Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals
produced by neurons. Electroencephalogram (EEG) signals are the most important data …

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 …

The problem of the origin of canonical and aberrant DNA mutations and the contribution of
protons to genetic stability is an essential topic in molecular biology. Based on the empirical …

In this study, we examine the dynamical complexity transitions during HILDCAA events.
HILDCAA preceded by an Interplanetary Coronal Mass Ejection (ICME) storm recovery …

The classification of time series using machine learning (ML) analysis and entropy-based
features is an urgent task for the study of nonlinear signals in the fields of finance, biology …

This study investigates machine learning algorithms to identify objective features for
diagnosing food addiction (FA) and assessing confirmed symptoms (SC). Data were …

Wavelet analysis is an effective manner to estimate community congestion based on time
collection analysis. Wavelet evaluation is an effective sign-processing method that can …

Chaotic phenomena are widely found in dynamic systems in nature, such as weather
temperatures, fluid turbulence and biological neural signal transmission. Due to various …