scholarly journals Recognition of MIR Data of Semen Armeniacae Amarum and Semen Persicae Using Discrete Wavelet Transformation and BP-Artificial Neural Network

2012 ◽  
Vol 27 ◽  
pp. 253-264 ◽  
Author(s):  
Cun-Gui Cheng ◽  
Peng Yu ◽  
Chang-Shun Wu ◽  
Jia-Ni Shou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transformation ◽  
Feature Vector ◽  
Recognition Rate ◽  
Back Propagation ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transformation ◽  
Artificial Neural ◽  
The Difference

Horizontal attenuation total reflection-Fourier transformation infrared spectroscopy (HATR-FT-IR) is used to measure the Mid-IR (MIR) of semen armeniacae amarum and its confusable varieties semen persicae. In order to extrude the difference between semen armeniacae amarum and semen persicae, discrete wavelet transformation (DWT) is used to decompose the MIR of semen armeniacae amarum and semen persicae. Two main scales are selected as the feature extracting space in the DWT domain. According to the distribution of semen armeniacae amarum and semen persicae’s MIR, five feature regions are determined at every spectra band by selecting two scales in the DWT domain. Thus, ten feature parameters form the feature vector. The feature vector is input to the back-propagation artificial neural network (BP-ANN) to train so as to accurately classify the semen armeniacae amarum and semen persicae. 100 couples of MIR are used to train and test the proposed method, where 50 couples of data are used to train samples and other 50 couples of data are used to test samples. Experimental results show that the accurate recognition rate between semen armeniacae amarum and semen persicae is averaged 99% following the proposed method.

scholarly journals Recognition of FT-IR Data Cuscutae Semen, Japanese Dodder, and Sinapis Semen Using Discrete Wavelet Transformation and RBF Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Tao Hu ◽  
Xuexiang Weng ◽  
Lishan Xu ◽  
Cungui Cheng ◽  
Peng Yu
Keyword(s):  
Neural Networks ◽  
Wavelet Transformation ◽  
Feature Vector ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transformation ◽  
Rbf Neural Networks ◽  
Feature Extracting ◽  
Ir Studies ◽  
Ft Ir

Horizontal attenuation total reflection Fourier transformation infrared spectroscopy (HATR-FT-IR) studies on cuscutae semen and its confusable varieties Japanese dodder and sinapis semen combined with discrete wavelet transformation (DWT) and radial basis function (RBF) neural networks have been conducted in order to classify them. DWT is used to decompose the FT-IRs of cuscutae semen, Japanese dodder, and sinapis semen. Two main scales are selected as the feature extracting space in the DWT domain. According to the distribution of cuscutae semen, Japanese dodder, and sinapis semen’s FT-IRs, three feature regions are determined at detail 3, and two feature regions are determined at detail 4 by selecting two scales in the DWT domain. Thus five feature parameters form the feature vector. The feature vector is input to the RBF neural networks to train so as to accurately classify the cuscutae semen, Japanese dodder, and sinapis semen. 120 sets of FT-IR data are used to train and test the proposed method, where 60 sets of data are used to train samples, and another 60 sets of FT-IR data are used to test samples. Experimental results show that the accurate recognition rate of cuscutae semen, Japanese dodder, and sinapis semen is average of 100.00%, 98.33%, and 100.00%, respectively, following the proposed method.

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%.

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 Implementation of Back Propagation Artificial Neural Network for Heart Disease Abnormality Diagnosis

2021 ◽  
Vol 1764 (1) ◽  
pp. 012165
Author(s):  
Jaya Kuncara Rosa Susila ◽  
Muhammad Afit ◽  
Pujo Laksono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Back Propagation ◽  
Artificial Neural

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

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

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