scholarly journals Cerebral Microbleeds Detection via Discrete Wavelet Transform and Back Propagation Neural Network

2019 ◽  
Author(s):  
Jin Hong ◽  
Zhihai Lu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Cerebral Microbleeds ◽  
Discrete Wavelet

Transmission Line Using Discrete Wavelet Transform and Back-Propagation Neural Network Based on Clarke’s Transformation

2016 ◽  
Vol 818 ◽  
pp. 156-165 ◽  
Author(s):  
Makmur Saini ◽  
Abdullah Asuhaimi bin Mohd Zin ◽  
Mohd Wazir Bin Mustafa ◽  
Ahmad Rizal Sultan ◽  
Rahimuddin
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Comparison Of The Results

This paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.

scholarly journals Pengenalan Citra Wajah Dengan Menggunakan Transformasi Wavelet Diskrit dan Jaringan Saraf Tiruan Back-Propagation

2010 ◽  
Vol 1 (2) ◽  
pp. 691
Author(s):  
Suhendry Effendy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Input Image ◽  
Discrete Wavelet ◽  
Web Camera ◽  
Artificial Neural ◽  
Facial Images

This paper discusses the facial image recognition system using Discrete Wavelet Transform and back-propagation artificial neural network. Discrete Wavelet Transform processes the input image to obtain the essential features found on the face image. These features are then classified using an back-propagation artificial neural network for the input image to be identified. Testing the system using facial images in AT & T Database of Faces of 400 images comprising 40 facial images of individuals and web-camera catches as many as 100 images of 10 individuals. The accuracy of level of recognition on AT & T Database of Faces reaches 93.5%, while the accuracy of level of recognition on a web-camera capture images up to 96%. Testing is also done on image of AT & T Database of Faces with given noise. Apparently the noise in the image does not give meaningful effect on the level of recognition accuracy. 

scholarly journals Multiple Watermarking for Images using Back-Propagation Neural Network and DWT

2019 ◽  
Vol 9 (1) ◽  
pp. 4088-4093
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Image Watermarking ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Discrete Wavelet ◽  
Multiple Watermarking ◽  
Segmented Image ◽  
Multiple Watermarks

An effective multiple watermarking technique supported on neural network into the wavelet transform can be proposed. The wavelet coefficients has been preferred by Human Visual System. In the proposed work focus on Discrete Wavelet Transform based segmented image watermarking techniques using Back-Propagation neural networks. Using improved BPNN, the multiple watermarks are embedded into the original image, which can advance the pace of the learn, reduce the error and the qualified neural networks are extricate multiple watermarks as of the embedded images. The planned strategy achieves a excellent visual effect scheduled the watermarked images as well as high robustness on extracted multiple watermarks.

scholarly journals A Methodical Block based Feature Level Image Fusion Technique with Wavelet Transform using Neural Network for Satellite Images

2019 ◽  
Vol 8 (6S) ◽  
pp. 465-474
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Back Propagation ◽  
Image Data ◽  
Back Propagation Neural Network ◽  
Discrete Wavelet ◽  
Learning Capability ◽  
Feed Forward Back Propagation ◽  
Block Based

This paper describe about the feature extraction or detection machine learning application which one is wavelet transform integrated with neural network. It has obtained an effective block based feature level with wavelet transform using neural network (BFWN) model for image fusion. In the projected BFWN model, the discrete wavelet transform (DWT) and neural network (NN) are considered for fusing IRS-1D images using LISS- III scanner about the location different areas in India. Also Quick Bird image data and Landsat 7 image data are used to carry out on the proposed BFWN method. The characteristics like contrast visibility, energy of gradient, spatial frequency, variance and edge information are under study. A Feed forward back propagation neural network is trained and tested for categorization since the learning capability of neural network makes it feasible to customize the image fusion process. The trained neural network is used to fuse the two source images. The proposed BFWN model is distinguish, with DWT alone to assess the quality of the fused image. The results obviously show that the proposed BFWN model is a capable and feasible algorithm for image fusion

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