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Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate count, yeast & mould count, and spore count were taken as input parameters, and sensory score as output parameter for developing TDNN single and multilayer models (Fig.1).

Figure 6. Performance plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 5 we can see that the mse curve reaches the In performance goal but it does not decrease in that good manner,but in Figure 6 the mse is reduces widely. By analyzing all these results one can say that RNN is better choice than Feedforward MLP in prediction purpose.

Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

TDNN models with single and multi layers were developed taking soluble nitrogen, pH, standard plate count, yeast & mould count, spore count as input parameters, and sensory score as output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used in order to compare the prediction ability of the developed TDNN models. Regression equations were developed for predicting the shelf life of processed cheese, which came out as 28.25 days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence from the study it can be concluded that TDNN artificial neural network models are quite efficient in predicting shelf life of processed cheese.

Figure 5. Performance plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 3. Regression plot for NASDAQ index (MLP)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 3 depicts the regression plot for the feedforward MLP network, analyzing it we can say that Y=T regression is not so good.

Figure 4. Regression plot for NASDAQ index (RNN)-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning

Figure 4,depicts the regression plot for the Timedelay RNN network, analyzing it we can say that Y=T regression is totally fit. This paper also comprises of comparative study of performance(mse) plot of both network.

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.

Figure 4. Extracted bands diagramClassification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Based on that coefficient, different bands have to be extracted. The bands are alpha, beta, theta, gamma, and delta. Figure 4 shown in below which is represent the different extract band diagrams.

opencc-by-4.0Aug 2015View details →

Figure 3. Electrode placement diagram-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Reference electrode placed AFz placed in between AF1 and AF2 electrode and ground electrode Oz is placed between O1 and O2 electrodes. The impedance of the electrode range is 5KΩ. The sampling rate was fixed range between 256 samples per second for all the channels. Figure 3 shown in below which is represent the electrode placement diagram. The recorded EEG signal is used to recognize the different level of emotions.

opencc-by-4.0Aug 2015View details →

Figure 2. Emotion recognition using EEG-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

This section describes that collection of EEG signals for different emotion recognition experiments. The electroencephalography signals of 32 participants were recorded during one minute videos. Based on that participants are rated in terms of valence and arousal, like/dislike, familiarities and dominance. Emotion related ratings are given based on the online self assessment which is 120 one minute extracted music videos, which are rated by 14-16 volunteers based on arousal and valence. The EEG signals were recorded using 64 electrodes, first 62 electrodes are active electrode, one for reference and remaining one is ground electrode. All the electrodes are placed on the scalp which is made up of the Ag/Ag-Cl. Figure 2 shown in below which is represent the basic EEG signal recording methods and emotion analysis process.

opencc-by-4.0Aug 2015View details →

Figure 1. Brain Structure-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

EEG data have collected from desirable subjects. Each and every EEG signal has different kind of bands like Alpha, Beta, Gamma, Theta, and Delta. Each band stores the particular information about the emotions. Alpha band (8-13 Hz) which located in Frontal Occipital, Beta band (13-30 Hz) which located in Frontal Central, Gamma band (30-100 Hz), Theta band (4- 7 Hz) which located in Midline Temp, Delta band (0-4Hz) which located in Frontal Lobe. Before processing the EEG signal and extracting these bands, preprocess the signal and reduce the noise. The basic brain figure is shown in below.

Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

For all the 32 participants the EEG signal has to be sampled and process their emotions. The emotions are depends on the music and video clips. In this paper the video clips are changed from one person to other person. Here hybrid feed forward neural networks with radial basis function; probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast and insensitive neural network which provides the optimized classification result. Compare to the multi layer perception neural network it provide accurate result. It classifies the emotion into two different groups like arousal and valence. Figure 8 shows that the model implemented result of emotion classification and person identification.

Figure 7. Neural Network model-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

In Probabilistic Neural Network the operations are organized into a multilayer feed forward neural network with four layers like input layer, hidden layer, pattern layer and output layer. PNN use the Euclidean distance measure the difference between one neuron to other neurons. The actual target values are stored in the hidden neuron and the optimized weighted values are fed into the same category hidden neuron. Then finally the output layer compared the weighted votes of each target values and the target votes are used to predict the emotions.

Figure 10. Sensitivity and specificity of different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Figure10 has shown in sensitivity and specificity of different neural network which is used to explain how exactly the emotions are classified into groups and accuracy value shown in above table 3.

Figure 9. Mean Square Error for different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Figure 9 has shown in different epochs using neural networks with mean square error performance and the Table 3 shows that different bands mean square error values while training the neural networks with particle swarm optimization.

Figure 6. Mean square error of alpha band-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Particle swarm optimization algorithm first optimizes the neural networks weight and bias and provides the minimum mean square error with nearer by zero. The following figure 6 has shown that minimum mean square error when training the particular band features.

Figure 5. Process flow of PSO-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

PSO is used to identify the best solution from collection of solution. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position but, is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, metaheuristic such as PSO do not guarantee an optimal solution is ever found. The following Figure 5 explains the basic flow of PSO process.

opencc-by-4.0Jan 2016View details →

Figure 1. CNN architecture (adopted from Krizhevsky et al. '12)-Measuring Customer Behavior with Deep Convolutional Neural Networks

The architecture of a CNN can be described as following. A small pixel region goes to input neurons and then connects to a first convolution hidden layer (Figure1). There we can see a set of learnable filters, which are activated during the presentation some particular type of feature in pixel region in the input. On this phase, CNN does shift invariance, which is carried by feature map. Subsampling layer goes next. There we have two processes: local averaging and sampling. As a result, we get declining resolution of feature map. To correspond this task CNN needs supervised learning. Before starting the experiment, we gave a set of labeled videos with different emotional experience. The system analyses images and finds similar features. Then the system creates a map, where it arranges videos in accordance with similar features. Thereby, images with similar emotions form certain class.  To test the system, we add other videos and correct the system when it refers them improperly. The proposed model consists of four convolutional layers, followed by max-pooling layers, and three fully-connected layers with a final classificatory presented with MLP (with six basic outputs, corresponding to basic emotions for emotion classification and two outputs for motion classification for typical and non-typical behavior). The input data was presented as infrared camera output.

opencc-by-4.0Jun 2016View details →

BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 16. Social media postings

After different stages of experimentation, we produced different statistical analysis of our results: a. concerning the most accessed social media. It indicated that Panoramio was the most visited, and Google+ had the majority of postings (Figure 16).  The statistics indicate that the different social networks had different levels of impact on the children and that in the future developments the focus should be on those more frequently accessed.  b. concerning the pedagogical content. A survey was done among teachers and children to evaluate the interest in our solutions and the quality of the educational content and therefore of the overall efficiency