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Purushothaman, S.
- Implementation of Hamilton Rating Scale Depression Data Using Back Propagation Network and Echo STAE Neural Network (BPAESNN) Methods
Abstract Views :186 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, Vels University, Chennai, IN
2 PET Engineering College, Vallioor-627117, IN
3 Mother Teresa Womens University, Kodaikanal, IN
1 Department of Computer Science, Vels University, Chennai, IN
2 PET Engineering College, Vallioor-627117, IN
3 Mother Teresa Womens University, Kodaikanal, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 9 (2013), Pagination: 345-349Abstract
Depression is a serious and widespread public health challenge. This paper propose neural network algorithm for faster learning of psychological depression data. Implementation of neural networks methods for depression data mining using back propagation algorithm (BPA) and Echo state neural network (ESNN) are presented. Experimental data were collected with 21 input variables and one output for working with artificial neural network (ANN). Using the data collected, the training patterns and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of ANN In order to find the optimum number of nodes required in the hidden layer of an ANN, a method has been proposed, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to ESNN. The network trained with transformed vectors is seen to require the least computational effort. The work proves to be an efficient system for diagnosis of depression.Keywords
Hamilton Rating Scale (HRS) Depression Data, BPA, ESNN.- Estimation of Inframe Fill Stability Using Echo State Neural Network
Abstract Views :158 |
PDF Views:3
Authors
Affiliations
1 Department of Civil Engineering, CMJ University, IN
2 PET Engineering College, Vallioor-627117, IN
1 Department of Civil Engineering, CMJ University, IN
2 PET Engineering College, Vallioor-627117, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 9 (2013), Pagination: 356-360Abstract
In regions of high seismicity, infilled frames are commonly used for low and medium-height buildings. "Infilled frame" is a composite structure. It is formed by one or more infill panels surrounded by a frame. Infilled frame also refers to the situation in which the frame is built first and then infilled with one or more masonry panels. The primary function of masonry was either to protect the inside of the structure from the environment or to divide inside spaces. The presence of masonry infills helps the overall behavior of structures when applying lateral forces. The lateral stiffness and the lateral load capacity of the structure largely increase when masonry infills are considered to interact with their surrounding frames. In this paper, ANSYS 14 software is used for analyzing the infill frames. Echo state neural network (ESNN) has been used to supplement the estimation of stress values of the proposed infill frame model. The number of nodes or reservoirs in the hidden layer for ESNN algorithm varies depends upon the accuracy of estimation required. Exact number of reservoirs is fixed based on the trial and error method, through which the accuracy of estimation by the ESNN is achieved.Keywords
Echo State Neural Network (ESNN), Reservoir, Processing Elements (PE), Finite Element Method (FEM), Equivalent Stress.- Implementation of Wavelet Transform and Back Propagation Neural Network for Identification of Microcalcification in Breast
Abstract Views :168 |
PDF Views:2
Authors
Affiliations
1 VELS University, Pallavaram, Chennai-600117, IN
2 PET Engineering College, Vallioor-627117, IN
3 Mother Teresa Women's University, Kodaikanal-624102, IN
1 VELS University, Pallavaram, Chennai-600117, IN
2 PET Engineering College, Vallioor-627117, IN
3 Mother Teresa Women's University, Kodaikanal-624102, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 9 (2013), Pagination: 361-365Abstract
Wavelet decomposition has been applied to mammogram image to obtain four different coefficients for 5 levels of decompositions. The coefficients are: low frequency coefficients (A), vertical high frequency coefficients (V), horizontal high frequency coefficients (H), and diagonal high frequency coefficients (D). The features of the mammography image are obtained using the wavelet transform selecting the different levels of decompositions. The proposed method presents a new classification approach to microcalcification (MC) detection in mammograms using wavelet and back propagation algorithm (BPA) Neural Network. These features obtained from wavelet are representation of MC as well as other information of the image. Daubauchi wavelet has been used to decompose image to 5 levels. Statistical features are extracted from the wavelet coefficients. Training the BPA with features and testing the BPA to identify the presence of MC has been done. The percentage identification is above 96.2%. The performance of the proposed method based on the quality of the mammogram image.Keywords
Mammogram, Microcalcification, Wavelet Transform, Back Propagation Neural Network.- Implementation of Echo State Neural Network and Radial Basis Function Network for Intrusion Detection
Abstract Views :148 |
PDF Views:2
Authors
Affiliations
1 VELS University, Pallavaram, Chennai-600117, IN
2 PET Engineering College, Vallioor-627117, IN
3 Mother Teresa Women's University, Kodaikanal-624102, IN
1 VELS University, Pallavaram, Chennai-600117, IN
2 PET Engineering College, Vallioor-627117, IN
3 Mother Teresa Women's University, Kodaikanal-624102, IN