Shape Classification Using Multiple Classifiers with Different Feature Sets

2011 ◽  
Vol 368-373 ◽  
pp. 1583-1587
Author(s):  
Jun Ying Chen ◽  
Jing Chen ◽  
Zeng Xi Feng
Keyword(s):  
Neural Network ◽  
Recognition Rate ◽  
Shape Classification ◽  
K Nearest Neighbor ◽  
Feed Forward Neural Network ◽  
Combination Strategy ◽  
Combination Rules ◽  
Feature Sets ◽  
Multiple Classifiers ◽  
Individual Classifier

In this paper, a new shape classification method based on different feature sets using multiple classifiers is proposed. Different feature sets are derived from the shapes by using different extraction methods. The implements of feature extraction are based on two ways: Fourier descriptors and Zernike moments. Multiple classifiers comprise Normal densities based linear classifier, k-nearest neighbor classifier, Feed-Forward neural network, Radial Basis Function neural network classifier. Each classifier is trained by two feature sets respectively to form two classification results. The final classification results are a combined response of the individual classifier using six different classifier combination rules and the results were compared with those derived from multiple classifiers based on the same feature sets and individual classifier. In this study we examined the different classification tasks on Kimia dataset. For the tasks the best combination strategy was found using the product rule, giving an average recognition rate of 95.83%.

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

Speaker Recognition With Normal and Telephonic Assamese Speech Using I-Vector and Learning-Based Classifier

2017 ◽  
pp. 256-280
Author(s):  
Mridusmita Sharma ◽  
Rituraj Kaushik ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Feed Forward Neural Network ◽  
Individual Behaviour ◽  
Behavioural Characteristics ◽  
Regional Languages ◽  
Optimal Feature

Speaker recognition is the task of identifying a person by his/her unique identification features or behavioural characteristics that are included in the speech uttered by the person. Speaker recognition deals with the identity of the speaker. It is a biometric modality which uses the features of the speaker that is influenced by one's individual behaviour as well as the characteristics of the vocal cord. The issue becomes more complex when regional languages are considered. Here, the authors report the design of a speaker recognition system using normal and telephonic Assamese speech for their case study. In their work, the authors have implemented i-vectors as features to generate an optimal feature set and have used the Feed Forward Neural Network for the recognition purpose which gives a fairly high recognition rate.

Voltage Stability Margin Assessment Using Multilayer Feed Forward Neural Network

2014 ◽  
Vol 573 ◽  
pp. 661-667 ◽  
Author(s):  
G.S. Naganathan ◽  
C.K. Babulal
Keyword(s):  
Neural Network ◽  
Power Flow ◽  
Voltage Stability ◽  
Reactive Power ◽  
Stability Margin ◽  
Feed Forward Neural Network ◽  
Continuation Power Flow ◽  
Feature Sets ◽  
Input Feature ◽  
Voltage Stability Margin

With the deregulation of electricity markets, the system operation strategies have changed in recent years. The systems are operated with smaller margins. How to maintain the voltage stability of the power systems have become an important issue.This paper presents an Artificial Feed Forward Neural Network (FFNN) approach for the assessment of power system voltage stability. This paper uses some input feature sets using real power, reactive power, voltage magnitude and phase angle to train the neural network (NN). The target output for each input pattern is obtained by computing the distance to voltage collapse from the current system operating point using a continuation power flow type algorithm. This paper compared different input feature sets and showed that reactive power and the phase angle are the best predictors of voltage stability margin. Further, the paper shows that the proposed ANN based method can successfully estimate the voltage stability margin not only under normal operation but also under N-1 contingency situations. The proposed method has been applied to the IEEE 14 and IEEE 30 bus test system. The continuation power flow technique run with PSAT and the proposed method is implemented in MATLAB.

Speaker Recognition With Normal and Telephonic Assamese Speech Using I-Vector and Learning-Based Classifier

2020 ◽  
pp. 805-829
Author(s):  
Mridusmita Sharma ◽  
Rituraj Kaushik ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Feed Forward Neural Network ◽  
Individual Behaviour ◽  
Behavioural Characteristics ◽  
Regional Languages ◽  
Optimal Feature

Speaker recognition is the task of identifying a person by his/her unique identification features or behavioural characteristics that are included in the speech uttered by the person. Speaker recognition deals with the identity of the speaker. It is a biometric modality which uses the features of the speaker that is influenced by one's individual behaviour as well as the characteristics of the vocal cord. The issue becomes more complex when regional languages are considered. Here, the authors report the design of a speaker recognition system using normal and telephonic Assamese speech for their case study. In their work, the authors have implemented i-vectors as features to generate an optimal feature set and have used the Feed Forward Neural Network for the recognition purpose which gives a fairly high recognition rate.

COMPARISON OF NEURAL NETWORK AND K-NN CLASSIFICATION METHODS IN VOWEL AND PATELLAR SUBLUXATION IMAGE RECOGNITIONS

1993 ◽  
Vol 07 (04) ◽  
pp. 775-782 ◽  
Author(s):  
EUNG-KYEU KIM ◽  
JIAN-TONG WU ◽  
SHINICHI TAMURA ◽  
YOSHINOBU SATO ◽  
ROBERT CLOSE ◽  
...  
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Average Recognition Rate ◽  
Patellar Subluxation ◽  
Voice Data ◽  
Neighbor Classification ◽  
Better Than

We make a comparision of classification ability between BPN (BackPropagation Neural Network) and k-NN (k-Nearest Neighbor) classification methods. Voice data and patellar subluxation images are used. The result was that the average recognition rate of BPN was 9.2 percent higher than that of the k-NN classification method. Although k-NN classification is simple in theory, classification time was fairly long. Therefore, it seems that real time recognition is difficult. On the other hand, the BPN method has a long learning time but a very short recognition time. Especially if the number of dimensions of the samples is large, it can be said that BPN is better than k-NN in classification ability.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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