S. Dolenko

发表

A method for optical imaging and monitoring of the excretion of fluorescent nanocomposites from the body using artificial neural networks.

O. Shenderova, O. Sarmanova, S. Burikov, 2018, Nanomedicine : nanotechnology, biology, and medicine.

Raman Spectroscopy of Water-Ethanol Solutions: The Estimation of Hydrogen Bonding Energy and the Appearance of Clathrate-like Structures in Solutions.

S. Burikov, S. Dolenko, T. Dolenko, 2015, The journal of physical chemistry. A.

Improvement of reliability of molecular DNA computing: solution of inverse problem of Raman spectroscopy using artificial neural networks

O. Sarmanova, S. Burikov, S. Dolenko, 2017 .

Prediction of relativistic electron flux in the Earth’s outer radiation belt at geostationary orbit by adaptive methods

S. Dolenko, A. Efitorov, I. Myagkova, 2017, Geomagnetism and Aeronomy.

Chemically modified nanocrystalline SnO2-based materials for nitrogen-containing gases detection using gas sensor array

Alexander Efitorov, Marina Rumyantseva, Sergey Dolenko, 2017 .

Training with noise as a method to increase noise resilience of neural network solution of inverse problems

S. Dolenko, I. Isaev, 2016, Optical Memory and Neural Networks.

Neural Network Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts

Alexander Efitorov, Sergey Dolenko, Sergey Burikov, 2016 .

Use of Conditional Generative Variational Autoencoder Networks to Improve Representativity of Data in Optical Spectroscopy

S. Burikov, S. Dolenko, K. Laptinskiy, 2021, Proceedings of The 5th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2021).

Diagnostics of Water-Ethanol Solutions by Raman Spectra with Artificial Neural Networks: Methods to Improve Resilience of the Solution to Distortions of Spectra

S. Burikov, S. Dolenko, I. Isaev, 2019, Studies in Computational Intelligence.

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

S. Burikov, S. Dolenko, I. Isaev, 2018, Journal of Physics: Conference Series.

Using artificial neural networks for elaboration of fluorescence biosensors on the basis of nanoparticles

S. Burikov, S. Dolenko, K. Laptinskiy, 2014 .

Contribution of Fermi and Darling–Dennison resonances to the formation of Raman spectra of water and water–ethanol solutions

S. Burikov, S. Dolenko, T. Dolenko, 2017 .

Comparison of data integration methods for neural network solution of the inverse problem of exploration geophysics

E. Obornev, I. Obornev, E. Rodionov, 2022, 2022 VIII International Conference on Information Technology and Nanotechnology (ITNT).

Prediction of the Dst Geomagnetic Index Using Adaptive Methods

S. Dolenko, I. Myagkova, V. Shirokii, 2021, Russian Meteorology and Hydrology.

Short- and Medium-range Prediction of Relativistic Electron Flux in the Earth’s Outer Radiation Belt by Machine Learning Methods

Yu. S. Shugai, S. Dolenko, I. Myagkova, 2021, Russian Meteorology and Hydrology.

Using method integration transfer learning for neural network solution of an inverse problem in optical spectroscopy

O. Sarmanova, S. Burikov, S. Dolenko, 2022, Saratov Fall Meeting.

Monitoring, analysis and post-casting of the Earth’s particle radiation environment during February 14–March 5, 2014

V. Kalegaev, S. Dolenko, M. Panasyuk, 2019, Journal of Space Weather and Space Climate.

Taking into Account Mutual Correlations during Selection of Significant Input Features in Neural Network Solution of Inverse Problems of Spectroscopy

S. Burikov, S. Dolenko, I. Isaev, 2022, Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022).

Medium-term prediction of the fluence of relativistic electrons in geostationary orbit using solar wind streams forecast based on solar observations

V. Kalegaev, S. Dolenko, Y. Shugay, 2022, Advances in Space Research.

Medium-Term Prediction of Relativistic Electron Fluxes in a Geostationary Orbit Using Machine Learning Methods Based on Observations of Solar Coronal Holes

Yu. S. Shugai, V. Kalegaev, S. Dolenko, 2020, Geomagnetism and Aeronomy.

Prediction of Relativistic Electrons Flux in the Outer Radiation Belt of the Earth Using Adaptive Methods

S. Dolenko, A. Efitorov, I. Myagkova, 2016, BICA 2016.

Adaptive Neuro-Fuzzy Inference System Used to Classify the Measurements of Chemical Sensors

Alexander Efitorov, Sergey Dolenko, S. Dolenko, 2018, Biologically Inspired Cognitive Architectures 2018.

A New Type of a Wavelet Neural Network

A. Efitorov, S. Dolenko, S. Dolenko, 2018, Optical Memory and Neural Networks.

Raman spectroscopy of micellization-induced liquid–liquid fluctuations in sodium dodecyl sulfate aqueous solutions

S. Burikov, S. Dolenko, T. Dolenko, 2015 .

Manifestation of Fermi resonance in Raman spectra of micellar aqueous solutions of sodium octanoate

T. A. Dolenko, I. V. Plastinin, S. A. Burikov, 2020, Saratov Fall Meeting.

The Role of Fermi and Darling–Dennison Resonances in the Formation of the Raman Spectra of Water and Water–Ethanol Solutions

S. Burikov, S. Dolenko, T. Dolenko, 2019, Bulletin of the Russian Academy of Sciences: Physics.

Neural Network Solution of Inverse Problems of Geological Prospecting with Discrete Output

E. Obornev, I. Obornev, E. Rodionov, 2021, Proceedings of The 5th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2021).

Using Domain Knowledge for Feature Selection in Neural Network Solution of the Inverse Problem of Magnetotelluric Sounding

Sergey Dolenko, Igor Isaev, E. A. Obornev, 2020 .

Neural Network Recognition of the Type of Parameterization Scheme for Magnetotelluric Data

Sergey Dolenko, Igor Isaev, E. A. Obornev, 2018, Advances in Neural Computation, Machine Learning, and Cognitive Research II.

Adding Noise During Training as a Method to Increase Resilience of Neural Network Solution of Inverse Problems: Test on the Data of Magnetotelluric Sounding Problem

Sergey Dolenko, Igor Isaev, S. Dolenko, 2017 .

Increase of the Resistance to Noise in Data for Neural Network Solution of the Inverse Problem of Magnetotellurics with Group Determination of Parameters

Igor Isaev, S. A. Dolenko, Eugeny Obornev, 2016, ICANN.

Comparative Analysis of Residual Minimization and Artificial Neural Networks as Methods of Solving Inverse Problems: Test on Model Data

Sergey Dolenko, Igor Isaev, S. Dolenko, 2016, BICA 2016.

Study of Influence of Parameter Grouping on the Error of Neural Network Solution of the Inverse Problem of Electrical Prospecting

I. G. Persiantsev, Igor Isaev, S. A. Dolenko, 2013, EANN.

Optical imaging of fluorescent carbon biomarkers using artificial neural networks

Jessica M Rosenholm, Tatiana A Dolenko, Sergey A Burikov, 2014, Journal of biomedical optics.

Using Artificial Neural Networks for Optical Imaging of Fluorescent Biomarkers

Sergey Dolenko, Sergey Burikov, K. A. Laptinskiy, 2014 .

Application of Wavelet Neural Networks for Monitoring of Extraction of Carbon Multi-Functional Medical Nano-Agents from the Body

Alexander Efitorov, Sergey Dolenko, Olga Sarmanova, 2018, BICA.

Neural Network Classification Method for Solution of the Problem of Monitoring Theremoval of the Theranostics Nanocomposites from an Organism

Olga Sarmanova, Sergey Burikov, Sergey Dolenko, 2017, BICA 2017.

Selective detection of individual gases and CO/H 2 mixture at low concentrations in air by single semiconductor metal oxide sensors working in dynamic temperature mode

M. Rumyantseva, A. Gaskov, S. Dolenko, 2018 .

Monitoring of the excretion of fluorescent nanocomposites out of the body using artificial neural networks

K. A. Laptinskiy, T. A. Dolenko, S. A. Burikov, 2018, Photonics Europe.

Development of the algorithm of adaptive construction of hierarchical neural network classifiers

S. A. Dolenko, V. A. Svetlov, S. Dolenko, 2017, Optical Memory and Neural Networks.

Joint application of Raman and optical absorption spectroscopy to determine concentrations of heavy metal ions in water using artificial neural networks

Sergey Dolenko, Olga Sarmanova, Sergey Burikov, 2020, Saratov Fall Meeting.

Feature Selection in Neural Network Solution of Inverse Problem Based on Integration of Optical Spectroscopic Methods

Sergey Dolenko, Olga Sarmanova, Igor Isaev, 2020 .

Use of adaptive window wavelet neural networks to solve inverse problems of spectroscopy

Alexander O. Efitorov, Sergey A. Dolenko, S. Dolenko, 2021, Saratov Fall Meeting.

Monitoring of nanodiamonds in human urine using artificial neural networks

O. Shenderova, O. Sarmanova, S. Burikov, 2016 .

A Neural Network of Multiresolution Wavelet Analysis

Alexander Efitorov, Vladimir Shiroky, S. A. Dolenko, 2018, ICANN.

Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts by Artificial Neural Networks

Alexander Efitorov, Sergey Burikov, Tatiana A. Dolenko, 2016, ICANN.

Machine learning methods solving an inverse problem in spectroscopy: comparison of efficiency and noise resilience

Tatiana A. Dolenko, Kirill A. Laptinskiy, Sergey A. Burikov, 2021, Saratov Fall Meeting.

The Prediction of the Dst-Index Based on Machine Learning Methods

Alexander Efitorov, Sergey Dolenko, Irina N. Myagkova, 2018, Cosmic Research.

Development of the fluorescent carbon nanosensor for pH and temperature of liquid media with artificial neural networks.

O. Sarmanova, S. Burikov, S. Dolenko, 2021, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

Multi-stage Algorithm Based on Neural Network Committee for Prediction and Search for Precursors in Multi-dimensional Time Series

I. G. Persiantsev, S. A. Dolenko, Ju. S. Shugai, 2009, ICANN.

Use of Wavelet Neural Networks to Solve Inverse Problems in Spectroscopy of Multi-component Solutions

Alexander Efitorov, Sergey Dolenko, Sergey Burikov, 2019, Studies in Computational Intelligence.

Decomposition of water Raman stretching band with a combination of optimization methods

S. Burikov, S. Dolenko, T. Dolenko, 2010 .

New opportunity solutions to inverse problems in laser spectroscopy involving artificial neural networks

I. G. Persiantsev, Sergey Dolenko, Tatiana A. Dolenko, 2002 .

Multifold Acceleration of Neural Network Computations Using GPU

I. G. Persiantsev, S. A. Dolenko, Alexander Guzhva, 2009, ICANN.

Neural Network Prediction of Daily Relativistic Electrons Fluence in the Outer Radiation Belt of the Earth: Selection of Delay Embedding Method

Sergey Dolenko, Irina N. Myagkova, Roman Batusov, 2017, BICA.

The Use of Coupling Functions in the Forecasting of the Dst-Index Amplitude with Adaptive Methods

V. Kalegaev, S. Dolenko, I. Myagkova, 2021, Geomagnetism and Aeronomy.

The Solution to the Problem of Classifying High-Dimension fMRI Data Based on the Spark Platform

Alexander Efitorov, Vladimir Shirokii, Vyacheslav Orlov, 2020 .

Determination of Gas Temperature in a CVD Reactor from Optical Emmission Spectra with the Help of Artificial Neural Networks and Group Method of Data Handling (GMDH)

A. V. Filippov, S. Dolenko, A. Serov, 1998 .

Time-resolved fluorimetry of two-fluorophore organic systems using artificial neural networks

S. Dolenko, T. Dolenko, M. Kompitsas, 2002 .

Kohonen Self-Organizing Maps as a New Method for Determination of Salt Composition of Multi-Component Solutions

Sergey Dolenko, Sergey Burikov, Tatiana A. Dolenko, 2014 .

Comparison of Input Data Compression Methods in Neural Network Solution of Inverse Problem in Laser Raman Spectroscopy of Natural Waters

I. G. Persiantsev, Sergey Burikov, Tatiana A. Dolenko, 2012, ICANN.

Comparison of nonlinear methods of motion correction in fMRI data

Alexander Efitorov, Vyacheslav Orlov, Vadim Ushakov, 2018, BICA.

Prediction of geomagnetic indexes with the help of artificial neural networks

S. Dolenko, I. Myagkova, V. Shiroky, 2017 .

Analysis of the Earth's magnetosphere states using the algorithm of adaptive construction of hierarchical neural network classifiers

S. Dolenko, I. Isaev, I. Myagkova, 2017 .

Effect of Simultaneous Time Series Prediction with Various Horizons on Prediction Quality at the Example of Electron Flux in the Outer Radiation Belt of the Earth

Vladimir Shiroky, Irina Myagkova, S. A. Dolenko, 2016, ICANN.

Horizon of Neural Network Prediction of Relativistic Electrons Flux in the Outer Radiation Belt of the Earth

I. G. Persiantsev, Vladimir Shiroky, Irina Myagkova, 2015, EANN '15.

Comparative Efficiency of Prediction of Relativistic Electron Flux in the Near-Earth Space Using Various Machine Learning Methods

S. Dolenko, I. Myagkova, V. Shirokii, 2020 .