Wavelet Coefficient Trained Neural Network Classifier for Improvement in Qualitative Classification Performance of Oxygen-Plasma Treated Thick Film Tin Oxide Sensor Array Exposed to Different Odors/Gases

2011 ◽  
Vol 11 (4) ◽  
pp. 1013-1018 ◽  
Author(s):  
Ravi Kumar ◽  
R. R. Das ◽  
V. N. Mishra ◽  
R. Dwivedi
Keyword(s):  
Neural Network ◽  
Thick Film ◽  
Tin Oxide ◽  
Oxygen Plasma ◽  
Sensor Array ◽  
Wavelet Coefficient ◽  
Classification Performance ◽  
Neural Network Classifier ◽  
Qualitative Classification ◽  
Trained Neural Network

Response of oxygen plasma-treated thick film tin oxide sensor array for LPG, CCl4, CO and C3H7OH

1999 ◽  
Vol 30 (3) ◽  
pp. 259-264 ◽  
Author(s):  
A Chaturvedi ◽  
V.N Mishra ◽  
R Dwivedi ◽  
S.K Srivastava
Keyword(s):  
Thick Film ◽  
Tin Oxide ◽  
Oxygen Plasma ◽  
Sensor Array

Discrimination of individual gas/odor using responses of integrated thick film tin oxide sensor array and fuzzy-neuro concept

1999 ◽  
Vol 30 (8) ◽  
pp. 793-800 ◽  
Author(s):  
R.R. Das ◽  
K.K. Shukla ◽  
R. Dwivedi ◽  
A.R. Srivastava
Keyword(s):  
Thick Film ◽  
Tin Oxide ◽  
Sensor Array

scholarly journals Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

2013 ◽  
Vol 13 (3) ◽  
pp. 142-151 ◽  
Author(s):  
Muhammad Ibn Ibrahimy ◽  
Rezwanul Ahsan ◽  
Othman Omran Khalifa
Keyword(s):  
Neural Network ◽  
Single Channel ◽  
Learning Algorithms ◽  
Classification Performance ◽  
Training Algorithms ◽  
Neural Network Classifier ◽  
Hand Motion ◽  
Emg Signal ◽  
Levenberg Marquardt ◽  
Hidden Neurons

This paper presents an application of artificial neural network for the classification of single channel EMG signal in the context of hand motion detection. Seven statistical input features that are extracted from the preprocessed single channel EMG signals recorded for four predefined hand motions have been used for neural network classifier. Different structures of neural network, based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out their applicability for EMG signal classification. The classification performances are analyzed for different architectures of neural network by considering the number of input features, number of hidden neurons, learning algorithms, correlation between network outputs and targets, and mean square error. Between the Levenberg-Marquardt and scaled conjugate gradient learning algorithms, the aforesaid algorithm shows better classification performance. The outcomes of the research show that the optimal design of Levenberg-Marquardt based neural network classifier can perform well with an average classification success rate of 88.4%. A comparison of results has also been presented to validate the effectiveness of the designed neural network classifier to discriminate EMG signals.

scholarly journals Classification of coronary artery disease data sets by using a deep neural network

2017 ◽  
Vol 1 (4) ◽  
pp. 271-277 ◽  
Author(s):  
Abdullah Caliskan ◽  
Mehmet Emin Yuksel
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Deep Neural Network ◽  
Cost Effective ◽  
Classification Performance ◽  
Data Sets ◽  
Neural Network Classifier ◽  
Artery Disease

Abstract In this study, a deep neural network classifier is proposed for the classification of coronary artery disease medical data sets. The proposed classifier is tested on reference CAD data sets from the literature and also compared with popular representative classification methods regarding its classification performance. Experimental results show that the deep neural network classifier offers much better accuracy, sensitivity and specificity rates when compared with other methods. The proposed method presents itself as an easily accessible and cost-effective alternative to currently existing methods used for the diagnosis of CAD and it can be applied for easily checking whether a given subject under examination has at least one occluded coronary artery or not.

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