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Methods, algorithms, systems, high-performance intelligent processing technologies

Fundamental and applied principles of building methods, algorithms, systems, technologies of high-performance intellectual processing, analysis and visualization of extra-large amounts of data with applications to the tasks of industrial analytics, biomedicine, social media analysis, etc., including the use of virtual and augmented reality technologies.

 

  • Projects

    The project "Creation of a domestic high-tech software and instrumental complex for the implementation of process control systems based on free software" with JSC "EleSi" by decree of the Government of the Russian Federation No. 218.
     
    The project "Scientific and methodological foundations of the construction of software and hardware systems of multidimensional visualization for solving problems of monitoring and controlling of infrastructure facilities" (state task "Science" No. 2.4218.2017 / ПЧ).

    16.01.2017 — 31.12.2019 Interactive multi-dimensional visualization environment for solving problems of monitoring and management of infrastructure facilities //state assignment of the Ministry of education and science of Russia
     
    30.05.2018 — 31.12.2019 Information and software package for early diagnosis of diseases using technologies of intellectual analysis and data storage State support of the leading universities of  Russian Federation in order to improve their competitive ability among the world's leading scientific and educational centers (5-100)
     
    30.05.2018 – 31.12.2019
    Adaptive machine learning algorithms with controlled accuracy in the controlling of technological processes State support of the leading universities of  Russian Federation in order to improve  their competitiveness among the world's leading research and educational centers (5-100)

    Key publications

  • Gavrin, Damir Murzagulov, Alexander Zamyatin. Detection of Change Point in Process Signals by Cascade Classification. // IEEE International Russian Automation Conference 2018]. DOI: 10.1109/RUSAUTOCON.2018.8501677. ISBN: 978-1-5386-4938-1
  • Murzagulov, Alexander V. Zyamatin, Pavel M. Ostrast. Аpproach to Detection of Anomalies of Process Signals Using Classification and Wavelet Transforms. // IEEE International Russian Automation Conference 2018 ]. DOI: 10.1109/RUSAUTOCON.2018.8501786. ISBN: 978-1-5386-4938-1
  • Wajdi Alghamdi, Daniel Stamate, Daniel Stahl, Alexander Zamyatin, Robin Murray, Marta Di Forti. A New Machine Learning Framework for Understanding the Link Between Cannabis Use and First-Episode Psychosis // Health Informatics Meets ehealth. G. Schreier and D. Hayn (Eds.). P. 9 - 16, DOI: 10.3233/978-1-61499-858-7-9        •
  • Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Alexander Zamyatin, Robin Murray and Marta di Forti. Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use? // IFIP International Federation for Information Processing 2018. Published by Springer International Publishing AG 2018. All Rights Reserved: AIAI 2018, IFIP AICT 519, pp. 311–322, 2018. Https://doi.org/10.1007/978-3-319-92007-8_27        
  • Daniel Stamate, Alexander Zamyatin etc. PIDT: A Novel Decision Tree Algorithm Based on Parameterised Impurities and Statistical Pruning Approaches // IFIP International Federation for Information Processing 2018. Published by Springer International Publishing AG 2018. L. Iliadis et al. (Eds.): AIAI 2018, IFIP AICT 519, pp. 273–284, 2018. Https://doi.org/10.1007/978-3-319-92007-8_24
  • • S. V. Andryushchenko, A. S. Uglov, A. V. Zamyatin. Statistical classification of immunosignature for the problems of early diagnosis of diseases with a significant reduction in the dimension of the attribute space Sovremennye tehnologii v medicine 2018; 10(3): 14-19,https://doi.org/10.17691/stm2018.10.1.0
  •  Aksenov S.V., Kostin K.A., Ivanova A.V., Liang J., Zamyatin A.V. An ensemble of convolutional neural networks for the use in video endoscopy. Sovremennye tehnologii v medicine 2018; 10(2): 7–19, https://doi.org/10.17691/stm2018.10.2.01
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