, issue 2, pp. 1-11. https://doi.org/10.1177/2053951716665128
  13. Zamula A., Kavun S. Complex systems modeling with intelligent control elements, International Journal of Modeling, Simulation, and Scientific Computing, 2017, vol. 8, no. 1, pp. 1750009:1-1750009:19. https://doi.org/10.1142/S179396231750009X
  14. Lin H.W., Tegmark M., Rolnick D. Why does deep and cheap learning work so well?, Journal of Statistical Physics, 2017, vol. 168, issue 6, pp. 1223-1247. https://doi.org/10.1007/s10955-017-1836-5
  15. Zhou L., Pan S., Wang J., Vasilakos A.V. Machine learning on big data: Opportunities and challenges, Neurocomputing, 2017, vol. 237, pp. 350-361. https://doi.org/10.1016/j.neucom.2017.01.026
  16. Mirzaey M., Jamshidi M., Hojatpour Y. Applications of artificial neural networks in information system of management accounting, International Journal of Mechatronics, Electrical and Computer Technology, 2017, vol. 7, issue 25, pp. 3523-3530.
  17. Obukhov A.D., Krasnyanskiy M.N. Neural network architecture of information systems, Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki, 2019, vol. 29, issue 3, pp. 438-455 (in Russian). https://doi.org/10.20537/vm190312
  18. Cybenko G.V. Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems, 1989, vol. 2, issue 4, pp. 303-314. https://doi.org/10.1007/BF02551274
  19. Hecht-Nielsen R. Replicator neural networks for universal optimal source coding, Science, 1995, vol. 269, issue 5232, pp 1860-1863. https://doi.org/10.1126/science.269.5232.1860
  20. Kolmogorov A.N. On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition, Doklady Akademii Nauk SSSR, 1957, vol. 114, no. 5, pp. 953-956 (in Russian). http://mi.mathnet.ru/eng/dan22050
  21. Kuznetsov M.A., Surkov V.O. Analysis of complexity metrics of a software code for obfuscating transformations of an executable code, IOP Conference Series: Materials Science and Engineering, 2016, vol. 155, pp. 012008. https://doi.org/10.1088/1757-899X/155/1/012008
  22. Antinyan V., Staron M., Sandberg A. Evaluating code complexity triggers, use of complexity measures and the influence of code complexity on maintenance time, Empirical Software Engineering, 2017, vol. 22, issue 6, pp. 3057-3087. https://doi.org/10.1007/s10664-017-9508-2
  23. Tsvetkov V.Ya., Buravtsev A.V. Metrics of a complex determinate system, Ontology of Designing, 2017, vol. 7, issue 3, pp. 334-346 (in Russian). https://doi.org/10.18287/2223-9537-2017-7-3-334-346
  24. Savchenko D., Hynninen T., Taipal O. Code quality measurement: Case study, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), IEEE, 2018, pp. 1455-1459. https://doi.org/10.23919/mipro.2018.8400262
  25. Evdokimov I.V., Baikalov I.S., Zudenkov A.I., Radionov T.V., Tsiryul'nikova A.M. Considering the question of the metrics of the labority of development of mobile application, Fundamental Research, 2017, no. 9-1, pp. 54-58 (in Russian). http://fundamental-research.ru/ru/article/view?id=41703
  26. Lesik I.A. Forecasting stock price growth using feedforward neural networks, Software and Systems, 2015, no. 2, pp. 70-74 (in Russian). https://doi.org/10.15827/0236-235X.110.070-074
  27. Jin H., Song Q., Hu X. Auto-keras: An efficient neural architecture search system, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, Association for Computing Machinery, 2019, pp. 1946-1956. https://doi.org/10.1145/3292500.3330648
  28. Mendoza H., Klein A., Feurer M., Springenberg J.T., Urban M., Burkart M., Dippel M., Lindauer M., Hutter F. Towards automatically-tuned deep neural networks, Automated Machine Learning, 2019, pp. 135-149. https://doi.org/10.1007/978-3-030-05318-5_7
  29. Gong X., Chang S., Jiang Y., Wang Z. Autogan: Neural architecture search for generative adversarial networks, International Conference on Computer Vision (ICCV), IEEE, 2019, pp. 3223-3233. https://doi.org/10.1109/ICCV.2019.00332
  30. Pan W., Chai C. Measuring software stability based on complex networks in software, Cluster Computing, 2019, vol. 22, issue 2, pp. 2589-2598. https://doi.org/10.1007/s10586-017-1353-y
Full text
<< Previous article