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12 results for “backpropagation”
Figure 9. Classification accuracy regardless the ethnic group (Total accuracy 75%)-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Our experiments show that, the impact of ethnic group on the accuracy of emotions<br> recognition is a positive where the accuracy of emotion recognition considering ethnic group is<br> 83.3% as shown in Figure 8, and we got 75% of accuracy regardless ethnic group as shown in<br> Figure 9.</p>
Figure 8. Classification accuracy of emotions considering the ethnic group-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>To study the accuracy of emotion recognition for our approach regardless the ethnic group<br> we used 108 images for the training representing six emotions of six persons. For testing, we used<br> 36 images representing six emotions of six persons.<br> On the other hand, to study the accuracy of emotion recognition for our approach<br> considering the ethnic group we used 36 images for training for each ethnic group representing six<br> emotions of six persons, and test the classifier by using 12 images representing six emotions of six<br> persons.</p>
Figure 6. Interface of FFE program-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Points, Features Extraction and save all input information for the classifier (Features, Ethnic group,<br> Gender and emotion). Figure 6 shows the interface of FFE program.</p>
Figure 7. Samples of MSFDE dataset-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>We performed groups of experiments to study the impact of ethnic group (race) in the<br> accuracy of emotion recognition with three kinds of ethnic groups (Asian, Caucasian as African).<br> So we have three experiments, each experiment has a neural network as a classifier, and each neural<br> network has three layers where there are 16 neurons in the hidden layer except Asian network has<br> 17 neurons (the best result with 17 neurons for Asians).</p>
Figure 3. 46 points are selected on face elements to describe the emotions.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>The number of points and the position of points are not standardized, but it is depending on<br> the features that will be extracted, and used for the classifier. Many researches use various number<br> of points and positions based on their view about the feature to be considered [13] [18] [19]. Figure<br> 3 shows the points we used.</p>
Figure 1. A proposed approach-of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Our proposed approach uses the face expression to detect the emotions through five steps<br> that shows in Figure 1.</p>
Figure. 2. Examples of Angry from different races. (A,B) African. (C,D) Asian. (E,F) Caucasian.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>We chose 46 points which are distributed over human face image and use these points for<br> features extraction. The choice of these points is to determine the shape of each element of the face<br> (eyes, eyebrows and mouth), because the shape of these elements is changeable for each emotion,<br> but these changes are different for each race as shown in Figure 2.</p>
Figure 4. Distance between eyebrow and eye.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Based on what we stated above, we need to extract 28 features, which describe the distances<br> between certain points explained in the previous stage, these features are classified into six groups,<br> and each group describes the features of one face element. All features are a vertical distances<br> between two points. Group one contains seven features for mouth, groups two and three contains 14<br> features for eyes, groups four and five contain six features for eyebrows, and the last group has one<br> feature only which is the distance between the beginning of the eyebrow and the beginning of the<br> eye in same side, this is significant (from point 23 to 15) because it is used to measure the distance<br> of eyebrow from the eye. This feature is shown in Figure 4 by a line.</p>
Figure 5. ANN Structure 4.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>For classification purpose of the emotions, we use ANN of supervised learning based on<br> backpropagation algorithm. Backpropagation neural network architecture is used with its standards<br> learning function with 28 inputs representing the extracted features and 6 outputs representing 6<br> emotions, happy, sad, angry, fear, shame and disgust. the emotions. We have also a hidden layer<br> with 16 nodes selected after various trails to obtain the best results. The used ANN is depicted in<br> Figure 5.</p>
SUMOylation of NaV1.2 channels regulates the velocity of backpropagating action potentials in cortical pyramidal neurons
<p>Voltage-gated sodium channels located in axon initial segments (AIS) trigger action potentials (AP) and play pivotal roles in the excitability of cortical pyramidal neurons. The differential electrophysiological properties and distributions of Na<sub>V</sub>1.2 and Na<sub>V</sub>1.6 channels lead to distinct contributions to AP initiation and backpropagation. While Na<sub>V</sub>1.6 at the distal AIS promotes AP initiation and forward propagation, Na<sub>V</sub>1.2 at the proximal AIS promotes backpropagation of APs to the soma. Here, we show the Small Ubiquitin-like Modifier (SUMO) pathway modulates persistent sodium current (I<sub>NaP</sub>) generation at the AIS to increase neuronal gain and the speed of backpropagation. Since SUMO does not affect Na<sub>V</sub>1.6, these effects were attributed to SUMOylation of Na<sub>V</sub>1.2. Moreover, SUMO effects were absent in a mouse engineered to express Na<sub>V</sub>1.2-Lys38Gln channels that lack the site for SUMO linkage. Thus, SUMOylation of Na<sub>V</sub>1.2 exclusively controls I<sub>NaP</sub> generation and AP backpropagation, thereby playing a prominent role in synaptic integration and plasticity.</p>
SUMOylation of NaV1.2 channels regulates the velocity of backpropagating action potentials in cortical pyramidal neurons
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State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network
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OpenNeuro
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