Combined neural network model employing wavelet coefficients for EEG signals classification

ED Übeyli - Digital Signal Processing, 2009 - Elsevier
This paper illustrates the use of combined neural network model to guide model selection for
classification of electroencephalogram (EEG) signals. The EEG signals were decomposed …

Implementing automated diagnostic systems for breast cancer detection

ED Übeyli - Expert systems with Applications, 2007 - Elsevier
This paper intends to an integrated view of implementing automated diagnostic systems for
breast cancer detection. The major objective of the paper is to be a guide for the readers …

Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures

X Zhang, K Garikipati - Computer Methods in Applied Mechanics and …, 2020 - Elsevier
Many important multi-component crystalline solids undergo mechanochemical spinodal
decomposition: a phase transformation in which the compositional redistribution is coupled …

Combining recurrent neural networks with eigenvector methods for classification of ECG beats

ED Übeyli - Digital Signal Processing, 2009 - Elsevier
The purpose of this study is to evaluate the accuracy of the recurrent neural networks
(RNNs) trained with Levenberg–Marquardt algorithm on the electrocardiogram (ECG) beats …

Identification of gas-liquid flow regimes using a non-intrusive Doppler ultrasonic sensor and virtual flow regime maps

SG Nnabuife, KES Pilario, L Lao, Y Cao… - Flow Measurement and …, 2019 - Elsevier
The accurate prediction of flow regimes is vital for the analysis of behaviour and operation of
gas/liquid two-phase systems in industrial processes. This paper investigates the feasibility …

Non-invasive classification of gas–liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

BM Abbagoni, H Yeung - Measurement Science and Technology, 2016 - iopscience.iop.org
The identification of flow pattern is a key issue in multiphase flow which is encountered in
the petrochemical industry. It is difficult to identify the gas–liquid flow regimes objectively …

Two-phase flow regime identification using multi-method feature extraction and explainable kernel Fisher discriminant analysis

U Khan, W Pao, KES Pilario, N Sallih… - International Journal of …, 2024 - emerald.com
Purpose Identifying the flow regime is a prerequisite for accurately modeling two-phase flow.
This paper aims to introduce a comprehensive data-driven workflow for flow regime …

Flow regime classification using various dimensionality reduction methods and AutoML

U Khan, W Pao, KE Pilario, N Sallih - Engineering Analysis with Boundary …, 2024 - Elsevier
Accurate identification of flow regimes is paramount in several industries, especially in
chemical and hydrocarbon sectors. This paper describes a comprehensive data-driven …

Development of Gas–Liquid Flow Regimes Identification Using a Noninvasive Ultrasonic Sensor, Belt-Shape Features, and Convolutional Neural Network in an S …

SG Nnabuife, B Kuang, JF Whidborne… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The problem of classifying gas–liquid two-phase flow regimes from ultrasonic signals is
considered. A new method, belt-shaped features (BSFs), is proposed for performing feature …

Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

M Seera, CP Lim, WS Liew, E Lim, CK Loo - Expert Systems with …, 2015 - Elsevier
In this paper, two real-world medical classification problems using electrocardiogram (ECG)
and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine …