An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network

2018 ◽  
Vol 31 (1) ◽  
pp. 268 ◽  
Author(s):  
Ahmed Talib Abdulameer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Computer Software ◽  
Principal Component ◽  
Magnetic Resonance Images ◽  
Medical Decision ◽  
Discrete Wavelet ◽  
Second Phase ◽  
Dragonfly Algorithm ◽  
Artificial Neural

  Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers.

scholarly journals Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network

2017 ◽  
Vol 24 (2) ◽  
pp. 229-240 ◽  
Author(s):  
Monika Prucnal ◽  
Adam G. Polak
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Sleep Stage ◽  
Principal Component ◽  
Sleep Stages ◽  
Discrete Wavelet ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Artificial Neural

AbstractEEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.

Notch (stress concentration) factor estimation of a cylinder under internal pressure using different approaches

2021 ◽  
Vol 63 (5) ◽  
pp. 430-435
Author(s):  
Osman Atalay ◽  
Ihsan Toktas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stress Concentration ◽  
Mechanical Design ◽  
Computer Software ◽  
Element Analysis ◽  
Notch Stress ◽  
Artificial Neural ◽  
Effect Parameter ◽  
Stress Concentration Factors

Abstract Today, fluid transportation via pipes can be found in many sectors. Therefore, safe fluid transportation possesses critical importance. While working, transportation pipes are exposed to unwanted loads that culminate in stresses which cause deformation on the part geometry especially in sharp corners, holes or sudden cross-section change areas considered as notched. The notch effect parameter is considered in the mechanical design formulas. This study is interested in the notch factor that is estimated for a cylinder which undergoes an inner pressure. Some users can use false numerical values due to misreading or lack of attention. Because of this reason, graphs were converted to the numerical value by using computer software. In this study, Peterson’s chart was accepted as scientifically valid. Stress concentration factors were obtained by using four other approaches. These are regression, analytical, artificial neural network and finite element analysis. Among these models, high accuracy values were given by the artificial neural network model.

Emotion Classification by EEG Signal Generated by Brain using Discrete Wavelet Transform and Artificial Neural Network Backpropagation with Classical Music Stimulus

2019 ◽  
Vol 1 (2) ◽  
pp. 1-4
Author(s):  
Aditya Dimas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classical Music ◽  
Brain Activity ◽  
Piano Music ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Emotion Classification ◽  
Artificial Neural ◽  
Music Stimulus

People feel different emotions when listening to music on certain levels. Such feelings occur because the music stimuli causing reduced or increased brain activity and producing brainwave with specific characteristics. Results of research indicated that classical piano music can influence one’s emotional intelligent. By using Electroenchephalography (EEG) as a brainwave recording instrument, we can assess the effect of stimulation on the emotions generated through brain activity. This study aimed at developing a method that defines the effect of sound to brain activity using an EEG signal that can be used to identify one's emotion based on classical piano music stimulus reaction. Based on its frequency, this signal was the classified using DWT. To train Artificial Neural Network, some features were taken from the signal. This ANN research was carried out using the process of backpropagation

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

2017 ◽  
Vol 14 (9) ◽  
pp. 095601 ◽  
Author(s):  
Huimin Sun ◽  
Yaoyong Meng ◽  
Pingli Zhang ◽  
Yajing Li ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Non Invasive ◽  
Artificial Neural
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