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Neural Network System for Selection of Table Tennis Equipment with Elements of Crypto Protection

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Information Technology for Education, Science, and Technics (ITEST 2024)

Abstract

The purpose of the research is to create a system for selecting table tennis equipment. Such a system would help players and coaches to select the optimal combination of rubbers and blade and contain elements of crypto-protection to ensure the security of personal data. To organize the cryptoprotection of the neural network system, an improved method of increasing the speed of implementation of the group matrix cryptotransformation is used. A method of increasing the stability of pseudorandom sequences built on the basis of matrix cryptographic transformation operations was developed by adding them modulo, which ensured an increase in the probability of degenerate transformation results. The use of this method made it possible to reduce the mathematical complexity and time of the cryptographic transformation due to the reduction of the complexity of construction and the use of the inverse transformation. The synthesis of pseudo-random sequences based on the application of matrix cryptographic transformation operations by adding them modulo and statistical analysis of the degeneracy of the transformation results was carried out.

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References

  1. The website of the Table Tennis Federation of Ukraine, https://www.uttf.com.ua/, last accessed 2024/04/20

  2. Table Tennis Equipment Reviews, https://revspin.net/, last accessed 2024/04/20

  3. The Database of Table Tennis Blades Compositions, https://stervinou.net/ttbdb/index.php/, last accessed 2024/04/20

  4. Tazetdinov, V.A., Sysoienko, S.V.: Neural network system for selection of table tennis equipment. Visnyk Cherkaskogo Derzhavnogo Tekhnolohichnogo Universytetu 1, 79–85 (2021). https://doi.org/10.24025/2306-4412.1.2021.225999

    Article  Google Scholar 

  5. Tilp, M.: Artificial neural networks. In: Memmert, D. (ed.) Computer Science in Sport. Springer, Berlin, Heidelberg (2024). https://doi.org/10.1007/978-3-662-68313-2_20

  6. Raabe, D.: Deep neural networks. In: Memmert, D. (ed.) Computer Science in Sport. Springer, Berlin, Heidelberg (2024). https://doi.org/10.1007/978-3-662-68313-2_21

  7. Huang, W., Lu, M., Zeng, Y., et al.: Technical and tactical diagnosis model of table tennis matches based on BP neural network. BMC Sports Sci. Med. Rehabil. 13, 54 (2021). https://doi.org/10.1186/s13102-021-00283-3

    Article  Google Scholar 

  8. Sarker, I.H.: Deep cybersecurity: A comprehensive overview from neural network and deep learning perspective. SN COMPUT. SCI. 2, 154 (2021). https://doi.org/10.1007/s42979-021-00535-6

    Article  Google Scholar 

  9. Tazetdinov, V., Sysoienko, S., Khrulov, M.: Self-organization of the table tennis market information bank based on neural networks. In: Faure, E., Danchenko, O., Bondarenko, M., Tryus, Y., Bazilo, C., Zaspa, G. (eds.) Information Technology for Education, Science, and Technics. ITEST 2022. Lecture Notes on Data Engineering and Communications Technologies, vol. 178. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35467-0_11

  10. Tazetdinov, V.A.: Automation of the process of selection of equipment for table tennis using neural network systems. Komputerno-Integrovani Tekhnologii: Osvita, Nauka, Vyrobnyztvo: Sci J. 32, 81–84 (2018)

    Google Scholar 

  11. Babenko, V.G., Melnyk, O.G., Stabetska, T.A.: Construction of non-linear operations of extended matrix cryptographic transformation. In: Cryptographic coding: a collective monograph, 41–55. Shchedraya Usadba Plus LLC Publishing House, Kharkiv (2014)

    Google Scholar 

  12. Faure, E., Shcherba, A., Lavdanskyi, A., Makhynko, M., Khizirova, M.: Three-pass protocol on permutations: Implementation example and security. CEUR Workshop Proceedings 3654, 110–125 (2024)

    Google Scholar 

  13. Babenko, V., Myroniuk, T., Lavdanskyi, A., Tarasenko, Y., Myroniuk, O.: Information-driven permutation operations for cryptographic transformation. CEUR Workshop Proceedings 3654, 137–149 (2024).

    Google Scholar 

  14. Lavdansky, A.A., Faure, E.V.: Estimation of statistical properties of sequences at the output of a combinational generator with the help of graphic tests. System studies and information technologies 2, 39–50 (2015)

    Google Scholar 

  15. Faure, E.V., Shcherba, A.I., Lavdansky, A.A.: Estimation of statistical characteristics of a sequence of pseudo-random numbers generated by a combinational generator. In: Computer-integrated technologies: Education, science, production 18: 165–171 (2015)

    Google Scholar 

  16. Rudnytskyi, V.M., Babenko, V.G., Rudnytskyi, S.V.: The method of synthesis of matrix models of operations of cryptographic coding and decoding of information. Collection of scientific works of the Kharkiv Air Force University 4(33), 198–200 (2012)

    Google Scholar 

  17. Hegadi, A., Patil, A.: Statistical analysis on in-built pseudo random number generators using NIST Test Suite, In: 5th International Conference on Computing, Communication and Security (2020). https://doi.org/10.1109/ICCCS49678.2020.9276849

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Correspondence to Valeriy Tazetdinov .

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Tazetdinov, V., Sysoienko, S., Tazetdinov, O., Al-Azzeh, J., Mesleh, A. (2024). Neural Network System for Selection of Table Tennis Equipment with Elements of Crypto Protection. In: Faure, E., et al. Information Technology for Education, Science, and Technics. ITEST 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 221. Springer, Cham. https://doi.org/10.1007/978-3-031-71801-4_10

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  • DOI: https://doi.org/10.1007/978-3-031-71801-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-71800-7

  • Online ISBN: 978-3-031-71801-4

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