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…