Open Access
MATEC Web Conf.
Volume 341, 2021
The VII International Scientific and Practical Conference “Information Technologies and Management of Transport Systems” (ITMTS 2021)
Article Number 00027
Number of page(s) 8
Published online 21 July 2021
  1. Fault tree analysis, URL: [Google Scholar]
  2. I. A. Ryabinin, A. V. Strukov, Quantitative examples of safety assessment using logical-probabilistic methods, International Journal of Risk Assessment and Management (IJRAM), 21(1/2), doi: 10.1504/IJRAM.2018.090253 (2018) [Google Scholar]
  3. I. Makarova K. Shubenkova E. Mukhametdinov I. Giniyatullin Selection of the Method to Predict Vehicle Operation Reliability, Lecture Notes in Networks and Systems, 117, pp. 316-328 (2020) [Google Scholar]
  4. I. Makarova E. Mukhametdinov V. Mavrin Unified information environment role to improve the vehicle reliability at life cycle stages during the transition to industry 4.0, Proceedings - International Conference on Developments in eSystems Engineering, DeSEVolume October-2019, pp. 800-805 (2019) [Google Scholar]
  5. R.G. Khabibullin, I.V. Makarova, E.I. Belyaev, I.F. Suleimanov, S.S. Pernebekov, U.A., Ussipbayev, A.S., Junusbekov, Z.A. Balabekov, The study and management of reliability parameters for automotive equipment using simulation modeling, Life Science Journal, 10 (12), pp. 828-831 (2013) [Google Scholar]
  6. I. Makarova R. Khabibullin A. Belyaev E. Belyaev Dealer-service center competitiveness increase using modern management methods, Transport Problems, 7(2), pp. 53-59 (2012) [Google Scholar]
  7. O.P. Yadav, N. Singh et al., A Framework for Reliability Prediction During Product Development Process Incorporating Engineering Judgments, Quality Engineering, 15(4), pp. 649-662 (2003) [Google Scholar]
  8. I. Makarova K. Shubenkova P. Buyvol V. Shepelev A. Gritsenko The Role of Reverse Logistics in the Transition to a Circular Economy: Case Study of Automotive Spare Parts Logistics, FME Transactions, 49(1), pp. 173-185 (2021) [Google Scholar]
  9. A. Gritsenko V. Shepelev E. Zadorozhnaya K. Shubenkova Test diagnostics of engine systems in passenger cars, FME Transactions, 48 (1), pp. 46-52 (2020) [Google Scholar]
  10. A. V. Gritsenko, E. A. Zadorozhnaya, V. D. Shepelev, Diagnostics of friction bearings by oil pressure parameters during cycle-by-cycle loading, Tribology in Industry, 40(2), pp. 300-310 (2018) [Google Scholar]
  11. A. Gritsenko V. Shepelev E. Zadorozhnaya Z. Almetova A. Burzev The advancement of the methods of vibro-acoustic control of the ICE gas distribution mechanism, FME Transactions, 48 (1), pp. 127-136 (2020) [Google Scholar]
  12. I. Makarova E. Mukhametdinov V. Mavrin K. Shubenkova Improvement of the Vehicle Clutch’s Diagnosing System with the Use of Vibrodiagnostics, 2018 IEEE International Conference on Technology Management, Operations and Decisions, pp. 101-106 (2018) [Google Scholar]
  13. A. Ragab M. Koujok H. Ghezzaz, M. Amazouz, M.-S. Ouali S.Yacout, Deep understanding in industrial processes by complementing human expertise with interpretable patterns of machine learning, Expert Systems with Applications, 122, pp. 388-405 (2019) [Google Scholar]
  14. M. Drakaki Y.L. Karnavas, P. Tzionas I.D. Chasiotis, Recent Developments Towards Industry 4.0 Oriented Predictive Maintenance in Induction Motors, Procedia Computer Science, 180, pp. 943-949 (2021) [Google Scholar]
  15. E. Alpaydin Introduction to Machine Learning, MIT Press, 712 p. (2020) [Google Scholar]
  16. J. Dongre G. L. Prajapati, S. V. Tokekar, The role of Apriori algorithm for finding the association rules in Data mining, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, pp. 657-660, doi: 10.1109/ICICICT.2014.6781357 (2014) [Google Scholar]
  17. S. Luma-Osmani, F. Ismaili X. Zenuni B. Raufi A Systematic Literature Review in Causal Association Rules Mining, 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0048-0054, doi: 10.1109/IEMCON51383.2020.9284908 (2020) [Google Scholar]
  18. I. Makarova G. Yakupova P. Buyvol E. Mukhametdinov A. Pashkevich Association rules to identify factors affecting risk and severity of road accidents, VEHITS 2020 - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems, pp. 614-621 (2020) [Google Scholar]
  19. An Introduction to Association Rule Analysis, URL: [Google Scholar]
  20. L. Zhang W. Wang Y. Zhang Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics, IEEE Access, 7, pp. 45032-45047, doi: 10.1109/ACCESS.2019.2908452 (2019) [Google Scholar]
  21. Program code of Science Adviser, URL: [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.