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neural network classifier</title> <meta name="description" content="Search results for: two-stage neural network classifier"> <meta name="keywords" content="two-stage neural network classifier"> <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 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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="two-stage neural network classifier"> <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> 1530</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: two-stage neural network classifier</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1440</span> Identification of Nonlinear Systems Using Radial Basis Function Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Pislaru">C. Pislaru</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Shebani"> A. Shebani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the K-Means clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=system%20identification" title="system identification">system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</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=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=K-means%20clustering%20algorithm" title=" K-means clustering algorithm "> K-means clustering algorithm </a> </p> <a href="https://publications.waset.org/abstracts/14775/identification-of-nonlinear-systems-using-radial-basis-function-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14775.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">470</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">1439</span> An Approach to Building a Recommendation Engine for Travel Applications Using Genetic Algorithms and Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adrian%20Ionita">Adrian Ionita</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana-Maria%20Ghimes"> Ana-Maria Ghimes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20profile" title=" DNA profile"> DNA profile</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</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=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20profiling" title=" user profiling"> user profiling</a> </p> <a href="https://publications.waset.org/abstracts/89268/an-approach-to-building-a-recommendation-engine-for-travel-applications-using-genetic-algorithms-and-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89268.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">163</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">1438</span> A TiO₂-Based Memristor Reliable for Neuromorphic Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=X.%20S.%20Wu">X. S. Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Jia"> H. Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20H.%20Qian"> P. H. Qian</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Zhang"> Z. Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20L.%20Cai"> H. L. Cai</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20M.%20Zhang"> F. M. Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A bipolar resistance switching behaviour is detected for a Ti/TiO2-x/Au memristor device, which is fabricated by a masked designed magnetic sputtering. The current dependence of voltage indicates the curve changes slowly and continuously. When voltage pulses are applied to the device, the set and reset processes maintains linearity, which is used to simulate the synapses. We argue that the conduction mechanism of the device is from the oxygen vacancy channel model, and the resistance of the device change slowly due to the reaction between the titanium electrode and the intermediate layer and the existence of a large number of oxygen vacancies in the intermediate layer. Then, Hopfield neural network is constructed to simulate the behaviour of neural network in image processing, and the accuracy rate is more than 98%. This shows that titanium dioxide memristor has a broad application prospect in high performance neural network simulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=memristor%20fabrication" title="memristor fabrication">memristor fabrication</a>, <a href="https://publications.waset.org/abstracts/search?q=neuromorphic%20computing" title=" neuromorphic computing"> neuromorphic computing</a>, <a href="https://publications.waset.org/abstracts/search?q=bionic%20synaptic%20application" title=" bionic synaptic application"> bionic synaptic application</a>, <a href="https://publications.waset.org/abstracts/search?q=TiO%E2%82%82-based" title=" TiO₂-based"> TiO₂-based</a> </p> <a href="https://publications.waset.org/abstracts/172936/a-tio2-based-memristor-reliable-for-neuromorphic-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172936.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">90</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">1437</span> A Multi-Objective Evolutionary Algorithm of Neural Network for Medical Diseases Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sultan%20Noman%20Qasem">Sultan Noman Qasem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20network" title="radial basis function network">radial basis function network</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20learning" title=" hybrid learning"> hybrid learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/15843/a-multi-objective-evolutionary-algorithm-of-neural-network-for-medical-diseases-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15843.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">563</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">1436</span> Comparing SVM and Naïve Bayes Classifier for Automatic Microaneurysm Detections </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Sopharak">A. Sopharak</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Uyyanonvara"> B. Uyyanonvara</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Barman"> S. Barman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diabetic%20retinopathy" title="diabetic retinopathy">diabetic retinopathy</a>, <a href="https://publications.waset.org/abstracts/search?q=microaneurysm" title=" microaneurysm"> microaneurysm</a>, <a href="https://publications.waset.org/abstracts/search?q=naive%20Bayes%20classifier" title=" naive Bayes classifier"> naive Bayes classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20classifier" title=" SVM classifier"> SVM classifier</a> </p> <a href="https://publications.waset.org/abstracts/3939/comparing-svm-and-naive-bayes-classifier-for-automatic-microaneurysm-detections" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3939.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">1435</span> Integrating Artificial Neural Network and Taguchi Method on Constructing the Real Estate Appraisal Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mu-Yen%20Chen">Mu-Yen Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Min-Hsuan%20Fan"> Min-Hsuan Fan</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Chen%20Chen"> Chia-Chen Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Siang-Yu%20Jhong"> Siang-Yu Jhong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices. <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=Taguchi%20method" title=" Taguchi method"> Taguchi method</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20estate%20valuation%20model" title=" real estate valuation model"> real estate valuation model</a>, <a href="https://publications.waset.org/abstracts/search?q=investors" title=" investors"> investors</a> </p> <a href="https://publications.waset.org/abstracts/12958/integrating-artificial-neural-network-and-taguchi-method-on-constructing-the-real-estate-appraisal-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12958.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">489</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">1434</span> Neural Network Based Fluctuation Frequency Control in PV-Diesel Hybrid Power System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Heri%20Suryoatmojo">Heri Suryoatmojo</a>, <a href="https://publications.waset.org/abstracts/search?q=Adi%20Kurniawan"> Adi Kurniawan</a>, <a href="https://publications.waset.org/abstracts/search?q=Feby%20A.%20Pamuji"> Feby A. Pamuji</a>, <a href="https://publications.waset.org/abstracts/search?q=Nursalim"> Nursalim</a>, <a href="https://publications.waset.org/abstracts/search?q=Syaffaruddin"> Syaffaruddin</a>, <a href="https://publications.waset.org/abstracts/search?q=Herbert%20Innah"> Herbert Innah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Photovoltaic (PV) system hybrid with diesel system is utilized widely for electrification in remote area. PV output power fluctuates due to uncertainty condition of temperature and sun irradiance. When the penetration of PV power is large, the reliability of the power utility will be disturbed and seriously impact the unstable frequency of system. Therefore, designing a robust frequency controller in PV-diesel hybrid power system is very important. This paper proposes new method of frequency control application in hybrid PV-diesel system based on artificial neural network (ANN). This method can minimize the frequency deviation without smoothing PV output power that controlled by maximum power point tracking (MPPT) method. The neural network algorithm controller considers average irradiance, change of irradiance and frequency deviation. In order the show the effectiveness of proposed algorithm, the addition of battery as energy storage system is also presented. To validate the proposed method, the results of proposed system are compared with the results of similar system using MPPT only. The simulation results show that the proposed method able to suppress frequency deviation smaller compared to the results of system using MPPT only. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20storage%20system" title="energy storage system">energy storage system</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20deviation" title=" frequency deviation"> frequency deviation</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20power%20generation" title=" hybrid power generation"> hybrid power generation</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20algorithm" title=" neural network algorithm"> neural network algorithm</a> </p> <a href="https://publications.waset.org/abstracts/4960/neural-network-based-fluctuation-frequency-control-in-pv-diesel-hybrid-power-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4960.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">502</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">1433</span> Data-Driven Analysis of Velocity Gradient Dynamics Using Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nishant%20Parashar">Nishant Parashar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sawan%20S.%20Sinha"> Sawan S. Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Balaji%20Srinivasan"> Balaji Srinivasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We perform an investigation of the unclosed terms in the evolution equation of the velocity gradient tensor (VGT) in compressible decaying turbulent flow. Velocity gradients in a compressible turbulent flow field influence several important nonlinear turbulent processes like cascading and intermittency. In an attempt to understand the dynamics of the velocity gradients various researchers have tried to model the unclosed terms in the evolution equation of the VGT. The existing models proposed for these unclosed terms have limited applicability. This is mainly attributable to the complex structure of the higher order gradient terms appearing in the evolution equation of VGT. We investigate these higher order gradients using the data from direct numerical simulation (DNS) of compressible decaying isotropic turbulent flow. The gas kinetic method aided with weighted essentially non-oscillatory scheme (WENO) based flow- reconstruction is employed to generate DNS data. By applying neural-network to the DNS data, we map the structure of the unclosed higher order gradient terms in the evolution of the equation of the VGT with VGT itself. We validate our findings by performing alignment based study of the unclosed higher order gradient terms obtained using the neural network with the strain rate eigenvectors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressible%20turbulence" title="compressible turbulence">compressible turbulence</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=velocity%20gradient%20tensor" title=" velocity gradient tensor"> velocity gradient tensor</a>, <a href="https://publications.waset.org/abstracts/search?q=direct%20numerical%20simulation" title=" direct numerical simulation"> direct numerical simulation</a> </p> <a href="https://publications.waset.org/abstracts/101552/data-driven-analysis-of-velocity-gradient-dynamics-using-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101552.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">168</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">1432</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">1431</span> The Impact of the Number of Neurons in the Hidden Layer on the Performance of MLP Neural Network: Application to the Fast Identification of Toxics Gases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Slimane%20Ouhmad">Slimane Ouhmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdellah%20Halimi"> Abdellah Halimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we have applied neural networks method MLP type to a database from an array of six sensors for the detection of three toxic gases. As the choice of the number of hidden layers and the weight values has a great influence on the convergence of the learning algorithm, we proposed, in this article, a mathematical formulation to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases on the one hand, and optimize the computation time on the other hand, the comparison to other results achieved in this case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MLP%20Neural%20Network" title="MLP Neural Network">MLP Neural Network</a>, <a href="https://publications.waset.org/abstracts/search?q=back-propagation" title=" back-propagation"> back-propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20of%20neurons%20in%20the%20hidden%20layer" title=" number of neurons in the hidden layer"> number of neurons in the hidden layer</a>, <a href="https://publications.waset.org/abstracts/search?q=identification" title=" identification"> identification</a>, <a href="https://publications.waset.org/abstracts/search?q=computing%20time" title=" computing time"> computing time</a> </p> <a href="https://publications.waset.org/abstracts/19653/the-impact-of-the-number-of-neurons-in-the-hidden-layer-on-the-performance-of-mlp-neural-network-application-to-the-fast-identification-of-toxics-gases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19653.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">347</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">1430</span> Influential Parameters in Estimating Soil Properties from Cone Penetrating Test: An Artificial Neural Network Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20G.%20Mahgoub">Ahmed G. Mahgoub</a>, <a href="https://publications.waset.org/abstracts/search?q=Dahlia%20H.%20Hafez"> Dahlia H. Hafez</a>, <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20A.%20Abu%20Kiefa"> Mostafa A. Abu Kiefa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Cone Penetration Test (CPT) is a common in-situ test which generally investigates a much greater volume of soil more quickly than possible from sampling and laboratory tests. Therefore, it has the potential to realize both cost savings and assessment of soil properties rapidly and continuously. The principle objective of this paper is to demonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle of internal friction (Φ) and the soil modulus of elasticity (E) from CPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used to study the influence of different parameters and recommend which parameters should be included as input parameters to improve the prediction. Neural networks discover relationships in the input data sets through the iterative presentation of the data and intrinsic mapping characteristics of neural topologies. General Regression Neural Network (GRNN) is one of the powerful neural network architectures which is utilized in this study. A large amount of field and experimental data including CPT results, plate load tests, direct shear box, grain size distribution and calculated data of overburden pressure was obtained from a large project in the United Arab Emirates. This data was used for the training and the validation of the neural network. A comparison was made between the obtained results from the ANN's approach, and some common traditional correlations that predict Φ and E from CPT results with respect to the actual results of the collected data. The results show that the ANN is a very powerful tool. Very good agreement was obtained between estimated results from ANN and actual measured results with comparison to other correlations available in the literature. The study recommends some easily available parameters that should be included in the estimation of the soil properties to improve the prediction models. It is shown that the use of friction ration in the estimation of Φ and the use of fines content in the estimation of E considerable improve the prediction models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=angle%20of%20internal%20friction" title="angle of internal friction">angle of internal friction</a>, <a href="https://publications.waset.org/abstracts/search?q=cone%20penetrating%20test" title=" cone penetrating test"> cone penetrating test</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20regression%20neural%20network" title=" general regression neural network"> general regression neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20modulus%20of%20elasticity" title=" soil modulus of elasticity"> soil modulus of elasticity</a> </p> <a href="https://publications.waset.org/abstracts/16780/influential-parameters-in-estimating-soil-properties-from-cone-penetrating-test-an-artificial-neural-network-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16780.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">415</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">1429</span> Training a Neural Network to Segment, Detect and Recognize Numbers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abhisek%20Dash">Abhisek Dash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well. <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=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20detection" title=" text detection"> text detection</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20segmentation" title=" text segmentation"> text segmentation</a> </p> <a href="https://publications.waset.org/abstracts/85788/training-a-neural-network-to-segment-detect-and-recognize-numbers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85788.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">161</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">1428</span> Alloy Design of Single Crystal Ni-base Superalloys by Combined Method of Neural Network and CALPHAD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Montakhabrazlighi">Mehdi Montakhabrazlighi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ercan%20Balikci"> Ercan Balikci</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The neural network (NN) method is applied to alloy development of single crystal Ni-base Superalloys with low density and improved mechanical strength. A set of 1200 dataset which includes chemical composition of the alloys, applied stress and temperature as inputs and density and time to rupture as outputs is used for training and testing the network. Thermodynamic phase diagram modeling of the screened alloys is performed with Thermocalc software to model the equilibrium phases and also microsegregation in solidification processing. The model is first trained by 80% of the data and the 20% rest is used to test it. Comparing the predicted values and the experimental ones showed that a well-trained network is capable of accurately predicting the density and time to rupture strength of the Ni-base superalloys. Modeling results is used to determine the effect of alloying elements, stress, temperature and gamma-prime phase volume fraction on rupture strength of the Ni-base superalloys. This approach is in line with the materials genome initiative and integrated computed materials engineering approaches promoted recently with the aim of reducing the cost and time for development of new alloys for critical aerospace components. This work has been funded by TUBITAK under grant number 112M783. <p class="card-text"><strong>Keywords:</strong> <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=rupture%20strength" title=" rupture strength"> rupture strength</a>, <a href="https://publications.waset.org/abstracts/search?q=superalloy" title=" superalloy"> superalloy</a>, <a href="https://publications.waset.org/abstracts/search?q=thermocalc" title=" thermocalc"> thermocalc</a> </p> <a href="https://publications.waset.org/abstracts/39191/alloy-design-of-single-crystal-ni-base-superalloys-by-combined-method-of-neural-network-and-calphad" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39191.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">313</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">1427</span> Measuring Multi-Class Linear Classifier for Image Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Susilawati%20Mohamad">Fatma Susilawati Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Azizah%20Abdul%20Manaf"> Azizah Abdul Manaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadhillah%20Ahmad"> Fadhillah Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Zarina%20Mohamad"> Zarina Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Wan%20Suryani%20Wan%20Awang"> Wan Suryani Wan Awang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A simple and robust multi-class linear classifier is proposed and implemented. For a pair of classes of the linear boundary, a collection of segments of hyper planes created as perpendicular bisectors of line segments linking centroids of the classes or part of classes. Nearest Neighbor and Linear Discriminant Analysis are compared in the experiments to see the performances of each classifier in discriminating ripeness of oil palm. This paper proposes a multi-class linear classifier using Linear Discriminant Analysis (LDA) for image identification. Result proves that LDA is well capable in separating multi-class features for ripeness identification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-class" title="multi-class">multi-class</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20classifier" title=" linear classifier"> linear classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbor" title=" nearest neighbor"> nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/51310/measuring-multi-class-linear-classifier-for-image-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51310.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">538</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">1426</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">1425</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">1424</span> Application of Neural Network in Portfolio Product Companies: Integration of Boston Consulting Group Matrix and Ansoff Matrix</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Khajezadeh">M. Khajezadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Saied%20Fallah%20Niasar"> M. Saied Fallah Niasar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ali%20Asli"> S. Ali Asli</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Davani%20Davari"> D. Davani Davari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Godarzi"> M. Godarzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Asgari"> Y. Asgari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to explore the joint application of both Boston and Ansoff matrices in the operational development of the product. We conduct deep analysis, by utilizing the Artificial Neural Network, to predict the position of the product in the market while the company is interested in increasing its share. The data are gathered from two industries, called hygiene and detergent. In doing so, the effort is being made by investigating the behavior of top player companies and, recommend strategic orientations. In conclusion, this combination analysis is appropriate for operational development; as well, it plays an important role in providing the position of the product in the market for both hygiene and detergent industries. More importantly, it will elaborate on the company’s strategies to increase its market share related to a combination of the Boston Consulting Group (BCG) Matrix and Ansoff Matrix. <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=portfolio%20analysis" title=" portfolio analysis"> portfolio analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=BCG%20matrix" title=" BCG matrix"> BCG matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=Ansoff%20matrix" title=" Ansoff matrix"> Ansoff matrix</a> </p> <a href="https://publications.waset.org/abstracts/103499/application-of-neural-network-in-portfolio-product-companies-integration-of-boston-consulting-group-matrix-and-ansoff-matrix" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103499.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">143</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">1423</span> Machine Learning Based Gender Identification of Authors of Entry Programs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Go%20Woon%20Kwak">Go Woon Kwak</a>, <a href="https://publications.waset.org/abstracts/search?q=Siyoung%20Jun"> Siyoung Jun</a>, <a href="https://publications.waset.org/abstracts/search?q=Soyun%20Maeng"> Soyun Maeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Haeyoung%20Lee"> Haeyoung Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Entry is an education platform used in South Korea, created to help students learn to program, in which they can learn to code while playing. Using the online version of the entry, teachers can easily assign programming homework to the student and the students can make programs simply by linking programming blocks. However, the programs may be made by others, so that the authors of the programs should be identified. In this paper, as the first step toward author identification of entry programs, we present an artificial neural network based classification approach to identify genders of authors of a program written in an entry. A neural network has been trained from labeled training data that we have collected. Our result in progress, although preliminary, shows that the proposed approach could be feasible to be applied to the online version of entry for gender identification of authors. As future work, we will first use a machine learning technique for age identification of entry programs, which would be the second step toward the author identification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=author%20identification" title=" author identification"> author identification</a>, <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=gender%20identification" title=" gender identification"> gender identification</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/66685/machine-learning-based-gender-identification-of-authors-of-entry-programs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66685.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">1422</span> Photo-Fenton Decolorization of Methylene Blue Adsolubilized on Co2+ -Embedded Alumina Surface: Comparison of Process Modeling through Response Surface Methodology and Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prateeksha%20Mahamallik">Prateeksha Mahamallik</a>, <a href="https://publications.waset.org/abstracts/search?q=Anjali%20Pal"> Anjali Pal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present study, Co(II)-adsolubilized surfactant modified alumina (SMA) was prepared, and methylene blue (MB) degradation was carried out on Co-SMA surface by visible light photo-Fenton process. The entire reaction proceeded on solid surface as MB was embedded on Co-SMA surface. The reaction followed zero order kinetics. Response surface methodology (RSM) and artificial neural network (ANN) were used for modeling the decolorization of MB by photo-Fenton process as a function of dose of Co-SMA (10, 20 and 30 g/L), initial concentration of MB (10, 20 and 30 mg/L), concentration of H2O2 (174.4, 348.8 and 523.2 mM) and reaction time (30, 45 and 60 min). The prediction capabilities of both the methodologies (RSM and ANN) were compared on the basis of correlation coefficient (R2), root mean square error (RMSE), standard error of prediction (SEP), relative percent deviation (RPD). Due to lower value of RMSE (1.27), SEP (2.06) and RPD (1.17) and higher value of R2 (0.9966), ANN was proved to be more accurate than RSM in order to predict decolorization efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adsolubilization" title="adsolubilization">adsolubilization</a>, <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=methylene%20blue" title=" methylene blue"> methylene blue</a>, <a href="https://publications.waset.org/abstracts/search?q=photo-fenton%20process" title=" photo-fenton process"> photo-fenton process</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20methodology" title=" response surface methodology"> response surface methodology</a> </p> <a href="https://publications.waset.org/abstracts/55686/photo-fenton-decolorization-of-methylene-blue-adsolubilized-on-co2-embedded-alumina-surface-comparison-of-process-modeling-through-response-surface-methodology-and-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55686.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">254</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">1421</span> Prediction of the Lateral Bearing Capacity of Short Piles in Clayey Soils Using Imperialist Competitive Algorithm-Based Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Dinarvand">Reza Dinarvand</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Sadeghian"> Mahdi Sadeghian</a>, <a href="https://publications.waset.org/abstracts/search?q=Somaye%20Sadeghian"> Somaye Sadeghian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods. <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=clayey%20soil" title=" clayey soil"> clayey soil</a>, <a href="https://publications.waset.org/abstracts/search?q=imperialist%20competition%20algorithm" title=" imperialist competition algorithm"> imperialist competition algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=lateral%20bearing%20capacity" title=" lateral bearing capacity"> lateral bearing capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20pile" title=" short pile"> short pile</a> </p> <a href="https://publications.waset.org/abstracts/104881/prediction-of-the-lateral-bearing-capacity-of-short-piles-in-clayey-soils-using-imperialist-competitive-algorithm-based-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104881.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">1420</span> Prediction of Temperature Distribution during Drilling Process Using Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Reza%20Tahavvor">Ali Reza Tahavvor</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Hosseini"> Saeed Hosseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Nazli%20Jowkar"> Nazli Jowkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Afshin%20Karimzadeh%20Fard"> Afshin Karimzadeh Fard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Experimental & numeral study of temperature distribution during milling process, is important in milling quality and tools life aspects. In the present study the milling cross-section temperature is determined by using Artificial Neural Networks (ANN) according to the temperature of certain points of the work piece and the points specifications and the milling rotational speed of the blade. In the present work, at first three-dimensional model of the work piece is provided and then by using the Computational Heat Transfer (CHT) simulations, temperature in different nods of the work piece are specified in steady-state conditions. Results obtained from CHT are used for training and testing the ANN approach. Using reverse engineering and setting the desired x, y, z and the milling rotational speed of the blade as input data to the network, the milling surface temperature determined by neural network is presented as output data. The desired points temperature for different milling blade rotational speed are obtained experimentally and by extrapolation method for the milling surface temperature is obtained and a comparison is performed among the soft programming ANN, CHT results and experimental data and it is observed that ANN soft programming code can be used more efficiently to determine the temperature in a milling process. <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=milling%20process" title=" milling process"> milling process</a>, <a href="https://publications.waset.org/abstracts/search?q=rotational%20speed" title=" rotational speed"> rotational speed</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature" title=" temperature"> temperature</a> </p> <a href="https://publications.waset.org/abstracts/24429/prediction-of-temperature-distribution-during-drilling-process-using-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24429.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">405</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">1419</span> Presenting a Model for Predicting the State of Being Accident-Prone of Passages According to Neural Network and Spatial Data Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamd%20Rezaeifar">Hamd Rezaeifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Reza%20Sahriari"> Hamid Reza Sahriari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accidents are considered to be one of the challenges of modern life. Due to the fact that the victims of this problem and also internal transportations are getting increased day by day in Iran, studying effective factors of accidents and identifying suitable models and parameters about this issue are absolutely essential. The main purpose of this research has been studying the factors and spatial data affecting accidents of Mashhad during 2007- 2008. In this paper it has been attempted to – through matching spatial layers on each other and finally by elaborating them with the place of accident – at the first step by adding landmarks of the accident and through adding especial fields regarding the existence or non-existence of effective phenomenon on accident, existing information banks of the accidents be completed and in the next step by means of data mining tools and analyzing by neural network, the relationship between these data be evaluated and a logical model be designed for predicting accident-prone spots with minimum error. The model of this article has a very accurate prediction in low-accident spots; yet it has more errors in accident-prone regions due to lack of primary data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accident" title="accident">accident</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=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a> </p> <a href="https://publications.waset.org/abstracts/185355/presenting-a-model-for-predicting-the-state-of-being-accident-prone-of-passages-according-to-neural-network-and-spatial-data-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185355.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">47</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">1418</span> Image Classification with Localization 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=Bhuyain%20Mobarok%20Hossain">Bhuyain Mobarok Hossain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN). <p class="card-text"><strong>Keywords:</strong> <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=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title=" particle filter"> particle filter</a> </p> <a href="https://publications.waset.org/abstracts/139288/image-classification-with-localization-using-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139288.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">305</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">1417</span> Land Cover Remote Sensing Classification Advanced Neural Networks Supervised Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eiman%20Kattan">Eiman Kattan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conventional%20neural%20network" title="conventional neural network">conventional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20cover" title=" land cover"> land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use" title=" land use"> land use</a> </p> <a href="https://publications.waset.org/abstracts/80774/land-cover-remote-sensing-classification-advanced-neural-networks-supervised-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80774.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">370</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">1416</span> Detection of Atrial Fibrillation Using Wearables via Attentional Two-Stream Heterogeneous Networks </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huawei%20Bai">Huawei Bai</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianguo%20Yao"> Jianguo Yao</a>, <a href="https://publications.waset.org/abstracts/search?q=Fellow"> Fellow</a>, <a href="https://publications.waset.org/abstracts/search?q=IEEE"> IEEE</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PPG%20wearables" title="PPG wearables">PPG wearables</a>, <a href="https://publications.waset.org/abstracts/search?q=atrial%20fibrillation" title=" atrial fibrillation"> atrial fibrillation</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20fusion" title=" feature fusion"> feature fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=attention%20mechanism" title=" attention mechanism"> attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=hyber%20network" title=" hyber network"> hyber network</a> </p> <a href="https://publications.waset.org/abstracts/113139/detection-of-atrial-fibrillation-using-wearables-via-attentional-two-stream-heterogeneous-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113139.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">121</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">1415</span> Cells Detection and Recognition in Bone Marrow Examination with Deep Learning Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiyin%20He">Shiyin He</a>, <a href="https://publications.waset.org/abstracts/search?q=Zheng%20Huang"> Zheng Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cell%20detection" title="cell detection">cell detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cell%20recognition" title=" cell recognition"> cell recognition</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=Mask-RCNN" title=" Mask-RCNN"> Mask-RCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet" title=" ResNet"> ResNet</a> </p> <a href="https://publications.waset.org/abstracts/98649/cells-detection-and-recognition-in-bone-marrow-examination-with-deep-learning-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98649.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">189</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">1414</span> Application of Neural Networks to Predict Changing the Diameters of Bubbles in Pool Boiling Distilled Water</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <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=M.%20Manteghian"> M. Manteghian</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Masoumi"> M. Masoumi</a>, <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> </p> <p class="card-text"><strong>Abstract:</strong></p> In this research, the capability of neural networks in modeling and learning complicated and nonlinear relations has been used to develop a model for the prediction of changes in the diameter of bubbles in pool boiling distilled water. The input parameters used in the development of this network include element temperature, heat flux, and retention time of bubbles. The test data obtained from the experiment of the pool boiling of distilled water, and the measurement of the bubbles form on the cylindrical element. The model was developed based on training algorithm, which is typologically of back-propagation type. Considering the correlation coefficient obtained from this model is 0.9633. This shows that this model can be trusted for the simulation and modeling of the size of bubble and thermal transfer of boiling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bubble%20diameter" title="bubble diameter">bubble diameter</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20flux" title=" heat flux"> heat flux</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=training%20algorithm" title=" training algorithm"> training algorithm</a> </p> <a href="https://publications.waset.org/abstracts/2793/application-of-neural-networks-to-predict-changing-the-diameters-of-bubbles-in-pool-boiling-distilled-water" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2793.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">443</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">1413</span> Detecting HCC Tumor in Three Phasic CT Liver Images with Optimization of Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdieh%20Khalilinezhad">Mahdieh Khalilinezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Silvana%20Dellepiane"> Silvana Dellepiane</a>, <a href="https://publications.waset.org/abstracts/search?q=Gianni%20Vernazza"> Gianni Vernazza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of the present work is to build a model based on tissue characterization that is able to discriminate pathological and non-pathological regions from three-phasic CT images. Based on feature selection in different phases, in this research, we design a neural network system that has optimal neuron number in a hidden layer. Our approach consists of three steps: feature selection, feature reduction, and classification. For each ROI, 6 distinct set of texture features are extracted such as first order histogram parameters, absolute gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet, for a total of 270 texture features. We show that with the injection of liquid and the analysis of more phases the high relevant features in each region changed. Our results show that for detecting HCC tumor phase3 is the best one in most of the features that we apply to the classification algorithm. The percentage of detection between these two classes according to our method, relates to first order histogram parameters with the accuracy of 85% in phase 1, 95% phase 2, and 95% in phase 3. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-phasic%20liver%20images" title="multi-phasic liver images">multi-phasic liver images</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20analysis" title=" texture analysis"> texture analysis</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=hidden%20layer" title=" hidden layer"> hidden layer</a> </p> <a href="https://publications.waset.org/abstracts/26386/detecting-hcc-tumor-in-three-phasic-ct-liver-images-with-optimization-of-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26386.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">262</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">1412</span> Study of a Crude Oil Desalting Plant of the National Iranian South Oil Company in Gachsaran by 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=H.%20Kiani">H. Kiani</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Moradi"> S. Moradi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Soltani%20Soulgani"> B. Soltani Soulgani</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Mousavian"> S. Mousavian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=desalting%20unit" title="desalting unit">desalting unit</a>, <a href="https://publications.waset.org/abstracts/search?q=crude%20oil" title=" crude oil"> crude oil</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=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=recovery" title=" recovery"> recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=separation" title=" separation"> separation</a> </p> <a href="https://publications.waset.org/abstracts/3441/study-of-a-crude-oil-desalting-plant-of-the-national-iranian-south-oil-company-in-gachsaran-by-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3441.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">450</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">1411</span> A Neural Network System for Predicting the Hardness of Titanium Aluminum Nitrite (TiAlN) Coatings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20M.%20Elmabrouk">Omar M. Elmabrouk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes. <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=hardness" title=" hardness"> hardness</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=titanium%20aluminium%20nitrate%20coating" title=" titanium aluminium nitrate coating"> titanium aluminium nitrate coating</a> </p> <a href="https://publications.waset.org/abstracts/33962/a-neural-network-system-for-predicting-the-hardness-of-titanium-aluminum-nitrite-tialn-coatings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33962.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">554</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=two-stage%20neural%20network%20classifier&page=3" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=two-stage%20neural%20network%20classifier&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=two-stage%20neural%20network%20classifier&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=two-stage%20neural%20network%20classifier&page=3">3</a></li> <li class="page-item active"><span 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