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activation</title> <meta name="description" content="Search results for: neural activation"> <meta name="keywords" content="neural activation"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="neural activation"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2736</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: neural activation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2736</span> Amplifying Sine Unit-Convolutional Neural Network: An Efficient Deep Architecture for Image Classification and Feature Visualizations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamshaid%20Ul%20Rahman">Jamshaid Ul Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Faiza%20Makhdoom"> Faiza Makhdoom</a>, <a href="https://publications.waset.org/abstracts/search?q=Dianchen%20Lu"> Dianchen Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amplifying%20sine%20unit" title="amplifying sine unit">amplifying sine unit</a>, <a href="https://publications.waset.org/abstracts/search?q=activation%20function" title=" activation function"> activation function</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillatory%20activation" title=" oscillatory activation"> oscillatory activation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=CIFAR-10" title=" CIFAR-10"> CIFAR-10</a> </p> <a href="https://publications.waset.org/abstracts/169054/amplifying-sine-unit-convolutional-neural-network-an-efficient-deep-architecture-for-image-classification-and-feature-visualizations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169054.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2735</span> Artificial Neural Network Speed Controller for Excited DC Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elabed%20Saud">Elabed Saud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Network%20%28ANNs%29" title="Artificial Neural Network (ANNs)">Artificial Neural Network (ANNs)</a>, <a href="https://publications.waset.org/abstracts/search?q=excited%20DC%20motor" title=" excited DC motor"> excited DC motor</a>, <a href="https://publications.waset.org/abstracts/search?q=convenional%20controller" title=" convenional controller"> convenional controller</a>, <a href="https://publications.waset.org/abstracts/search?q=speed%20Controller" title=" speed Controller"> speed Controller</a> </p> <a href="https://publications.waset.org/abstracts/21941/artificial-neural-network-speed-controller-for-excited-dc-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21941.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">726</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2734</span> The Realization of a System’s State Space Based on Markov Parameters by Using Flexible Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Isapour">Ali Isapour</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Nateghi"> Ramin Nateghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> — Markov parameters are unique parameters of the system and remain unchanged under similarity transformations. Markov parameters from a power series that is convergent only if the system matrix’s eigenvalues are inside the unity circle. Therefore, Markov parameters of a stable discrete-time system are convergent. In this study, we aim to realize the system based on Markov parameters by using Artificial Neural Networks (ANN), and this end, we use Flexible Neural Networks. Realization means determining the elements of matrices A, B, C, and D. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Markov%20parameters" title="Markov parameters">Markov parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=realization" title=" realization"> realization</a>, <a href="https://publications.waset.org/abstracts/search?q=activation%20function" title=" activation function"> activation function</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20neural%20network" title=" flexible neural network"> flexible neural network</a> </p> <a href="https://publications.waset.org/abstracts/119535/the-realization-of-a-systems-state-space-based-on-markov-parameters-by-using-flexible-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119535.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">194</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2733</span> An Empirical Study on Switching Activation Functions in Shallow and Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Apoorva%20Vinod">Apoorva Vinod</a>, <a href="https://publications.waset.org/abstracts/search?q=Archana%20Mathur"> Archana Mathur</a>, <a href="https://publications.waset.org/abstracts/search?q=Snehanshu%20Saha"> Snehanshu Saha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Though there exists a plethora of Activation Functions (AFs) used in single and multiple hidden layer Neural Networks (NN), their behavior always raised curiosity, whether used in combination or singly. The popular AFs –Sigmoid, ReLU, and Tanh–have performed prominently well for shallow and deep architectures. Most of the time, AFs are used singly in multi-layered NN, and, to the best of our knowledge, their performance is never studied and analyzed deeply when used in combination. In this manuscript, we experiment with multi-layered NN architecture (both on shallow and deep architectures; Convolutional NN and VGG16) and investigate how well the network responds to using two different AFs (Sigmoid-Tanh, Tanh-ReLU, ReLU-Sigmoid) used alternately against a traditional, single (Sigmoid-Sigmoid, Tanh-Tanh, ReLUReLU) combination. Our results show that using two different AFs, the network achieves better accuracy, substantially lower loss, and faster convergence on 4 computer vision (CV) and 15 Non-CV (NCV) datasets. When using different AFs, not only was the accuracy greater by 6-7%, but we also accomplished convergence twice as fast. We present a case study to investigate the probability of networks suffering vanishing and exploding gradients when using two different AFs. Additionally, we theoretically showed that a composition of two or more AFs satisfies Universal Approximation Theorem (UAT). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activation%20function" title="activation function">activation function</a>, <a href="https://publications.waset.org/abstracts/search?q=universal%20approximation%20function" title=" universal approximation function"> universal approximation function</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence" title=" convergence"> convergence</a> </p> <a href="https://publications.waset.org/abstracts/160024/an-empirical-study-on-switching-activation-functions-in-shallow-and-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160024.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2732</span> Hidden Markov Model for the Simulation Study of Neural States and Intentionality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20B.%20Mishra">R. B. Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hidden Markov Model (HMM) has been used in prediction and determination of states that generate different neural activations as well as mental working conditions. This paper addresses two applications of HMM; one to determine the optimal sequence of states for two neural states: Active (AC) and Inactive (IA) for the three emission (observations) which are for No Working (NW), Waiting (WT) and Working (W) conditions of human beings. Another is for the determination of optimal sequence of intentionality i.e. Believe (B), Desire (D), and Intention (I) as the states and three observational sequences: NW, WT and W. The computational results are encouraging and useful. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hiden%20markov%20model" title="hiden markov model">hiden markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=believe%20desire%20intention" title=" believe desire intention"> believe desire intention</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20activation" title=" neural activation"> neural activation</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/31030/hidden-markov-model-for-the-simulation-study-of-neural-states-and-intentionality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2731</span> Can the Intervention of SCAMPER Bring about Changes of Neural Activation While Taking Creativity Tasks?</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu-Chu%20Yeh">Yu-Chu Yeh</a>, <a href="https://publications.waset.org/abstracts/search?q=WeiChin%20Hsu"> WeiChin Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih-Yen%20Chang"> Chih-Yen Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=creativity" title="creativity">creativity</a>, <a href="https://publications.waset.org/abstracts/search?q=default%20mode%20network" title=" default mode network"> default mode network</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20activation" title=" neural activation"> neural activation</a>, <a href="https://publications.waset.org/abstracts/search?q=SCAMPER" title=" SCAMPER"> SCAMPER</a> </p> <a href="https://publications.waset.org/abstracts/120798/can-the-intervention-of-scamper-bring-about-changes-of-neural-activation-while-taking-creativity-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120798.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">100</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2730</span> Facial Emotion Recognition with Convolutional Neural Network Based Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Koray%20U.%20Erbas">Koray U. Erbas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20based%20FER" title=" deep learning based FER"> deep learning based FER</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20emotion%20recognition" title=" facial emotion recognition"> facial emotion recognition</a> </p> <a href="https://publications.waset.org/abstracts/128197/facial-emotion-recognition-with-convolutional-neural-network-based-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128197.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2729</span> Classification of Echo Signals Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aisulu%20Tileukulova">Aisulu Tileukulova</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhexebay%20Dauren"> Zhexebay Dauren</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radar" title="radar">radar</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=echo%20signals" title=" echo signals"> echo signals</a> </p> <a href="https://publications.waset.org/abstracts/147596/classification-of-echo-signals-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147596.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">353</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2728</span> A Review of Feature Selection Methods Implemented in Neural Stem Cells</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natasha%20Petrovska">Natasha Petrovska</a>, <a href="https://publications.waset.org/abstracts/search?q=Mirjana%20Pavlovic"> Mirjana Pavlovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20M.%20Larrondo-Petrie"> Maria M. Larrondo-Petrie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neural stem cells (NSCs) are multi-potent, self-renewing cells that generate new neurons. Three subtypes of NSCs can be separated regarding the stages of NSC lineage: quiescent neural stem cells (qNSCs), activated neural stem cells (aNSCs) and neural progenitor cells (NPCs), but their gene expression signatures are not utterly understood yet. Single-cell examinations have started to elucidate the complex structure of NSC populations. Nevertheless, there is a lack of thorough molecular interpretation of the NSC lineage heterogeneity and an increasing need for tools to analyze and improve the efficiency and correctness of single-cell sequencing data. Feature selection and ordering can identify and classify the gene expression signatures of these subtypes and can discover novel subpopulations during the NSCs activation and differentiation processes. The aim here is to review the implementation of the feature selection technique on NSC subtypes and the classification techniques that have been used for the identification of gene expression signatures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20similarity" title=" feature similarity"> feature similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20stem%20cells" title=" neural stem cells"> neural stem cells</a>, <a href="https://publications.waset.org/abstracts/search?q=genes" title=" genes"> genes</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection%20methods" title=" feature selection methods"> feature selection methods</a> </p> <a href="https://publications.waset.org/abstracts/163549/a-review-of-feature-selection-methods-implemented-in-neural-stem-cells" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163549.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">152</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2727</span> Artificial Neural Network Regression Modelling of GC/MS Retention of Terpenes Present in Satureja montana Extracts Obtained by Supercritical Carbon Dioxide</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Kova%C4%8Devi%C4%87">Strahinja Kovačević</a>, <a href="https://publications.waset.org/abstracts/search?q=Jelena%20Vladi%C4%87"> Jelena Vladić</a>, <a href="https://publications.waset.org/abstracts/search?q=Senka%20Vidovi%C4%87"> Senka Vidović</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoran%20Zekovi%C4%87"> Zoran Zeković</a>, <a href="https://publications.waset.org/abstracts/search?q=Lidija%20Jevri%C4%87"> Lidija Jevrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanja%20Podunavac%20Kuzmanovi%C4%87"> Sanja Podunavac Kuzmanović</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANN%20regression" title="ANN regression">ANN regression</a>, <a href="https://publications.waset.org/abstracts/search?q=GC%2FMS" title=" GC/MS"> GC/MS</a>, <a href="https://publications.waset.org/abstracts/search?q=Satureja%20montana" title=" Satureja montana"> Satureja montana</a>, <a href="https://publications.waset.org/abstracts/search?q=terpenes" title=" terpenes"> terpenes</a> </p> <a href="https://publications.waset.org/abstracts/2742/artificial-neural-network-regression-modelling-of-gcms-retention-of-terpenes-present-in-satureja-montana-extracts-obtained-by-supercritical-carbon-dioxide" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2742.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">452</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2726</span> A Multi-Output Network with U-Net Enhanced Class Activation Map and Robust Classification Performance for Medical Imaging Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaiden%20Xuan%20Schraut">Jaiden Xuan Schraut</a>, <a href="https://publications.waset.org/abstracts/search?q=Leon%20Liu"> Leon Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yiqiao%20Yin"> Yiqiao Yin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image to-label result provides insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. In order to gain local insight into cancerous regions, separate tasks such as imaging segmentation need to be implemented to aid the doctors in treating patients, which doubles the training time and costs which renders the diagnosis system inefficient and difficult to be accepted by the public. To tackle this issue and drive AI-first medical solutions further, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional convolutional neural networks (CNN) module for auxiliary classification output. Class activation maps are a method of providing insight into a convolutional neural network’s feature maps that leads to its classification but in the case of lung diseases, the region of interest is enhanced by U-net-assisted Class Activation Map (CAM) visualization. Therefore, our proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray’s class activation map to provide a visualization that improves the explainability and is able to generate classification results simultaneously which builds trust for AI-led diagnosis systems. The proposed U-Net model achieves 97.61% accuracy and a dice coefficient of 0.97 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-output%20network%20model" title="multi-output network model">multi-output network model</a>, <a href="https://publications.waset.org/abstracts/search?q=U-net" title=" U-net"> U-net</a>, <a href="https://publications.waset.org/abstracts/search?q=class%20activation%20map" title=" class activation map"> class activation map</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20imaging%20analysis" title=" medical imaging analysis"> medical imaging analysis</a> </p> <a href="https://publications.waset.org/abstracts/155534/a-multi-output-network-with-u-net-enhanced-class-activation-map-and-robust-classification-performance-for-medical-imaging-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155534.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2725</span> Investigating the Neural Heterogeneity of Developmental Dyscalculia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fengjuan%20Wang">Fengjuan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Azilawati%20Jamaludin"> Azilawati Jamaludin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Developmental Dyscalculia (DD) is defined as a particular learning difficulty with continuous challenges in learning requisite math skills that cannot be explained by intellectual disability or educational deprivation. Recent studies have increasingly recognized that DD is a heterogeneous, instead of monolithic, learning disorder with not only cognitive and behavioral deficits but so too neural dysfunction. In recent years, neuroimaging studies employed group comparison to explore the neural underpinnings of DD, which contradicted the heterogenous nature of DD and may obfuscate critical individual differences. This research aimed to investigate the neural heterogeneity of DD using case studies with functional near-infrared spectroscopy (fNIRS). A total of 54 aged 6-7 years old of children participated in this study, comprising two comprehensive cognitive assessments, an 8-minute resting state, and an 8-minute one-digit addition task. Nine children met the criteria of DD and scored at or below 85 (i.e., the 16th percentile) on the Mathematics or Math Fluency subtest of the Wechsler Individual Achievement Test, Third Edition (WIAT-III) (both subtest scores were 90 and below). The remaining 45 children formed the typically developing (TD) group. Resting-state data and brain activation in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), and intraparietal sulcus (IPS) were collected for comparison between each case and the TD group. Graph theory was used to analyze the brain network under the resting state. This theory represents the brain network as a set of nodes--brain regions—and edges—pairwise interactions across areas to reveal the architectural organizations of the nervous network. Next, a single-case methodology developed by Crawford et al. in 2010 was used to compare each case’s brain network indicators and brain activation against 45 TD children’s average data. Results showed that three out of the nine DD children displayed significant deviation from TD children’s brain indicators. Case 1 had inefficient nodal network properties. Case 2 showed inefficient brain network properties and weaker activation in the IFG and IPS areas. Case 3 displayed inefficient brain network properties with no differences in activation patterns. As a rise above, the present study was able to distill differences in architectural organizations and brain activation of DD vis-à-vis TD children using fNIRS and single-case methodology. Although DD is regarded as a heterogeneous learning difficulty, it is noted that all three cases showed lower nodal efficiency in the brain network, which may be one of the neural sources of DD. Importantly, although the current “brain norm” established for the 45 children is tentative, the results from this study provide insights not only for future work in “developmental brain norm” with reliable brain indicators but so too the viability of single-case methodology, which could be used to detect differential brain indicators of DD children for early detection and interventions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20activation" title="brain activation">brain activation</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20network" title=" brain network"> brain network</a>, <a href="https://publications.waset.org/abstracts/search?q=case%20study" title=" case study"> case study</a>, <a href="https://publications.waset.org/abstracts/search?q=developmental%20dyscalculia" title=" developmental dyscalculia"> developmental dyscalculia</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20near-infrared%20spectroscopy" title=" functional near-infrared spectroscopy"> functional near-infrared spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20theory" title=" graph theory"> graph theory</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20heterogeneity" title=" neural heterogeneity"> neural heterogeneity</a> </p> <a href="https://publications.waset.org/abstracts/167059/investigating-the-neural-heterogeneity-of-developmental-dyscalculia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167059.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">53</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2724</span> Further Analysis of Global Robust Stability of Neural Networks with Multiple Time Delays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sabri%20Arik">Sabri Arik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study the global asymptotic robust stability of delayed neural networks with norm-bounded uncertainties. By employing the Lyapunov stability theory and Homeomorphic mapping theorem, we derive some new types of sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with discrete time delays under parameter uncertainties and with respect to continuous and slopebounded activation functions. An important aspect of our results is their low computational complexity as the reported results can be verified by checking some properties symmetric matrices associated with the uncertainty sets of network parameters. The obtained results are shown to be generalization of some of the previously published corresponding results. Some comparative numerical examples are also constructed to compare our results with some closely related existing literature results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=delayed%20systems" title=" delayed systems"> delayed systems</a>, <a href="https://publications.waset.org/abstracts/search?q=lyapunov%20functionals" title=" lyapunov functionals"> lyapunov functionals</a>, <a href="https://publications.waset.org/abstracts/search?q=stability%20analysis" title=" stability analysis"> stability analysis</a> </p> <a href="https://publications.waset.org/abstracts/24118/further-analysis-of-global-robust-stability-of-neural-networks-with-multiple-time-delays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24118.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">528</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2723</span> Investigating the Influence of Activation Functions on Image Classification Accuracy via Deep Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gulfam%20Haider">Gulfam Haider</a>, <a href="https://publications.waset.org/abstracts/search?q=sana%20danish"> sana danish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title="deep neural network">deep neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=optimizers" title=" optimizers"> optimizers</a>, <a href="https://publications.waset.org/abstracts/search?q=RMsprop" title=" RMsprop"> RMsprop</a>, <a href="https://publications.waset.org/abstracts/search?q=ReLU" title=" ReLU"> ReLU</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20gradient%20descent" title=" stochastic gradient descent"> stochastic gradient descent</a> </p> <a href="https://publications.waset.org/abstracts/169078/investigating-the-influence-of-activation-functions-on-image-classification-accuracy-via-deep-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169078.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2722</span> Neural Rendering Applied to Confocal Microscopy Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Li">Daniel Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a novel application of neural rendering methods to confocal microscopy. Neural rendering and implicit neural representations have developed at a remarkable pace, and are prevalent in modern 3D computer vision literature. However, they have not yet been applied to optical microscopy, an important imaging field where 3D volume information may be heavily sought after. In this paper, we employ neural rendering on confocal microscopy focus stack data and share the results. We highlight the benefits and potential of adding neural rendering to the toolkit of microscopy image processing techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20rendering" title="neural rendering">neural rendering</a>, <a href="https://publications.waset.org/abstracts/search?q=implicit%20neural%20representations" title=" implicit neural representations"> implicit neural representations</a>, <a href="https://publications.waset.org/abstracts/search?q=confocal%20microscopy" title=" confocal microscopy"> confocal microscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20image%20processing" title=" medical image processing"> medical image processing</a> </p> <a href="https://publications.waset.org/abstracts/153909/neural-rendering-applied-to-confocal-microscopy-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153909.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">658</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2721</span> The Utilization of Tea Residues for Activated Carbon Preparation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiazhen%20Zhou">Jiazhen Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Youcai%20Zhao"> Youcai Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Waste tea is commonly generated in certain areas of China and its utilization has drawn a lot of concern nowadays. In this paper, highly microporous and mesoporous activated carbons were produced from waste tea by physical activation in the presence of water vapor in a tubular furnace. The effect of activation temperature on yield and pore properties of produced activated carbon are studied. The yield decreased with the increase of activation temperature. According to the Nitrogen adsorption isotherms, the micropore and mesopore are both developed in the activated carbon. The specific surface area and the mesopore volume fractions of the activated carbon increased with the raise of activation temperature. The maximum specific surface area attained 756 m²/g produced at activation temperature 900°C. The results showed that the activation temperature had a significant effect on the micro and mesopore volumes as well as the specific surface area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activated%20carbon" title="activated carbon">activated carbon</a>, <a href="https://publications.waset.org/abstracts/search?q=nitrogen%20adsorption%20isotherm" title=" nitrogen adsorption isotherm"> nitrogen adsorption isotherm</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20activation" title=" physical activation"> physical activation</a>, <a href="https://publications.waset.org/abstracts/search?q=waste%20tea" title=" waste tea"> waste tea</a> </p> <a href="https://publications.waset.org/abstracts/71072/the-utilization-of-tea-residues-for-activated-carbon-preparation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71072.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">328</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2720</span> Research of the Activation Energy of Conductivity in P-I-N SiC Structures Fabricated by Doping with Aluminum Using the Low-Temperature Diffusion Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilkham%20Gafurovich%20Atabaev">Ilkham Gafurovich Atabaev</a>, <a href="https://publications.waset.org/abstracts/search?q=Khimmatali%20Nomozovich%20Juraev"> Khimmatali Nomozovich Juraev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The activation energy of conductivity in p-i-n SiC structures fabricated by doping with Aluminum using the new low-temperature diffusion method is investigated. In this method, diffusion is stimulated by the flux of carbon and silicon vacancies created by surface oxidation. The activation energy of conductivity in the p - layer is 0.25 eV and it is close to the ionization energy of Aluminum in 4H-SiC from 0.21 to 0.27 eV for the hexagonal and cubic positions of aluminum in the silicon sublattice for weakly doped crystals. The conductivity of the i-layer (measured in the reverse biased diode) shows 2 activation energies: 0.02 eV and 0.62 eV. Apparently, the 0.62 eV level is a deep trap level and it is a complex of Aluminum with a vacancy. According to the published data, an analogous level system (with activation energies of 0.05, 0.07, 0.09 and 0.67 eV) was observed in the ion Aluminum doped 4H-SiC samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activation%20energy" title="activation energy">activation energy</a>, <a href="https://publications.waset.org/abstracts/search?q=aluminum" title=" aluminum"> aluminum</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20temperature%20diffusion" title=" low temperature diffusion"> low temperature diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=SiC" title=" SiC"> SiC</a> </p> <a href="https://publications.waset.org/abstracts/74668/research-of-the-activation-energy-of-conductivity-in-p-i-n-sic-structures-fabricated-by-doping-with-aluminum-using-the-low-temperature-diffusion-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74668.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">279</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2719</span> Modelling Fluoride Pollution of Groundwater Using Artificial Neural Network in the Western Parts of Jharkhand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neeta%20Kumari">Neeta Kumari</a>, <a href="https://publications.waset.org/abstracts/search?q=Gopal%20Pathak"> Gopal Pathak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20neural%20network%20%28ANN%29" title="Artificial neural network (ANN)">Artificial neural network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=FFN%20%28Feed-forward%20network%29" title=" FFN (Feed-forward network)"> FFN (Feed-forward network)</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation%20algorithm" title=" backpropagation algorithm"> backpropagation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Levenberg-Marquardt%20algorithm" title=" Levenberg-Marquardt algorithm"> Levenberg-Marquardt algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater%20fluoride%20contamination" title=" groundwater fluoride contamination"> groundwater fluoride contamination</a> </p> <a href="https://publications.waset.org/abstracts/19324/modelling-fluoride-pollution-of-groundwater-using-artificial-neural-network-in-the-western-parts-of-jharkhand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19324.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">550</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2718</span> Influence of the Refractory Period on Neural Networks Based on the Recognition of Neural Signatures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Luis%20Carrillo-Medina">José Luis Carrillo-Medina</a>, <a href="https://publications.waset.org/abstracts/search?q=Roberto%20Latorre"> Roberto Latorre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20signature" title="neural signature">neural signature</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20fingerprint" title=" neural fingerprint"> neural fingerprint</a>, <a href="https://publications.waset.org/abstracts/search?q=processing%20based%20on%20signal%20identification" title=" processing based on signal identification"> processing based on signal identification</a>, <a href="https://publications.waset.org/abstracts/search?q=self-organizing%20neural%20network" title=" self-organizing neural network"> self-organizing neural network</a> </p> <a href="https://publications.waset.org/abstracts/20408/influence-of-the-refractory-period-on-neural-networks-based-on-the-recognition-of-neural-signatures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20408.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">492</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2717</span> Nonparametric Sieve Estimation with Dependent Data: Application to Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chad%20Brown">Chad Brown</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sieve%20extremum%20estimates" title="sieve extremum estimates">sieve extremum estimates</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20estimation" title=" nonparametric estimation"> nonparametric estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=rectified%20linear%20unit" title=" rectified linear unit"> rectified linear unit</a>, <a href="https://publications.waset.org/abstracts/search?q=nonstationary%20processes" title=" nonstationary processes"> nonstationary processes</a> </p> <a href="https://publications.waset.org/abstracts/186727/nonparametric-sieve-estimation-with-dependent-data-application-to-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186727.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">43</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2716</span> Neural Correlates of Decision-Making Under Ambiguity and Conflict </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Helen%20Pushkarskaya">Helen Pushkarskaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Smithson"> Michael Smithson</a>, <a href="https://publications.waset.org/abstracts/search?q=Jane%20E.%20Joseph"> Jane E. Joseph</a>, <a href="https://publications.waset.org/abstracts/search?q=Christine%20Corbly"> Christine Corbly</a>, <a href="https://publications.waset.org/abstracts/search?q=Ifat%20Levy"> Ifat Levy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Studies of decision making under uncertainty generally focus on imprecise information about outcome probabilities (“ambiguity”). It is not clear, however, whether conflicting information about outcome probabilities affects decision making in the same manner as ambiguity does. Here we combine functional Magnetic Resonance Imaging (fMRI) and a simple gamble design to study this question. In this design, the levels of ambiguity and conflict are parametrically varied, and ambiguity and conflict gambles are matched on both expected value and variance. Behaviorally, participants avoided conflict more than ambiguity, and attitudes toward ambiguity and conflict did not correlate across subjects. Neurally, regional brain activation was differentially modulated by ambiguity level and aversion to ambiguity and by conflict level and aversion to conflict. Activation in the medial prefrontal cortex was correlated with the level of ambiguity and with ambiguity aversion, whereas activation in the ventral striatum was correlated with the level of conflict and with conflict aversion. This novel double dissociation indicates that decision makers process imprecise and conflicting information differently, a finding that has important implications for basic and clinical research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title="decision making">decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=ambiguity" title=" ambiguity"> ambiguity</a>, <a href="https://publications.waset.org/abstracts/search?q=conflict" title=" conflict"> conflict</a>, <a href="https://publications.waset.org/abstracts/search?q=fMRI" title=" fMRI"> fMRI</a> </p> <a href="https://publications.waset.org/abstracts/27681/neural-correlates-of-decision-making-under-ambiguity-and-conflict" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27681.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">564</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2715</span> Improving Cyclability and Capacity of Lithium Oxygen Batteries via Low Rate Pre-Activation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhihong%20Luo">Zhihong Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Guangbin%20Zhu"> Guangbin Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Lulu%20Guo"> Lulu Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhujun%20Lyu"> Zhujun Lyu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kun%20Luo"> Kun Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cycling life has become the threshold for the prospective application of Li-O₂ batteries, and the protection of Li anode has recently regarded as the key factor to the performance. Herein, a simple low rate pre-activation (20 cycles at 0.5 Ag⁻¹ and a capacity of 200 mAh g⁻¹) was employed to effectively improve the performance and cyclability of Li-O₂ batteries. The charge/discharge cycles at 1 A g⁻¹ with a capacity of 1000 mAh g⁻¹ were maintained for up to 290 times versus 55 times for the cell without pre-activation. The ultimate battery capacity and high rate discharge property were also largely enhanced. Morphology, XRD and XPS analyses reveal that the performance improvement is in close association with the formation of the smooth and compact surface layer formed on the Li anode after low rate pre-activation, which apparently alleviated the corrosion of Li anode and the passivation of cathode during battery cycling, and the corresponding mechanism was also discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lithium%20oxygen%20battery" title="lithium oxygen battery">lithium oxygen battery</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-activation" title=" pre-activation"> pre-activation</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclability" title=" cyclability"> cyclability</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity" title=" capacity"> capacity</a> </p> <a href="https://publications.waset.org/abstracts/103398/improving-cyclability-and-capacity-of-lithium-oxygen-batteries-via-low-rate-pre-activation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103398.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2714</span> Evaluating Generative Neural Attention Weights-Based Chatbot on Customer Support Twitter Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sinarwati%20Mohamad%20Suhaili">Sinarwati Mohamad Suhaili</a>, <a href="https://publications.waset.org/abstracts/search?q=Naomie%20Salim"> Naomie Salim</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Nazim%20Jambli"> Mohamad Nazim Jambli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sequence-to-sequence (seq2seq) models augmented with attention mechanisms are playing an increasingly important role in automated customer service. These models, which are able to recognize complex relationships between input and output sequences, are crucial for optimizing chatbot responses. Central to these mechanisms are neural attention weights that determine the focus of the model during sequence generation. Despite their widespread use, there remains a gap in the comparative analysis of different attention weighting functions within seq2seq models, particularly in the domain of chatbots using the Customer Support Twitter (CST) dataset. This study addresses this gap by evaluating four distinct attention-scoring functions—dot, multiplicative/general, additive, and an extended multiplicative function with a tanh activation parameter — in neural generative seq2seq models. Utilizing the CST dataset, these models were trained and evaluated over 10 epochs with the AdamW optimizer. Evaluation criteria included validation loss and BLEU scores implemented under both greedy and beam search strategies with a beam size of k=3. Results indicate that the model with the tanh-augmented multiplicative function significantly outperforms its counterparts, achieving the lowest validation loss (1.136484) and the highest BLEU scores (0.438926 under greedy search, 0.443000 under beam search, k=3). These results emphasize the crucial influence of selecting an appropriate attention-scoring function in improving the performance of seq2seq models for chatbots. Particularly, the model that integrates tanh activation proves to be a promising approach to improve the quality of chatbots in the customer support context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attention%20weight" title="attention weight">attention weight</a>, <a href="https://publications.waset.org/abstracts/search?q=chatbot" title=" chatbot"> chatbot</a>, <a href="https://publications.waset.org/abstracts/search?q=encoder-decoder" title=" encoder-decoder"> encoder-decoder</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20generative%20attention" title=" neural generative attention"> neural generative attention</a>, <a href="https://publications.waset.org/abstracts/search?q=score%20function" title=" score function"> score function</a>, <a href="https://publications.waset.org/abstracts/search?q=sequence-to-sequence" title=" sequence-to-sequence"> sequence-to-sequence</a> </p> <a href="https://publications.waset.org/abstracts/176622/evaluating-generative-neural-attention-weights-based-chatbot-on-customer-support-twitter-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176622.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2713</span> A Study on the Strategy for Domestic Space Industry Activation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hangil%20Park">Hangil Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Hwayeon%20Song"> Hwayeon Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Jingyung%20Sim"> Jingyung Sim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a business ecosystem of a domestic space industry is comprehensively analyzed to derive the influence factors. The priority level of each element as well as the disparity between the ideal and reality are investigated through a literature review and an expert survey. The three major influence factors determined are: (a) investment scale and approach, (b) propulsion system, and (c) industrialization with overseas expansion. Related issues based on the current status are evaluated, followed by a proposed activation strategy. This research's findings offer a direction for R&D budget allocation and law system maintenance for the activation of the domestic space industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=space%20industry" title="space industry">space industry</a>, <a href="https://publications.waset.org/abstracts/search?q=activation" title=" activation"> activation</a>, <a href="https://publications.waset.org/abstracts/search?q=strategy" title=" strategy"> strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20ecosystem" title=" business ecosystem"> business ecosystem</a> </p> <a href="https://publications.waset.org/abstracts/9226/a-study-on-the-strategy-for-domestic-space-industry-activation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9226.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">368</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2712</span> The Effect of Deformation Activation Volume, Strain Rate Sensitivity and Processing Temperature of Grain Size Variants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20B.%20Sob">P. B. Sob</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Alugongo"> A. A. Alugongo</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20B.%20Tengen"> T. B. Tengen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The activation volume of 6082T6 aluminum is investigated at different temperatures on grain size variants. The deformation activation volume was computed on the basis of the relationship between the Boltzmann’s constant k, the testing temperatures, the material strain rate sensitivity and the material yield stress of grain size variants. The material strain rate sensitivity is computed as a function of yield stress and strain rate of grain size variants. The effect of the material strain rate sensitivity and the deformation activation volume of 6082T6 aluminum at different temperatures of 3-D grain are discussed. It is shown that the strain rate sensitivities and activation volume are negative for the grain size variants during the deformation of nanostructured materials. It is also observed that the activation volume vary in different ways with the equivalent radius, semi minor axis radius, semi major axis radius and major axis radius. From the obtained results it is shown that the variation of activation volume increased and decreased with the testing temperature. It was revealed that, increased in strain rate sensitivity led to decrease in activation volume whereas increased in activation volume led to decrease in strain rate sensitivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nanostructured%20materials" title="nanostructured materials">nanostructured materials</a>, <a href="https://publications.waset.org/abstracts/search?q=grain%20size%20variants" title=" grain size variants"> grain size variants</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature" title=" temperature"> temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=yield%20stress" title=" yield stress"> yield stress</a>, <a href="https://publications.waset.org/abstracts/search?q=strain%20rate%20sensitivity" title=" strain rate sensitivity"> strain rate sensitivity</a>, <a href="https://publications.waset.org/abstracts/search?q=activation%20volume" title=" activation volume"> activation volume</a> </p> <a href="https://publications.waset.org/abstracts/39079/the-effect-of-deformation-activation-volume-strain-rate-sensitivity-and-processing-temperature-of-grain-size-variants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">251</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2711</span> A Multilayer Perceptron Neural Network Model Optimized by Genetic Algorithm for Significant Wave Height Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luis%20C.%20Parra">Luis C. Parra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=significant%20wave%20height" title="significant wave height">significant wave height</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20optimization" title=" machine learning optimization"> machine learning optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20neural%20networks" title=" multilayer perceptron neural networks"> multilayer perceptron neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/153526/a-multilayer-perceptron-neural-network-model-optimized-by-genetic-algorithm-for-significant-wave-height-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">107</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2710</span> Solving the Quadratic Programming Problem Using a Recurrent Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Behroozpoor">A. A. Behroozpoor</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20M.%20Mazarei"> M. M. Mazarei </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a fuzzy recurrent neural network is proposed for solving the classical quadratic control problem subject to linear equality and bound constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=REFERENCES%20%20%0D%0A%5B1%5D%09Xia" title="REFERENCES [1] Xia">REFERENCES [1] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y" title=" Y"> Y</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20new%20neural%20network%20for%20solving%20linear%20and%20quadratic%20programming%20problems.%20IEEE%20Transactions%20on%20Neural%20Networks" title=" A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks"> A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=7%286%29" title=" 7(6)"> 7(6)</a>, <a href="https://publications.waset.org/abstracts/search?q=1996" title=" 1996"> 1996</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.1544%E2%80%931548.%0D%0A%5B2%5D%09Xia" title=" pp.1544–1548. [2] Xia"> pp.1544–1548. [2] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y." title=" Y."> Y.</a>, <a href="https://publications.waset.org/abstracts/search?q=%26%20Wang" title=" & Wang"> & Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=J" title=" J"> J</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20recurrent%20neural%20network%20for%20solving%20nonlinear%20convex%20programs%20subject%20to%20linear%20constraints.%20IEEE%20Transactions%20on%20Neural%20Networks" title=" A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks"> A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=16%282%29" title="16(2)">16(2)</a>, <a href="https://publications.waset.org/abstracts/search?q=2005" title=" 2005"> 2005</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.%20379%E2%80%93386.%0D%0A%5B3%5D%09Xia" title=" pp. 379–386. [3] Xia"> pp. 379–386. [3] Xia</a>, <a href="https://publications.waset.org/abstracts/search?q=Y." title=" Y."> Y.</a>, <a href="https://publications.waset.org/abstracts/search?q=H" title=" H"> H</a>, <a href="https://publications.waset.org/abstracts/search?q=Leung" title=" Leung"> Leung</a>, <a href="https://publications.waset.org/abstracts/search?q=%26%20J" title=" & J"> & J</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang" title=" Wang"> Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20projection%20neural%20network%20and%20its%20application%20to%20constrained%20optimization%20problems.%20IEEE%20Transactions%20Circuits%20and%20Systems-I" title=" A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I"> A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I</a>, <a href="https://publications.waset.org/abstracts/search?q=49%284%29" title=" 49(4)"> 49(4)</a>, <a href="https://publications.waset.org/abstracts/search?q=2002" title=" 2002"> 2002</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.447%E2%80%93458.B.%20%0D%0A%5B4%5D%09Q.%20Liu" title=" pp.447–458.B. [4] Q. Liu"> pp.447–458.B. [4] Q. Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Guo" title=" Z. Guo"> Z. Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Wang" title=" J. Wang"> J. Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=A%20one-layer%20recurrent%20neural%20network%20for%20constrained%20seudoconvex%20optimization%20and%20its%20application%20for%20dynamic%20portfolio%20optimization.%20Neural%20Networks" title=" A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks"> A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks</a>, <a href="https://publications.waset.org/abstracts/search?q=26" title=" 26"> 26</a>, <a href="https://publications.waset.org/abstracts/search?q=2012" title=" 2012"> 2012</a>, <a href="https://publications.waset.org/abstracts/search?q=pp.%2099-109." title=" pp. 99-109. "> pp. 99-109. </a> </p> <a href="https://publications.waset.org/abstracts/19435/solving-the-quadratic-programming-problem-using-a-recurrent-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19435.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">644</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2709</span> Impact of Neuron with Two Dendrites in Heart Behavior</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kaouther%20Selmi">Kaouther Selmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Alaeddine%20Sridi"> Alaeddine Sridi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Bouallegue"> Mohamed Bouallegue</a>, <a href="https://publications.waset.org/abstracts/search?q=Kais%20Bouallegue"> Kais Bouallegue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neurons are the fundamental units of the brain and the nervous system. The variable structure model of neurons consists of a system of differential equations with various parameters. By optimizing these parameters, we can create a unique model that describes the dynamic behavior of a single neuron. We introduce a neural network based on neurons with multiple dendrites employing an activation function with a variable structure. In this paper, we present a model for heart behavior. Finally, we showcase our successful simulation of the heart's ECG diagram using our Variable Structure Neuron Model (VSMN). This result could provide valuable insights into cardiology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron" title=" neuron"> neuron</a>, <a href="https://publications.waset.org/abstracts/search?q=dendrites" title=" dendrites"> dendrites</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20behavior" title=" heart behavior"> heart behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a> </p> <a href="https://publications.waset.org/abstracts/170171/impact-of-neuron-with-two-dendrites-in-heart-behavior" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170171.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2708</span> Selecting the Best RBF Neural Network Using PSO Algorithm for ECG Signal Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20Mohsenifar">Najmeh Mohsenifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Narjes%20Mohsenifar"> Narjes Mohsenifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Abbas%20Kargar"> Abbas Kargar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF%20artificial%20neural%20network" title=" RBF artificial neural network"> RBF artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO%20algorithm" title=" PSO algorithm"> PSO algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=predict" title=" predict"> predict</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a> </p> <a href="https://publications.waset.org/abstracts/33466/selecting-the-best-rbf-neural-network-using-pso-algorithm-for-ecg-signal-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33466.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">627</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2707</span> Saturation Misbehavior and Field Activation of the Mobility in Polymer-Based OTFTs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Giraudet">L. Giraudet</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20Simonetti"> O. Simonetti</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20de%20Tournadre"> G. de Tournadre</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Dumeli%C3%A9"> N. Dumelié</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Clarenc"> B. Clarenc</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Reisdorffer"> F. Reisdorffer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we intend to give a comprehensive view of the saturation misbehavior of thin film transistors (TFTs) based on disordered semiconductors, such as most organic TFTs, and its link to the field activation of the mobility. Experimental evidence of the field activation of the mobility is given for disordered semiconductor based TFTs, when reducing the gate length. Saturation misbehavior is observed simultaneously. Advanced transport models have been implemented in a quasi-2D numerical TFT simulation software. From the numerical simulations it is clearly established that field activation of the mobility alone cannot explain the saturation misbehavior. Evidence is given that high longitudinal field gradient at the drain end of the channel is responsible for an excess charge accumulation, preventing saturation. The two combined effects allow reproducing the experimental output characteristics of short channel TFTs, with S-shaped characteristics and saturation failure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobility%20field%20activation" title="mobility field activation">mobility field activation</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title=" numerical simulation"> numerical simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=OTFT" title=" OTFT"> OTFT</a>, <a href="https://publications.waset.org/abstracts/search?q=saturation%20failure" title=" saturation failure "> saturation failure </a> </p> <a href="https://publications.waset.org/abstracts/19411/saturation-misbehavior-and-field-activation-of-the-mobility-in-polymer-based-otfts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19411.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">520</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=neural%20activation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=neural%20activation&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=neural%20activation&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=neural%20activation&page=5">5</a></li> <li class="page-item"><a class="page-link" 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