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ЖУРНАЛЫ // Компьютерная оптика

Компьютерная оптика, 2017, том 41, выпуск 5, страницы 765–774 (Mi co447)

Оценка надежности верификации автографа на основе искусственных нейронных сетей, сетей многомерных функционалов Байеса и сетей квадратичных форм
А. И. Иванов, П. С. Ложников, А. Е. Сулавко

Эта публикация цитируется в следующих статьяx:
  1. Zhong Chi, Tianxiao Yang, Raghavan Dhanasekaran, “Teaching Practice of College Students' Marketing Course Based on the Background of the Internet Era”, International Transactions on Electrical Energy Systems, 2022 (2022), 1  crossref
  2. Wei Wang, Xiaowei Chen, Tapan Senapati, “Content System of Physical Fitness Training for Track and Field Athletes and Evaluation Criteria of Some Indicators Based on Artificial Neural Network”, Discrete Dynamics in Nature and Society, 2022 (2022), 1  crossref
  3. Limei Deng, Ying Chang, Jun Ye, “Risk Management of Investment Projects Based on Artificial Neural Network”, Wireless Communications and Mobile Computing, 2022 (2022), 1  crossref
  4. Yanjun Chen, Sikang Zhang, Kalidoss Rajakani, “Accounting Information Disclosure and Financial Crisis Beforehand Warning Based on the Artificial Neural Network”, Wireless Communications and Mobile Computing, 2022 (2022), 1  crossref
  5. Shuai Wang, Xia Zhao, Rahim Khan, “Influence of Different Passing Methods of Physical Fitness in Football Using Deep Learning”, Computational Intelligence and Neuroscience, 2022 (2022), 1  crossref
  6. S. S. Zhumazhanova, I. D. Tatarinov, 2021 Dynamics of Systems, Mechanisms and Machines (Dynamics), 2021, 1  crossref
  7. А. Е. Сулавко, “Высоконадёжная двухфакторная биометрическая аутентификация по рукописным и голосовым паролям на основе гибких нейронных сетей”, Компьютерная оптика, 44:1 (2020), 82–91  mathnet  crossref [A. E. Sulavko, “Highly reliable two-factor biometric authentication based on handwritten and voice passwords using flexible neural networks”, Computer Optics, 44:1 (2020), 82–91  mathnet]
  8. E. T. Zainulina, I. A. Matveev, “Binding Cryptographic Keys into Biometric Data: Optimization”, J. Comput. Syst. Sci. Int., 59:5 (2020), 699  crossref
  9. T.A. Zolotareva, A.I. Ivanov, O.V. Selishchev, D.M. Skudnev, V. Breskich, A. Zheltenkov, Y. Dreizis, “Regularization of automatic training of Mahalanobis neurons for small samples of examples of the “Own” image”, E3S Web Conf., 224 (2020), 01024  crossref
  10. Aleksandr Ivanov, Tatyana Zolotareva, Advances in Intelligent Systems and Computing, 1294, Software Engineering Perspectives in Intelligent Systems, 2020, 829  crossref
  11. А. Е. Сулавко, “Абстрактная модель искусственной иммунной сети на основе комитета классификаторов и её использование для распознавания образов клавиатурного почерка”, Компьютерная оптика, 44:5 (2020), 830–842  mathnet  crossref [A. E. Sulavko, “An abstract model of an artificial immune network based on a classifier committee for biometric pattern recognition by the example of keystroke dynamics”, Computer Optics, 44:5 (2020), 830–842  mathnet]
  12. A E Sulavko, A E Samotuga, D G Stadnikov, V A Pasenchuk, S S Zhumazhanova, “Biometric authentication on the basis of lectroencephalograms parameters”, J. Phys.: Conf. Ser., 1260:2 (2019), 022011  crossref
  13. Nikolay Abramov, Alexander Talalaev, Vitaly Fralenko, Oleg Shishkin, Vyacheslav Khachumov, Proceedings of the V International conference Information Technology and Nanotechnology 2019, 2019, 180  crossref
  14. A A Nevzorov, A A Orlov, D A Stankevich, “Detection of quasi-harmonic signals with a priori unknown parameters in strong additive noise by machine learning methods”, J. Phys.: Conf. Ser., 1368:5 (2019), 052014  crossref
  15. К. С. Сарин, И. А. Ходашинский, “Метод баггинга и отбор признаков в построении нечётких классификаторов для распознавания рукописной подписи”, Компьютерная оптика, 43:5 (2019), 833–845  mathnet  crossref [K. S. Sarin, I. A. Hodashinsky, “Bagged ensemble of fuzzy classifiers and feature selection for handwritten signature verification”, Computer Optics, 43:5 (2019), 833–845  mathnet]
  16. P S Lozhnikov, A E Sulavko, “Generation of a biometrically activated digital signature based on hybrid neural network algorithms”, J. Phys.: Conf. Ser., 1050 (2018), 012047  crossref
  17. A. E. Sulavko, S. S. Zhumazhanova, G. A. Fofanov, 2018 Dynamics of Systems, Mechanisms and Machines (Dynamics), 2018, 1  crossref
  18. Vladimir I. Vasilyev, Pavel S. Lozhnikov, Alexey E. Sulavko, Grigory A. Fofanov, Samal S. Zhumazhanova S., “Flexible fast learning neural networks and their application for building highly reliable biometric cryptosystems based on dynamic features”, IFAC-PapersOnLine, 51:30 (2018), 527  crossref
  19. A E Sulavko, S S Zhumazhanova, “Biometric pattern recognition using wide networks of gravity proximity measures”, J. Phys.: Conf. Ser., 1050 (2018), 012082  crossref
  20. I V Isaev, S A Burikov, T A Dolenko, K A Laptinskiy, S A Dolenko, “Improving the resilience of neural network solution of inverse problems in Raman spectroscopy of multi-component solutions of inorganic compounds to the distortions caused by frequency shift of the spectral channels”, J. Phys.: Conf. Ser., 1096 (2018), 012100  crossref


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