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BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 8 . The Comparision of Run Times

That is distinct that dynamic mutation rate or reduction idea for mutation operator is more better of fixed rate. In fact obtain to high accuracy is result of our idea for mutation operator. The number of hidden layer neurone is important problem for NN. The natural selection by GA help finding the number of hidden layer neurone and it progress on duration generations. The structured model of GANN finds better answer than NN but with much run time in simulation. The learning of GA is much better than NN with back propagation because BP is a method based on gradient descend and local optimum is a serious risk for that. We hope that the number of training samples is more accurate without error, the new algorithm is better. Tests show that the combination of genetic algorithms and neural networks to an acceptable level solves the problem of overfitting.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 7 . Test Accuracy with prograess generation

In the training phase, the neural network weights errors are minimized and network design problem which the objective function to an acceptable level. In test step we have better results because weights of neural network are adjusted by genetic algorithm and back propagation method. Of course achievement to accuracy with 83.5% is reason using of good feature with minimum error.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 6. Training Accuracy with prograess generation

There are many features will reduce the efficiency of the algorithm and its complexity. Among the methods for selecting the appropriate features, the algorithm is a GA. One of the important parameters for testing methods is accuracy rate on progress generation. In fact accuracy is reverse error in algorithm results. As reader can compare the results of our paper with another works. Figure 4 show that accuracy present for Training step. We achieve to best answers of 800 generation to after generation.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 4. Structural Crossover

Guided crossover operator is based on the two point separation from parents are selected Left and right parts of them are related to each other by the condition to be meaningful With this new child of his parents is that. But a new generation of the random choice to have reached this stage. The crossover rate is fixed for our algorithm.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 5. Insertion and Deletion Hidden Layer in NN

Change in NN structure is other method that we used to optimization of solution[18]. Insertion a hidden layer caused to mutation operator is much natural. As connection with father and mother nodes is easily[20],[21]. Weights of node and errors automatically calculated. For each stage of the implementation of the mutation operator in genetic algorithms, neural networks, only one of the nodes in the hidden layer is selected and inserted. These layers are inserted on condition that the definition does not harm the network structure and the action is meaningful. As an added layer can adjust the weights and the connection to the parent node of a network layer to be removed.

BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 3. The Structure of Neural Network

A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network[9]. In more practical terms neural networks are nonlinear statistical data modelling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Two neurons neural network active in memory (ON or 1) or disable (Off or 0), and each edge (synapses or connections between nodes) is a weight. Edges with positive weight, stimulate or activate next active node, and edges with negative weight, disable or inhibit the next connected node (if it is active) ones.

opencc-by-4.0Jan 2012View details →

Figure 2.Flow Chart for Data preprocessing & Training-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Methodology This paper develops an ANN based comparative predictive model for NASDAQ stock prediction. The first ANN model is developed with Multi-Layer Feed forward Network Architecture & the second model is developed with Recurrent Neural Network Architecture. In this paper gradient descent based back propagation learning algorithm is used for the supervised learning of the predictive network.

opencc-by-4.0Apr 2017View details →

BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 4. Mobile Application to collect Sketch data

The drawing of a tree is shown on the drawing canvas in Figure 4. It is clear from the figure that there is some empty area on top, right, left and bottom of the drawing sketch, which can cause problems while using this image for training the neural network.

opencc-by-4.0Feb 2018View details →

Pore network modeling data for Fontainebleau and Berea Sandstones

This data set contains results from pore network modeling of one sample of dry Fontainebleau sandstone (Case 1), and two samples of Berea sandstones (dry, Case 2, oil and water saturated, case 3). For each sample, there are two .csv file. One file containing information about pockets (pores) and the other containing information about throats. The content of each column is described in the heading of the files.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 11. Accuracy of different method for unseen faces

Table 3 shows the mode detection accuracy of the proposed method and its combination with two other methods (uniform LBP and circular LBP) for different people. The overall accuracy of the proposed procedure is calculated as this way one video is chosen as input, and after mode detection the three aforementioned steps are applied on this video. The obtained feature vectors are given to the neural network and the corresponding labels to each frame are regarded as output. Afterwards, the overall accuracy is calculated from the confusion matrix. However, it should be noted that the expression detection criteria are the observation of a certain number of subsequent similar labels and in the case of observing a limited or sparse number of different labels the final label would not change. Figure 10 and 11 show result of different methods for seen and unseen data respectively. Table 4 shows results for seen data with proposed method and uniform LBP.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 9. Results of our facial motion capture system(a,b,c,d)

In test procedures, single video feature vectors consisting of different expressions are given to the neural network and the network produces the corresponding labels for each frame as output. If there is a mode in a video which is not available in the data base, the nearest available mode's label to this mode is produced. For example, in test3 and test6 videos, the surprise expression (that have been showed with number 7) is recognized as open mouth expression. At the end, considering the certain numbers of subsequent similar labels (at least 10 frames, because the minimum number of one modes' frames is related to “rising the eyebrow” mode that takes 10 frames), the expressions are detected, and a 3D show of these expressions are represented. For instance, in test8 videos that have been obtained from unseen face, the “smiling” and “open mouth” expressions are well recognized, but expressions related to rising the eyebrows are not detected properly and all the corresponding frames to this expression are regarded as normal expression. Figure 9 shows example of generated 3D models.

opencc-by-4.0May 2018View details →

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 8. 3D model of some facial expressions

Face region is separated precisely from video frames by using a segmentation method based on skin color. The depth data corresponding to this separated area is taken for a 3D representation from depth data corresponding to each frame. At the end, a file is prepared for each frame consisting of face points with 6 features: X, Y, depth, red, green and blue color. These data are used for producing a 3D model and a graphical avatar for each frame (Figure 7). Figure 8 shows 3D model of some facial expressions.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 6. Proposed feed-forward neural network classifier

After the feature extraction stage, neural network is used for classifying the modes. In this study, the utilized expressions are normal, smiling, open mouth, rising the eyebrows, anger and pursing modes. In fact, they are some selective modes for face movements. It should be noted that the modes can be increased but in this case we work with these six modes. This paper used three layers feed-forward neural network (Figure 6). The proposed neural network includes 800 nodes for the input layer (400 nodes for U matrix and 400 nodes for V matrix), 100 nodes for the hidden layer and 6-nodes for output layer. From the collected data 70% are used for training, 15% for validation and the last 15% are used to evaluate the neural network.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 5. Examples of the circular LBP (Huang et al., 2011)

One limitation of the basic LBP operator is that its small 3x3 neighborhood cannot capture dominant features with large scale structures. To deal with the texture at different scales the operator was later generalized to use neighborhoods of different sizes. A local neighborhood is defined as a set of sampling points evenly spaced on a circle which is centered at the pixel to be labeled. The sampling points that do not fall within the pixels are interpolated using bilinear interpolation, thus allowing for any radius and any number of sampling points in the neighborhood. Figure 5 shows some examples of the extended LBP operator where the notation (P, R) denotes a neighborhood of P sampling points on a circle of radius of R.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 7. Avatar 3D model generation

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 3. Feature vectors of facial expression in database

Figure 3 shows feature vectors of facial expression of our database. Matrices ‘U’ and ‘V’ values that are obtained from this algorithm are used as feature vectors. The ‘U’ matrix represents the position and the ‘V’ matrix represents the change of direction. In the following, the proposed method is combined with some other feature extraction methods (LBP uniform approach and LBP circular approach) and the obtained results will be mentioned.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 2. Facial expression recognition in proposed method

In this stage, a video is prepared using the color data captured from Kinect camera. The face region in each frame is obtained from the video using Viola-Jones algorithm (Figure 2). Because of different distance from the Kinect camera, the obtained images from the face must be re-sized, in order to have the same size. At the end, the colored images are converted to gray-scaled images.

A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 1. Peak of facial expression in database

In this study, data are obtained from the Kinect camera that benefits from colorful images and depth data. Kinect can record colorful and depth data simultaneously at 30 frames per second. The data are collected from the person who initially pose in front of the camera with normal face mode and then the various modes are represented. It should be noted that data are obtained at different distances from the Kinect camera and in different lighting conditions. Figure 1 shows various facial modes in our database.