Search Results for author: Andrei Velichko

Found 19 papers, 1 papers with code

Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment

no code implementations28 Aug 2023 Maksim Belyaev, Murugappan Murugappan, Andrei Velichko, Dmitry Korzun

We computed different types of entropy from EEG signals and found that Fuzzy Entropy performed the best in diagnosing and monitoring PD using rs-EEG.

EEG

Imagery Tracking of Sun Activity Using 2D Circular Kernel Time Series Transformation, Entropy Measures and Machine Learning Approaches

no code implementations14 Jun 2023 Irewola Aaron Oludehinwa, Andrei Velichko, Maksim Belyaev, Olasunkanmi I. Olusola

In this study, we developed a technique of tracking the sun's activity using 2D circular kernel time series transformation, statistical and entropy measures, with machine learning approaches.

Time Series

A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science

no code implementations3 Jun 2023 Andrei Velichko, Petr Boriskov, Maksim Belyaev, Vadim Putrolaynen

The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems.

Time Series

Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers

no code implementations22 Oct 2022 Mehmet Tahir Huyut, Andrei Velichko, Maksim Belyaev

Furthermore, in the HGB model operated with a single feature, the most efficient features were procalcitonin (F1^2 = 0. 96) and ferritin (F1^2 = 0. 91).

Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing

no code implementations13 Oct 2022 Andrei Velichko, Maksim Belyaev, Matthias P. Wagner, Alireza Taravat

The results of entropy approximation are demonstrated using the example of calculating the 2D entropy of Sentinel-2 images and R^2 metric evaluation.

regression Time Series +1

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

no code implementations8 Sep 2022 Andrei Velichko, Mehmet Tahir Huyut, Maksim Belyaev, Yuriy Izotov, Dmitry Korzun

In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL).

Edge-computing

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

no code implementations20 May 2022 Mehmet Tahir Huyut, Andrei Velichko

Since February 2020, the world has been engaged in an intense struggle with the COVID-19 dis-ease, and health systems have come under tragic pressure as the disease turned into a pandemic.

feature selection

A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare

no code implementations5 Aug 2021 Andrei Velichko

The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators.

Classification Edge-computing

A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks

no code implementations18 Jul 2021 Andrei Velichko, Hanif Heidari

Greater complexity in the time series leads to a higher classification accuracy and higher NNetEn values.

Time Series Time Series Analysis

An improved LogNNet classifier for IoT application

no code implementations30 May 2021 Hanif Heidari, Andrei Velichko

In the age of neural networks and Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda.

Time Series Time Series Analysis

Electrical switching and oscillations in vanadium dioxide

no code implementations7 Jan 2020 Alexander Pergament, Andrei Velichko, Maksim Belyaev, Vadim Putrolaynen

With a decrease in dimensions, a decrease in the thermal coupling action radius is observed, which can vary in the range from 0. 5 to 50 {\mu}m for structures with characteristic dimensions of 0. 1 to 5 {\mu}m, respectively.

Applied Physics Disordered Systems and Neural Networks

Switching dynamics of single and coupled VO2-based oscillators as elements of neural networks

no code implementations7 Jan 2020 Andrei Velichko, Maksim Belyaev, Vadim Putrolaynen, Alexander Pergament, Valentin Perminov

In the present paper, we report on the switching dynamics of both single and coupled VO2-based oscillators, with resistive and capacitive coupling, and explore the capability of their application in oscillatory neural networks.

Thermal coupling and effect of subharmonic synchronization in a system of two VO2 based oscillators

no code implementations6 Jan 2020 Andrei Velichko, Maksim Belyaev, Vadim Putrolaynen, Valentin Perminov, Alexander Pergament

In the case of a "weak" coupling, synchronization is accompanied by attraction effect and decrease of the main spectra harmonics width.

Total Energy

Oscillator Circuit for Spike Neural Network with Sigmoid Like Activation Function and Firing Rate Coding

no code implementations23 Nov 2019 Andrei Velichko, Petr Boriskov

The study presents an oscillator circuit for a spike neural network with the possibility of firing rate coding and sigmoid-like activation function.

Higher Order and Long-Range Synchronization Effects for Classification and Computing in Oscillator-Based Spiking Neural Networks

no code implementations10 Apr 2018 Andrei Velichko, Vadim Putrolaynen, Maksim Belyaev

In the circuit of two thermally coupled VO2 oscillators, we studied a higher order synchronization effect, which can be used in object classification techniques to increase the number of possible synchronous states of the oscillator system.

General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.