CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: RBF neural networks</title> <meta name="description" content="Search results for: RBF neural networks"> <meta name="keywords" content="RBF neural networks"> <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 navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="RBF neural networks" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" 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="RBF neural networks"> <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> 3734</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: RBF neural networks</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3734</span> Delay-Dependent Passivity Analysis for Neural Networks with Time-Varying Delays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Y.%20Jung">H. Y. Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Jing%20Wang"> Jing Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20H.%20Park"> J. H. Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Shen"> Hao Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This brief addresses the passivity problem for neural networks with time-varying delays. The aim is focus on establishing the passivity condition of the considered neural networks. <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=passivity%20analysis" title=" passivity analysis"> passivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying%20delays" title=" time-varying delays"> time-varying delays</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20matrix%20inequality" title=" linear matrix inequality"> linear matrix inequality</a> </p> <a href="https://publications.waset.org/abstracts/3026/delay-dependent-passivity-analysis-for-neural-networks-with-time-varying-delays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3026.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">570</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">3733</span> Artificial Neural Networks in Environmental Psychology: Application in Architectural Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20De%20Almeida%20Pereira">Diego De Almeida Pereira</a>, <a href="https://publications.waset.org/abstracts/search?q=Diana%20Borchenko"> Diana Borchenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks are used for many applications as they are able to learn complex nonlinear relationships between input and output data. As the number of neurons and layers in a neural network increases, it is possible to represent more complex behaviors. The present study proposes that artificial neural networks are a valuable tool for architecture and engineering professionals concerned with understanding how buildings influence human and social well-being based on theories of environmental psychology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environmental%20psychology" title="environmental psychology">environmental psychology</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture" title=" architecture"> architecture</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=human%20and%20social%20well-being" title=" human and social well-being"> human and social well-being</a> </p> <a href="https://publications.waset.org/abstracts/147521/artificial-neural-networks-in-environmental-psychology-application-in-architectural-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147521.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">496</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">3732</span> Using Gene Expression Programming in Learning Process of Rough Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanaa%20Rashed%20Abdallah">Sanaa Rashed Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasser%20F.%20Hassan"> Yasser F. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough%20sets" title="rough sets">rough sets</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20programming" title=" gene expression programming"> gene expression programming</a>, <a href="https://publications.waset.org/abstracts/search?q=rough%20neural%20networks" title=" rough neural networks"> rough neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/41805/using-gene-expression-programming-in-learning-process-of-rough-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41805.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">383</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">3731</span> Study of the Use of Artificial Neural Networks in Islamic Finance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kaoutar%20Abbahaddou">Kaoutar Abbahaddou</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Salah%20Chiadmi"> Mohammed Salah Chiadmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The need to find a relevant way to predict the next-day price of a stock index is a real concern for many financial stakeholders and researchers. We have known across years the proliferation of several methods. Nevertheless, among all these methods, the most controversial one is a machine learning algorithm that claims to be reliable, namely neural networks. Thus, the purpose of this article is to study the prediction power of neural networks in the particular case of Islamic finance as it is an under-looked area. In this article, we will first briefly present a review of the literature regarding neural networks and Islamic finance. Next, we present the architecture and principles of artificial neural networks most commonly used in finance. Then, we will show its empirical application on two Islamic stock indexes. The accuracy rate would be used to measure the performance of the algorithm in predicting the right price the next day. As a result, we can conclude that artificial neural networks are a reliable method to predict the next-day price for Islamic indices as it is claimed for conventional ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Islamic%20finance" title="Islamic finance">Islamic finance</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20price%20prediction" title=" stock price prediction"> stock price prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/142047/study-of-the-use-of-artificial-neural-networks-in-islamic-finance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142047.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">237</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">3730</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">626</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">3729</span> Using Artificial Neural Networks for Optical Imaging of Fluorescent Biomarkers </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20A.%20Laptinskiy">K. A. Laptinskiy</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Burikov"> S. A. Burikov</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20M.%20Vervald"> A. M. Vervald</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Dolenko"> S. A. Dolenko</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20A.%20Dolenko"> T. A. Dolenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article presents the results of the application of artificial neural networks to separate the fluorescent contribution of nanodiamonds used as biomarkers, adsorbents and carriers of drugs in biomedicine, from a fluorescent background of own biological fluorophores. The principal possibility of solving this problem is shown. Use of neural network architecture let to detect fluorescence of nanodiamonds against the background autofluorescence of egg white with high accuracy - better than 3 ug/ml. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=fluorescence" title=" fluorescence"> fluorescence</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20aggregation" title=" data aggregation"> data aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=biomarkers" title=" biomarkers"> biomarkers</a> </p> <a href="https://publications.waset.org/abstracts/14494/using-artificial-neural-networks-for-optical-imaging-of-fluorescent-biomarkers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14494.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">710</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">3728</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">3727</span> A Review on Artificial Neural Networks in Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Afsharipoor">B. Afsharipoor</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Nazemi"> E. Nazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented. <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=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20understanding" title=" image understanding"> image understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=MANN" title=" MANN"> MANN</a> </p> <a href="https://publications.waset.org/abstracts/36843/a-review-on-artificial-neural-networks-in-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36843.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">406</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">3726</span> Applications of Artificial Neural Networks in Civil Engineering </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naci%20B%C3%BCy%C3%BCkkarac%C4%B1%C4%9Fan">Naci Büyükkaracığan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks (ANN) is an electrical model based on the human brain nervous system and working principle. Artificial neural networks have been the subject of an active field of research that has matured greatly over the past 55 years. ANN now is used in many fields. But, it has been viewed that artificial neural networks give better results in particular optimization and control systems. There are requirements of optimization and control system in many of the area forming the subject of civil engineering applications. In this study, the first artificial intelligence systems are widely used in the solution of civil engineering systems were examined with the basic principles and technical aspects. Finally, the literature reviews for applications in the field of civil engineering were conducted and also artificial intelligence techniques were informed about the study and its results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=civil%20engineering" title=" civil engineering"> civil engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuzzy%20logic" title=" Fuzzy logic"> Fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=statistics" title=" statistics"> statistics</a> </p> <a href="https://publications.waset.org/abstracts/29908/applications-of-artificial-neural-networks-in-civil-engineering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29908.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">412</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">3725</span> Demand Forecasting Using Artificial Neural Networks Optimized by Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daham%20Owaid%20Matrood">Daham Owaid Matrood</a>, <a href="https://publications.waset.org/abstracts/search?q=Naqaa%20Hussein%20Raheem"> Naqaa Hussein Raheem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20forecasting" title=" demand forecasting"> demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=weight%20optimization" title=" weight optimization"> weight optimization</a> </p> <a href="https://publications.waset.org/abstracts/45069/demand-forecasting-using-artificial-neural-networks-optimized-by-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45069.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">3724</span> Prediction of Wind Speed by Artificial Neural Networks for Energy Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Adjiri-Bailiche">S. Adjiri-Bailiche</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Boudia"> S. M. Boudia</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Daaou"> H. Daaou</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Hadouche"> S. Hadouche</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Benzaoui"> A. Benzaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title="MATLAB">MATLAB</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=power%20low" title=" power low"> power low</a>, <a href="https://publications.waset.org/abstracts/search?q=vertical%20extrapolation" title=" vertical extrapolation"> vertical extrapolation</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20energy" title=" wind energy"> wind energy</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20speed" title=" wind speed "> wind speed </a> </p> <a href="https://publications.waset.org/abstracts/17635/prediction-of-wind-speed-by-artificial-neural-networks-for-energy-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17635.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">692</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">3723</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">3722</span> Improvement of Ground Truth Data for Eye Location on Infrared Driver Recordings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sorin%20Valcan">Sorin Valcan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihail%20Gaianu"> Mihail Gaianu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Labeling is a very costly and time consuming process which aims to generate datasets for training neural networks in several functionalities and projects. For driver monitoring system projects, the need for labeled images has a significant impact on the budget and distribution of effort. This paper presents the modifications done to an algorithm used for the generation of ground truth data for 2D eyes location on infrared images with drivers in order to improve the quality of the data and performance of the trained neural networks. The algorithm restrictions become tougher, which makes it more accurate but also less constant. The resulting dataset becomes smaller and shall not be altered by any kind of manual label adjustment before being used in the neural networks training process. These changes resulted in a much better performance of the trained neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=labeling%20automation" title="labeling automation">labeling automation</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared%20camera" title=" infrared camera"> infrared camera</a>, <a href="https://publications.waset.org/abstracts/search?q=driver%20monitoring" title=" driver monitoring"> driver monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=eye%20detection" title=" eye detection"> eye detection</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/148969/improvement-of-ground-truth-data-for-eye-location-on-infrared-driver-recordings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148969.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">117</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">3721</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">3720</span> Robotic Arm Control with Neural Networks Using Genetic Algorithm Optimization Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arbnor%20Pajaziti">Arbnor Pajaziti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Cana"> Hasan Cana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robotic%20arm" title="robotic arm">robotic arm</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=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/7408/robotic-arm-control-with-neural-networks-using-genetic-algorithm-optimization-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7408.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">523</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">3719</span> Clustering the Wheat Seeds Using SOM Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salah%20Ghamari">Salah Ghamari </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the ability of self organizing map artificial (SOM) neural networks in clustering the wheat seeds varieties according to morphological properties of them was considered. The SOM is one type of unsupervised competitive learning. Experimentally, five morphological features of 300 seeds (including three varieties: gaskozhen, Md and sardari) were obtained using image processing technique. The results show that the artificial neural network has a good performance (90.33% accuracy) in classification of the wheat varieties despite of high similarity in them. The highest classification accuracy (100%) was achieved for sardari. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=self%20organizing%20map" title=" self organizing map"> self organizing map</a>, <a href="https://publications.waset.org/abstracts/search?q=wheat%20variety" title=" wheat variety"> wheat variety</a> </p> <a href="https://publications.waset.org/abstracts/33833/clustering-the-wheat-seeds-using-som-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33833.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">656</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">3718</span> Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Mousavian">S. Mousavian</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Ashouri"> D. Ashouri</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Mousavian"> F. Mousavian</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Nikkhah%20Rashidabad"> V. Nikkhah Rashidabad</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Ghazinia"> N. Ghazinia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> PH, temperature, and time of extraction of each stage, agitation speed, and delay time between stages effect on efficiency of zinc extraction from concentrate. In this research, efficiency of zinc extraction was predicted as a function of mentioned variable by artificial neural networks (ANN). ANN with different layer was employed and the result show that the networks with 8 neurons in hidden layer has good agreement with experimental data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=zinc%20extraction" title="zinc extraction">zinc extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency" title=" efficiency"> efficiency</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=operating%20condition" title=" operating condition"> operating condition</a> </p> <a href="https://publications.waset.org/abstracts/2475/modeling-and-prediction-of-zinc-extraction-efficiency-from-concentrate-by-operating-condition-and-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2475.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">545</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">3717</span> Data Mining of Students' Performance Using Artificial Neural Network: Turkish Students as a Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samuel%20Nii%20Tackie">Samuel Nii Tackie</a>, <a href="https://publications.waset.org/abstracts/search?q=Oyebade%20K.%20Oyedotun"> Oyebade K. Oyedotun</a>, <a href="https://publications.waset.org/abstracts/search?q=Ebenezer%20O.%20Olaniyi"> Ebenezer O. Olaniyi</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnan%20Khashman"> Adnan Khashman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=students%E2%80%99%20evaluation" title=" students’ evaluation"> students’ evaluation</a> </p> <a href="https://publications.waset.org/abstracts/25099/data-mining-of-students-performance-using-artificial-neural-network-turkish-students-as-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25099.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">613</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">3716</span> Neural Networks with Different Initialization Methods for Depression Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tianle%20Yang">Tianle Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a common mental disorder, depression is a leading cause of various diseases worldwide. Early detection and treatment of depression can dramatically promote remission and prevent relapse. However, conventional ways of depression diagnosis require considerable human effort and cause economic burden, while still being prone to misdiagnosis. On the other hand, recent studies report that physical characteristics are major contributors to the diagnosis of depression, which inspires us to mine the internal relationship by neural networks instead of relying on clinical experiences. In this paper, neural networks are constructed to predict depression from physical characteristics. Two initialization methods are examined - Xaiver and Kaiming initialization. Experimental results show that a 3-layers neural network with Kaiming initialization achieves 83% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=depression" title="depression">depression</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=Xavier%20initialization" title=" Xavier initialization"> Xavier initialization</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaiming%20initialization" title=" Kaiming initialization"> Kaiming initialization</a> </p> <a href="https://publications.waset.org/abstracts/150440/neural-networks-with-different-initialization-methods-for-depression-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150440.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">128</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">3715</span> Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Su">Ying Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Morgan%20C.%20Wang"> Morgan C. Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Long-term time series forecasting is an important research area for automated machine learning (AutoML). Currently, forecasting based on either machine learning or statistical learning is usually built by experts, and it requires significant manual effort, from model construction, feature engineering, and hyper-parameter tuning to the construction of the time series model. Automation is not possible since there are too many human interventions. To overcome these limitations, this article proposed to use recurrent neural networks (RNN) through the memory state of RNN to perform long-term time series prediction. We have shown that this proposed approach is better than the traditional Autoregressive Integrated Moving Average (ARIMA). In addition, we also found it is better than other network systems, including Fully Connected Neural Networks (FNN), Convolutional Neural Networks (CNN), and Nonpooling Convolutional Neural Networks (NPCNN). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20machines%20learning" title="automated machines learning">automated machines learning</a>, <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20integrated%20moving%20average" title=" autoregressive integrated moving average"> autoregressive integrated moving average</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=time%20series%20analysis" title=" time series analysis"> time series analysis</a> </p> <a href="https://publications.waset.org/abstracts/173817/automated-machine-learning-algorithm-using-recurrent-neural-network-to-perform-long-term-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173817.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">105</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">3714</span> Long Short-Time Memory Neural Networks for Human Driving Behavior Modelling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lu%20Zhao">Lu Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadir%20Farhi"> Nadir Farhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeltsin%20Valero"> Yeltsin Valero</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoi%20Christoforou"> Zoi Christoforou</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Haddadou"> Nadia Haddadou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic%20modeling" title="traffic modeling">traffic modeling</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=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=car-following" title=" car-following"> car-following</a>, <a href="https://publications.waset.org/abstracts/search?q=lane-change" title=" lane-change"> lane-change</a> </p> <a href="https://publications.waset.org/abstracts/139730/long-short-time-memory-neural-networks-for-human-driving-behavior-modelling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139730.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">261</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">3713</span> A t-SNE and UMAP Based Neural Network Image Classification Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shelby%20Simpson">Shelby Simpson</a>, <a href="https://publications.waset.org/abstracts/search?q=William%20Stanley"> William Stanley</a>, <a href="https://publications.waset.org/abstracts/search?q=Namir%20Naba"> Namir Naba</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaodi%20Wang"> Xiaodi Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=t-SNE" title="t-SNE">t-SNE</a>, <a href="https://publications.waset.org/abstracts/search?q=UMAP" title=" UMAP"> UMAP</a>, <a href="https://publications.waset.org/abstracts/search?q=fashion%20MNIST" title=" fashion MNIST"> fashion MNIST</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/137765/a-t-sne-and-umap-based-neural-network-image-classification-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137765.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">198</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">3712</span> Optimization of Vertical Axis Wind Turbine Based on Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Affanuddin%20H.%20Siddique">Mohammed Affanuddin H. Siddique</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayesh%20S.%20Shukla"> Jayesh S. Shukla</a>, <a href="https://publications.waset.org/abstracts/search?q=Chetan%20B.%20Meshram"> Chetan B. Meshram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VAWT" title="VAWT">VAWT</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20design" title=" inverse design"> inverse design</a> </p> <a href="https://publications.waset.org/abstracts/91997/optimization-of-vertical-axis-wind-turbine-based-on-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91997.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">323</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">3711</span> Application of Artificial Neural Networks to Adaptive Speed Control under ARDUINO</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Javier%20Fernandez%20De%20Canete">Javier Fernandez De Canete</a>, <a href="https://publications.waset.org/abstracts/search?q=Alvaro%20Fernandez-Quintero"> Alvaro Fernandez-Quintero</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, adaptive control schemes are being used when model based control schemes are applied in presence of uncertainty and model mismatches. Artificial neural networks have been employed both in modelling and control of non-linear dynamic systems with unknown dynamics. In fact, these are powerful tools to solve this control problem when only input-output operational data are available. A neural network controller under SIMULINK together with the ARDUINO hardware platform has been used to perform real-time speed control of a computer case fan. Comparison of performance with a PID controller has also been presented in order to show the efficacy of neural control under different command signals tracking and also when disturbance signals are present in the speed control loops. <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=ARDUINO%20platform" title=" ARDUINO platform"> ARDUINO platform</a>, <a href="https://publications.waset.org/abstracts/search?q=SIMULINK" title=" SIMULINK"> SIMULINK</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20speed%20control" title=" adaptive speed control"> adaptive speed control</a> </p> <a href="https://publications.waset.org/abstracts/78360/application-of-artificial-neural-networks-to-adaptive-speed-control-under-arduino" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78360.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">363</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">3710</span> Neural Style Transfer Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaik%20Jilani%20Basha">Shaik Jilani Basha</a>, <a href="https://publications.waset.org/abstracts/search?q=Inavolu%20Avinash"> Inavolu Avinash</a>, <a href="https://publications.waset.org/abstracts/search?q=Alla%20Venu%20Sai%20Reddy"> Alla Venu Sai Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=Bitragunta%20Taraka%20Ramu"> Bitragunta Taraka Ramu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We can use the neural style transfer technique to build a picture with the same "content" as the beginning image but the "style" of the picture we've chosen. Neural style transfer is a technique for merging the style of one image into another while retaining its original information. The only change is how the image is formatted to give it an additional artistic sense. The content image depicts the plan or drawing, as well as the colors of the drawing or paintings used to portray the style. It is a computer vision programme that learns and processes images through deep convolutional neural networks. To implement software, we used to train deep learning models with the train data, and whenever a user takes an image and a styled image, the output will be as the style gets transferred to the original image, and it will be shown as the output. <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=computer%20vision" title=" computer vision"> computer vision</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=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/167224/neural-style-transfer-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167224.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">95</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">3709</span> A Neural Network Approach to Understanding Turbulent Jet Formations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Bin%20Ibrahim">Nurul Bin Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Advancements in neural networks have offered valuable insights into Fluid Dynamics, notably in addressing turbulence-related challenges. In this research, we introduce multiple applications of models of neural networks, namely Feed-Forward and Recurrent Neural Networks, to explore the relationship between jet formations and stratified turbulence within stochastically excited Boussinesq systems. Using machine learning tools like TensorFlow and PyTorch, the study has created models that effectively mimic and show the underlying features of the complex patterns of jet formation and stratified turbulence. These models do more than just help us understand these patterns; they also offer a faster way to solve problems in stochastic systems, improving upon traditional numerical techniques to solve stochastic differential equations such as the Euler-Maruyama method. In addition, the research includes a thorough comparison with the Statistical State Dynamics (SSD) approach, which is a well-established method for studying chaotic systems. This comparison helps evaluate how well neural networks can help us understand the complex relationship between jet formations and stratified turbulence. The results of this study underscore the potential of neural networks in computational physics and fluid dynamics, opening up new possibilities for more efficient and accurate simulations in these fields. <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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20fluid%20dynamics" title=" computational fluid dynamics"> computational fluid dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=stratified%20turbulence" title=" stratified turbulence"> stratified turbulence</a> </p> <a href="https://publications.waset.org/abstracts/171124/a-neural-network-approach-to-understanding-turbulent-jet-formations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171124.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">70</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">3708</span> Comparison of Classical Computer Vision vs. Convolutional Neural Networks Approaches for Weed Mapping in Aerial Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Cesar%20Pereira%20Junior">Paulo Cesar Pereira Junior</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandre%20Monteiro"> Alexandre Monteiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafael%20da%20Luz%20Ribeiro"> Rafael da Luz Ribeiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Carlos%20Sobieranski"> Antonio Carlos Sobieranski</a>, <a href="https://publications.waset.org/abstracts/search?q=Aldo%20von%20Wangenheim"> Aldo von Wangenheim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models. <p class="card-text"><strong>Keywords:</strong> <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=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20image%20processing" title=" digital image processing"> digital image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20agriculture" title=" precision agriculture"> precision agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20segmentation" title=" semantic segmentation"> semantic segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=unmanned%20aerial%20vehicles" title=" unmanned aerial vehicles"> unmanned aerial vehicles</a> </p> <a href="https://publications.waset.org/abstracts/112982/comparison-of-classical-computer-vision-vs-convolutional-neural-networks-approaches-for-weed-mapping-in-aerial-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112982.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">260</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">3707</span> Prediction of Vapor Liquid Equilibrium for Dilute Solutions of Components in Ionic Liquid by Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Mousavian">S. Mousavian</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Abedianpour"> A. Abedianpour</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Khanmohammadi"> A. Khanmohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Hematian"> S. Hematian</a>, <a href="https://publications.waset.org/abstracts/search?q=Gh.%20Eidi%20Veisi"> Gh. Eidi Veisi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ionic liquids are finding a wide range of applications from reaction media to separations and materials processing. In these applications, Vapor–Liquid equilibrium (VLE) is the most important one. VLE for six systems at 353 K and activity coefficients at infinite dilution 〖(γ〗_i^∞) for various solutes (alkanes, alkenes, cycloalkanes, cycloalkenes, aromatics, alcohols, ketones, esters, ethers, and water) in the ionic liquids (1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide [EMIM][BTI], 1-hexyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide [HMIM][BTI], 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide [OMIM][BTI], and 1-butyl-1-methylpyrrolidinium bis (trifluoromethylsulfonyl) imide [BMPYR][BTI]) have been used to train neural networks in the temperature range from (303 to 333) K. Densities of the ionic liquids, Hildebrant constant of substances, and temperature were selected as input of neural networks. The networks with different hidden layers were examined. Networks with seven neurons in one hidden layer have minimum error and good agreement with experimental data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ionic%20liquid" title="ionic liquid">ionic liquid</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=VLE" title=" VLE"> VLE</a>, <a href="https://publications.waset.org/abstracts/search?q=dilute%20solution" title=" dilute solution"> dilute solution</a> </p> <a href="https://publications.waset.org/abstracts/42919/prediction-of-vapor-liquid-equilibrium-for-dilute-solutions-of-components-in-ionic-liquid-by-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42919.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">300</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">3706</span> Fault Diagnosis of Squirrel-Cage Induction Motor by a Neural Network Multi-Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yahia.%20Kourd">Yahia. Kourd</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Guersi%20D.%20Lefebvre"> N. Guersi D. Lefebvre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose to study the faults diagnosis in squirrel-cage induction motor using MLP neural networks. We use neural healthy and faulty models of the behavior in order to detect and isolate some faults in machine. In the first part of this work, we have created a neural model for the healthy state using Matlab and a motor located in LGEB by acquirins data inputs and outputs of this engine. Then we detected the faults in the machine by residual generation. These residuals are not sufficient to isolate the existing faults. For this reason, we proposed additive neural networks to represent the faulty behaviors. From the analysis of these residuals and the choice of a threshold we propose a method capable of performing the detection and diagnosis of some faults in asynchronous machines with squirrel cage rotor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=faults%20diagnosis" title="faults diagnosis">faults diagnosis</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=multi-models" title=" multi-models"> multi-models</a>, <a href="https://publications.waset.org/abstracts/search?q=squirrel-cage%20induction%20motor" title=" squirrel-cage induction motor"> squirrel-cage induction motor</a> </p> <a href="https://publications.waset.org/abstracts/8200/fault-diagnosis-of-squirrel-cage-induction-motor-by-a-neural-network-multi-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8200.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">636</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">3705</span> Taxonomic Classification for Living Organisms Using Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saed%20Khawaldeh">Saed Khawaldeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Elsharnouby"> Mohamed Elsharnouby</a>, <a href="https://publications.waset.org/abstracts/search?q=Alaa%20%20Eddin%20Alchalabi"> Alaa Eddin Alchalabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Usama%20Pervaiz"> Usama Pervaiz</a>, <a href="https://publications.waset.org/abstracts/search?q=Tajwar%20Aleef"> Tajwar Aleef</a>, <a href="https://publications.waset.org/abstracts/search?q=Vu%20Hoang%20Minh"> Vu Hoang Minh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20networks" title="deep networks">deep networks</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=taxonomic%20classification" title=" taxonomic classification"> taxonomic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20sequences%20classification" title=" DNA sequences classification "> DNA sequences classification </a> </p> <a href="https://publications.waset.org/abstracts/65170/taxonomic-classification-for-living-organisms-using-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65170.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">442</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=RBF%20neural%20networks&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=124">124</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=125">125</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>