scholarly journals Incorporating FCM and Back Propagation Neural Network for Image Segmentation

2014 ◽  
pp. 16-21
Author(s):  
Ebrahim. Aghajari ◽  
Dr.Mrs. Gharpure Damayanti
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Input Image ◽  
Discrete Wavelet ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm ◽  
The Membership Function

Hybrid image segmentation is proposed in this paper. The input image is firstly preprocessed in order to extract the features using Discrete Wavelet Transform (DWT) .The features are then fed to Fuzzy C-means algorithm which is unsupervised. The membership function created by Fuzzy C-means (FCM) is used as a target to be fed in neural network. Then the Back Propagation Neural network (BPN) has been trained based on targets which is obtained by (FCM) and features as input data. Combining the FCM information and neural network in unsupervised manner lead us to achieve better segmentation .The proposed algorithm is tested on various Berkeley database gray level images.

LICENSE-PLATE RECOGNITION USING DWT AND NEURAL NETWORK

2006 ◽  
Vol 04 (04) ◽  
pp. 601-615 ◽  
Author(s):  
TIAN-DING CHEN
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
High Accuracy ◽  
Discrete Wavelet ◽  
License Plate ◽  
License Plate Recognition ◽  
Structure Modification ◽  
New Approach ◽  
Low Pass

This paper presents a new approach for license-plate recognition using Discrete Wavelet Transform (DWT) and Plastic Perception Neural Network (PPNN). It accomplishes the preliminary license-plate localization by applying low-pass wavelet coefficients. Since the amount of data reduces to 1/4, this approach saves a lot of running time, simplifies computational complexity, and economizes memory usage. It adopts the LL and HH sub-bands, which come from a two-dimensional Haar DWT to implement the localization and segmentation for license plates. The proposed methodology provides high accuracy for locating a license plate from an image, and has a high tolerance for license plate displacement in the images. Back-Propagation Neural Network (BPNN) has the advantage of anti-noise and anti-distortion, but the problems of traditional BPNN are a longer learning period, iterations are not prone to convergence, and local minimum. The proposed methods combine the parallel distributive process concept with the BPNN structure modification to solve the above problems. This paper also utilizes PPNN to solve taking position, scale and rotation of the license-plate recognition.

An Efficient Neural Network Model with Taylor Series-Based Data Pre-Processing for Stock Price Forecast

2013 ◽  
Vol 284-287 ◽  
pp. 3020-3024
Author(s):  
Jung Bin Li ◽  
Chien Ho Wu
Keyword(s):  
Neural Network ◽  
Empirical Study ◽  
Neural Network Model ◽  
Taylor Series ◽  
Stock Price ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
High Accuracy ◽  
Price Forecast

This study adopts popular back-propagation neural network to make one-period-ahead prediction of the stock price. A model based on Taylor series by using both fundamental and technical indicators EPS and MACD as input data is built for an empirical study. Leading Taiwanese companies in non-hi-tech industry such as Formosa Plastics, Yieh Phui Steel, Evergreen Marine, and Chang Hwa Bank are picked as targets to analyze their reasonable prices and moving trends. The performance of this model shows remarkable return and high accuracy in making long/short strategies.

scholarly journals An efficient method for network security situation assessment

2020 ◽  
Vol 16 (11) ◽  
pp. 155014772097151
Author(s):  
Xiaoling Tao ◽  
Kaichuan Kong ◽  
Feng Zhao ◽  
Siyan Cheng ◽  
Sufang Wang
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Data Storage ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Assessment Method ◽  
Trend Prediction ◽  
Situation Assessment ◽  
Situational Assessment

Network security situational assessment, the core task of network security situational awareness, can obtain security situation by comprehensively analyzing various factors that affect network status. Thus, network security situational assessment can provide accurate security state evaluation and security trend prediction for users. Although plenty of network security situational assessment methods have been proposed, there are still many problems to solve. First, because of high dimensionality of input data, computational complexity in model construction could be very high. Moreover, most of the existing schemes trade computational overhead for accuracy. Second, due to the lack of centralized standard, the weights of indicators are usually determined empirically or by subjective opinions of domain expert. To solve the above problems, we propose a novel network security situation assessment method based on stack autoencoding network and back propagation neural network. In stack autoencoding network and back propagation neural network, to reduce the data storage overhead and improve computational efficiency, we use stack autoencoding network to reduce the dimensions of the indicator data. And the low-dimensional data output by hidden layer of stack autoencoding network will be the input data of the error back propagation neural network. Then, the back propagation neural network algorithm is adopted to perform network security situation assessment. Finally, extensive experiments are conducted to verify the effectiveness of the proposed method.

scholarly journals Brain Tumor Classification and Segmentation using DTCW Transform, Back Propagation Neural Network and Spatial Fuzzy C-Means Clustering

2020 ◽  
Vol 9 (5) ◽  
pp. 2073-2079
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Mri ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Mr Images ◽  
Data Set ◽  
Fuzzy C Means ◽  
Tumor Identification ◽  
Fuzzy C Means Clustering

A novel method is presented in this paper for finding brain tumor and classifying it using the back-propagation neural network is proposed. Spatial Fuzzy C-Means clustering is utilized for the segmentation of image to identify the influenced area of brain MRI picture. Automated detection of tumors in brain MR images is urgent in many diagnosis processes. Because of noise, blurred edges, the detection, and classification of brain tumor are very difficult. This paper presents one programmed brain tumor identification strategy to expand the exactness and yield and diminishing the determination time. The objective is ordering the tissues to three classes of typical, start and malignant. The size and the location tumor is very important for doctors for defining the treatment of tumor. The proposed determination strategy comprises of four phases, pre-processing of MR images, feature extraction, and classification. The features are extracted using Dual-Tree Complex wavelet transformation (DTCWT). Back Propagation Neural Network (BPN) is employed for finding brain tumor in MRI images. In the last stage, a productive scheme is proposed for segmentation depends on the Spatial Fuzzy C-Means Clustering. The performance analysis clearly proves that the proposed scheme is more efficient and the efficiency of the scheme is measured with sensitivity and specificity. The evaluation is performed on the image data set of 15 MRI images of brain.

scholarly journals Recognition of Human Facial Expressions Using DCT-DWT and Artificial Neural Network

2021 ◽  
pp. 2090-2098
Author(s):  
Wasan. Maddah Alaluosi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Facial Expressions ◽  
Emotional State ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Discrete Wavelet ◽  
Three Stages ◽  
Artificial Neural ◽  
Facial Images

Facial expressions are a term that expresses a group of movements of the facial fore muscles that is related to one's own human emotions. Human–computer interaction (HCI) has been considered as one of the most attractive and fastest-growing fields. Adding emotional expression’s recognition to expect the users’ feelings and emotional state can drastically improves HCI. This paper aims to demonstrate the three most important facial expressions (happiness, sadness, and surprise). It contains three stages; first, the preprocessing stage was performed to enhance the facial images. Second, the feature extraction stage depended on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) methods. Third, the recognition stage was applied using an artificial neural network, known as Back Propagation Neural Network (BPNN), on database images from Cohen-Kanade. The method was shown to be very efficient, where the total rate of recognition of the three facial expressions was 92.9%.

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