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  1. Conference contribution › Research › Peer-reviewed

  2. Using PCA Machine Learning Approach Based on Psychological Questionnaires and Spectral Characteristics of the EEG to Separate the Healthy Participants and Participants with Major Depressive Disorder

    Merkulova, E. A., Kozulin, I. A., Savostyanov, A. N., Bocharov, A. V. & Privodnova, E. Y., 2023, 24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023. Institute of Electrical and Electronics Engineers (IEEE), p. 1740-1745 6 p.

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  3. Utilizing Lexicographic Resources for Sentiment Classification in Uzbek Language

    Mengliev, D. B., Akhmedov, E. Y., Barakhnin, V. B., Hakimov, Z. A. & Alloyorov, O. M., 2023, Proceedings of the 2023 IEEE 16th International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023. Institute of Electrical and Electronics Engineers (IEEE), p. 1720-1724 5 p.

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  4. Verification of industrial control algorithms in virtual laboratory stands

    Liakh, T. & Zyubin, V., 2017, 29th European Modeling and Simulation Symposium, EMSS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017. CAL-TEK S.r.l., p. 380-384 5 p.

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  5. Abstract › Research › Peer-reviewed

  6. A system for remote recognition of emotions from a facial expression //Cognitive Sciences, Genomics and Bioinformatics (CSGB-2018)

    Хазанкин, Г. Р., Малинин, А. Н. & Шмаков, И. С., 20 Aug 2018.

    Research output: Contribution to conference › Abstract › peer-review

  7. The Siberian multimodal brain tumor image segmentation dataset

    Голушко, С. К., Амелина, Е. В., Groza, V., Амелин, М. Е., Толстокулаков, Н. Ю., Тучинов, Б. Н. & Pavlovskiy, E., 2020.

    Research output: Contribution to conference › Abstract › peer-review

  8. Other contribution › Research

  9. Reducing over-smoothness in speech synthesis using Generative Adversarial Networks

    Pavlovskiy, E. N. & Шэн, Л., 2018

    Research output: Other contribution › Research

  10. Other contribution › Education

  11. Сетевые технологии : Видеокурс

    Bachilo, D. A. & Хазанкин, Г. Р., 2020, ИПЦ НГУ.

    Research output: Other contribution › Education

  12. Teaching manual › Education › Peer-reviewed

  13. Информационные системы: модели и технологии

    Федотов, А. М., Федотова, О. А. & Самбетбаева, М., 2019, Новосибирск: ИПЦ НГУ. 264 p.

    Research output: Book/Report › Teaching manual › peer-review

  14. Working paper › Research

  15. Superposition as Data Augmentation using LSTM and HMM in Small Training Sets

    Павловский, Е. Н. & Сивасвами, А., 24 Oct 2019, Daejeon, South Korea: Cornell University, 5 p.

    Research output: Working paper

  16. Conference article › Research › Peer-reviewed

  17. Conceptual methods for identifying needs of mobile network subscribers

    Palchunov, D., Yakhyaeva, G. & Dolgusheva, E., 2016, In: CEUR Workshop Proceedings. 1624, p. 147-160 14 p.

    Research output: Contribution to journal › Conference article › peer-review

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