49 citations · 472 across the 25 of their papers we have counts for
25 papers
Cross-Sectional Separability versus Longitudinal Response in Short-Record Parkinsonian Gait Analysis Using FEG-Pro: Nordic Walking and Adapted Physical Activity
Xuanbao Xiang, Andrei Velichko, Xiaobo Rao +1
\textbf{Objective:} Machine-learning separation of rehabilitation cohorts does not inherently establish a differential intervention response. This study used Forecast-Error Growth…
A Novel Approach for Estimating Largest Lyapunov Exponents in One-Dimensional Chaotic Time Series Using Machine Learning
A. Velichko, M. Belyaev, P. Boriskov
Understanding and quantifying chaos from data remains challenging. We present a data-driven method for estimating the largest Lyapunov exponent (LLE) from one-dimensional chaotic t…
Objective Features Extracted from Motor Activity Time Series for Food Addiction Analysis Using Machine Learning -- A Pilot Study
Mikhail Borisenkov, Maksim Belyaev, Nithya Rekha Sivakumar +6
Wearable sensors and IoT/IoMT platforms enable continuous, real-time monitoring, but objective digital markers for eating disorders are limited. In this study, we examined whether…
Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment
Maksim Belyaev, Murugappan Murugappan, Andrei Velichko +1
The study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in an Internet of Things (IoT) envi…
Solar Active Regions Detection Via 2D Circular Kernel Time Series Transformation, Entropy and Machine Learning Approach
Irewola Aaron Oludehinwa, Andrei Velichko, Maksim Belyaev +1
This study proposes an enhancement to the existing method for detecting Solar Active Regions (ARs). Our technique tracks ARs using images from the Atmospheric Imaging Assembly (AIA…
A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science
Andrei Velichko, Petr Boriskov, Maksim Belyaev +1
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. After training, th…
Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation
Andrei Velichko, Maksim Belyaev, Yuriy Izotov +2
Entropy measures are effective features for time series classification problems. Traditional entropy measures, such as Shannon entropy, use probability distribution function. Howev…
Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers
Mehmet Tahir Huyut, Andrei Velichko, Maksim Belyaev
Early evaluation of patients who require special care and who have high death-expectancy in COVID-19, and the effective determination of relevant biomarkers on large sample-groups…
Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing
Andrei Velichko, Maksim Belyaev, Matthias P. Wagner +1
Approximation of entropies of various types using machine learning (ML) regression methods are shown for the first time. The ML models presented in this study define the complexity…
Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application
Andrei Velichko, Mehmet Tahir Huyut, Maksim Belyaev +2
Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet o…
Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network
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 p…
Novel techniques for improving NNetEn entropy calculation for short and noisy time series
Hanif Heidari, Andrei Velichko, Murugappan Murugappan +1
Entropy is a fundamental concept in the field of information theory. During measurement, conventional entropy measures are susceptible to length and amplitude changes in time serie…
A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare
Andrei Velichko
Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource i…
A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks
Andrei Velichko, Hanif Heidari
Measuring the predictability and complexity of time series using entropy is essential tool de-signing and controlling a nonlinear system. However, the existing methods have some dr…
An improved LogNNet classifier for IoT application
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 m…
Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network
Y. A. Izotov, A. A. Velichko, A. A. Ivshin +1
The presented compact algorithm for recognizing handwritten digits of the MNIST database, created on the LogNNet reservoir neural network, reaches the recognition accuracy of 82%.…
Electron beam modification of vanadium dioxide oscillators
Maksim Belyaev, Andrei Velichko, Vadim Putrolaynen +2
The paper presents the results of a study of electron-beam modification (EBM) of VO2-switch I-V curve threshold parameters and the self-oscillation frequency of a circuit containin…
Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map
Andrei Velichko
This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir…
Electrical switching and oscillations in vanadium dioxide
Alexander Pergament, Andrei Velichko, Maksim Belyaev +1
We have studied electrical switching with S-shaped I-V characteristics in two-terminal MOM devices based on vanadium dioxide thin films. The switching effect is associated with the…
Switching dynamics of single and coupled VO2-based oscillators as elements of neural networks
Andrei Velichko, Maksim Belyaev, Vadim Putrolaynen +2
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 th…
Thermal coupling and effect of subharmonic synchronization in a system of two VO2 based oscillators
Andrei Velichko, Maksim Belyaev, Vadim Putrolaynen +2
We explore a prototype of an oscillatory neural network (ONN) based on vanadium dioxide switching devices. The model system under study represents two oscillators based on thermall…
Oscillator Circuit for Spike Neural Network with Sigmoid Like Activation Function and Firing Rate Coding
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. The circuit contains a switchin…
Switch Elements with S-Shaped Current-Voltage Characteristic in Models of Neural Oscillators
Petr Boriskov, Andrei Velichko
In this paper, we present circuit solutions based on a switch element with the S-type I-V characteristic implemented using the classic FitzHugh-Nagumo and FitzHugh-Rinzel models. U…
A Method for Evaluating Chimeric Synchronization of Coupled Oscillators and Its Application for Creating a Neural Network Information Converter
Andrei Velichko
This paper presents a new method for evaluating the synchronization of quasi-periodic oscillations of two oscillators, termed "chimeric synchronization". The family of metrics is p…