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neural network models</title> <meta name="description" content="Search results for: feed-forward neural network models"> <meta name="keywords" content="feed-forward neural network models"> <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" /> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="feed-forward neural network models"> <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> 11121</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: feed-forward neural network models</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11121</span> Feedforward Neural Network with Backpropagation for Epilepsy Seizure Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natalia%20%20Espinosa">Natalia Espinosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Arthur%20Amorim"> Arthur Amorim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rudolf%20%20Huebner"> Rudolf Huebner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Network%20%28ANN%29" title="Artificial Neural Network (ANN)">Artificial Neural Network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Epilepsy%20Detection" title=" Epilepsy Detection "> Epilepsy Detection </a>, <a href="https://publications.waset.org/abstracts/search?q=Seizure." title=" Seizure."> Seizure.</a> </p> <a href="https://publications.waset.org/abstracts/122872/feedforward-neural-network-with-backpropagation-for-epilepsy-seizure-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122872.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">223</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">11120</span> Predict Suspended Sediment Concentration Using Artificial Neural Networks Technique: Case Study Oued El Abiod Watershed, Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Bougamouza">Adel Bougamouza</a>, <a href="https://publications.waset.org/abstracts/search?q=Boualam%20Remini"> Boualam Remini</a>, <a href="https://publications.waset.org/abstracts/search?q=Abd%20El%20Hadi%20Ammari"> Abd El Hadi Ammari</a>, <a href="https://publications.waset.org/abstracts/search?q=Feteh%20Sakhraoui"> Feteh Sakhraoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The assessment of sediments being carried by a river is importance for planning and designing of various water resources projects. In this study, Artificial Neural Network Techniques are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the upstream of Foum El Gherza dam, Biskra, Algeria. The FFNN, GRNN, and RBNN models are established for estimating current suspended sediment values. Some statistics involving RMSE and R2 were used to evaluate the performance of applied models. The comparison of three AI models showed that the RBNN model performed better than the FFNN and GRNN models with R2 = 0.967 and RMSE= 5.313 mg/l. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision. <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=Oued%20Abiod%20watershed" title=" Oued Abiod watershed"> Oued Abiod watershed</a>, <a href="https://publications.waset.org/abstracts/search?q=feedforward%20network" title=" feedforward network"> feedforward network</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20regression%20network" title=" generalized regression network"> generalized regression network</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20network" title=" radial basis network"> radial basis network</a>, <a href="https://publications.waset.org/abstracts/search?q=sediment%20concentration" title=" sediment concentration"> sediment concentration</a> </p> <a href="https://publications.waset.org/abstracts/37324/predict-suspended-sediment-concentration-using-artificial-neural-networks-technique-case-study-oued-el-abiod-watershed-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37324.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">418</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">11119</span> Comparative Analysis of Sigmoidal Feedforward Artificial Neural Networks and Radial Basis Function Networks Approach for Localization in Wireless Sensor Networks </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Payal">Ashish Payal</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Rai"> C. S. Rai</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20V.%20R.%20Reddy"> B. V. R. Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m<sup>2</sup> grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=localization" title="localization">localization</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title=" wireless sensor networks"> wireless sensor networks</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=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=RSSI" title=" RSSI"> RSSI</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS" title=" GPS"> GPS</a> </p> <a href="https://publications.waset.org/abstracts/49637/comparative-analysis-of-sigmoidal-feedforward-artificial-neural-networks-and-radial-basis-function-networks-approach-for-localization-in-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49637.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">339</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">11118</span> Optimisation of the Input Layer Structure for Feedforward Narx Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zongyan%20Li">Zongyan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Matt%20Best"> Matt Best</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation%20analysis" title="correlation analysis">correlation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=F-ratio" title=" F-ratio"> F-ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=levenberg-marquardt" title=" levenberg-marquardt"> levenberg-marquardt</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=NARX" title=" NARX"> NARX</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=optimisation" title=" optimisation"> optimisation</a> </p> <a href="https://publications.waset.org/abstracts/23195/optimisation-of-the-input-layer-structure-for-feedforward-narx-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23195.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">372</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">11117</span> A Comparison between Artificial Neural Network Prediction Models for Coronal Hole Related High Speed Streams</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rehab%20Abdulmajed">Rehab Abdulmajed</a>, <a href="https://publications.waset.org/abstracts/search?q=Amr%20Hamada"> Amr Hamada</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Elsaid"> Ahmed Elsaid</a>, <a href="https://publications.waset.org/abstracts/search?q=Hisashi%20Hayakawa"> Hisashi Hayakawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayman%20Mahrous"> Ayman Mahrous</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solar emissions have a high impact on the Earth’s magnetic field, and the prediction of solar events is of high interest. Various techniques have been used in the prediction of solar wind using mathematical models, MHD models, and neural network (NN) models. This study investigates the coronal hole (CH) derived high-speed streams (HSSs) and their correlation to the CH area and create a neural network model to predict the HSSs. Two different algorithms were used to compare different models to find a model that best simulates the HSSs. A dataset of CH synoptic maps through Carrington rotations 1601 to 2185 along with Omni-data set solar wind speed averaged over the Carrington rotations is used, which covers Solar cycles (sc) 21, 22, 23, and most of 24. <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=coronal%20hole%20area" title=" coronal hole area"> coronal hole area</a>, <a href="https://publications.waset.org/abstracts/search?q=feed-forward%20neural%20network%20models" title=" feed-forward neural network models"> feed-forward neural network models</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20high%20speed%20streams" title=" solar high speed streams"> solar high speed streams</a> </p> <a href="https://publications.waset.org/abstracts/167145/a-comparison-between-artificial-neural-network-prediction-models-for-coronal-hole-related-high-speed-streams" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167145.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">88</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">11116</span> SOM Map vs Hopfield Neural Network: A Comparative Study in Microscopic Evacuation Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zouhour%20Neji%20Ben%20Salem">Zouhour Neji Ben Salem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microscopic evacuation focuses on the evacuee behavior and way of search of safety place in an egress situation. In recent years, several models handled microscopic evacuation problem. Among them, we have proposed Artificial Neural Network (ANN) as an alternative to mathematical models that can deal with such problem. In this paper, we present two ANN models: SOM map and Hopfield Network used to predict the evacuee behavior in a disaster situation. These models are tested in a real case, the second floor of Tunisian children hospital evacuation in case of fire. The two models are studied and compared in order to evaluate their performance. <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=self-organization%20map" title=" self-organization map"> self-organization map</a>, <a href="https://publications.waset.org/abstracts/search?q=hopfield%20network" title=" hopfield network"> hopfield network</a>, <a href="https://publications.waset.org/abstracts/search?q=microscopic%20evacuation" title=" microscopic evacuation"> microscopic evacuation</a>, <a href="https://publications.waset.org/abstracts/search?q=fire%20building%20evacuation" title=" fire building evacuation"> fire building evacuation</a> </p> <a href="https://publications.waset.org/abstracts/27689/som-map-vs-hopfield-neural-network-a-comparative-study-in-microscopic-evacuation-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27689.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">404</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">11115</span> Simulation of Flow through Dam Foundation by FEM and ANN Methods Case Study: Shahid Abbaspour Dam</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehrdad%20Shahrbanozadeh">Mehrdad Shahrbanozadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Gholam%20Abbas%20Barani"> Gholam Abbas Barani</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Shojaee"> Saeed Shojaee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a finite element (Seep3D model) and an artificial neural network (ANN) model were developed to simulate flow through dam foundation. Seep3D model is capable of simulating three-dimensional flow through a heterogeneous and anisotropic, saturated and unsaturated porous media. Flow through the Shahid Abbaspour dam foundation has been used as a case study. The FEM with 24960 triangular elements and 28707 nodes applied to model flow through foundation of this dam. The FEM being made denser in the neighborhood of the curtain screen. The ANN model developed for Shahid Abbaspour dam is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water level elevations of the upstream and downstream of the dam have been used as input variables and the piezometric heads as the target outputs in the ANN model. The two models are calibrated and verified using the Shahid Abbaspour’s dam piezometric data. Results of the models were compared with those measured by the piezometers which are in good agreement. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=seepage" title="seepage">seepage</a>, <a href="https://publications.waset.org/abstracts/search?q=dam%20foundation" title=" dam foundation"> dam foundation</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</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=seep%203D%20model" title=" seep 3D model"> seep 3D model</a> </p> <a href="https://publications.waset.org/abstracts/20239/simulation-of-flow-through-dam-foundation-by-fem-and-ann-methods-case-study-shahid-abbaspour-dam" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20239.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">474</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">11114</span> A Hybrid Feature Selection Algorithm with Neural Network for Software Fault Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalaf%20Khatatneh">Khalaf Khatatneh</a>, <a href="https://publications.waset.org/abstracts/search?q=Nabeel%20Al-Milli"> Nabeel Al-Milli</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjad%20Hudaib"> Amjad Hudaib</a>, <a href="https://publications.waset.org/abstracts/search?q=Monther%20Ali%20Tarawneh"> Monther Ali Tarawneh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</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=software%20fault%20prediction" title=" software fault prediction"> software fault prediction</a> </p> <a href="https://publications.waset.org/abstracts/167733/a-hybrid-feature-selection-algorithm-with-neural-network-for-software-fault-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167733.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">11113</span> Artificial Intelligence in the Design of High-Strength Recycled Concrete</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadi%20Rouhi%20Belvirdi">Hadi Rouhi Belvirdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Davoud%20Beheshtizadeh"> Davoud Beheshtizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing demand for sustainable construction materials has led to a growing interest in high-strength recycled concrete (HSRC). Utilizing recycled materials not only reduces waste but also minimizes the depletion of natural resources. This study explores the application of artificial intelligence (AI) techniques to model and predict the properties of HSRC. In the past two decades, the production levels in various industries and, consequently, the amount of waste have increased significantly. Continuing this trend will undoubtedly cause irreparable damage to the environment. For this reason, engineers have been constantly seeking practical solutions for recycling industrial waste in recent years. This research utilized the results of the compressive strength of 90-day high-strength recycled concrete. The method for creating recycled concrete involved replacing sand with crushed glass and using glass powder instead of cement. Subsequently, a feedforward artificial neural network was employed to model the compressive strength results for 90 days. The regression and error values obtained indicate that this network is suitable for modeling the compressive strength data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high-strength%20recycled%20concrete" title="high-strength recycled concrete">high-strength recycled concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=feedforward%20artificial%20neural%20network" title=" feedforward artificial neural network"> feedforward artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20materials" title=" construction materials"> construction materials</a> </p> <a href="https://publications.waset.org/abstracts/193212/artificial-intelligence-in-the-design-of-high-strength-recycled-concrete" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193212.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">13</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">11112</span> An Adjusted Network Information Criterion for Model Selection in Statistical Neural Network Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Godwin%20Udomboso">Christopher Godwin Udomboso</a>, <a href="https://publications.waset.org/abstracts/search?q=Angela%20Unna%20Chukwu"> Angela Unna Chukwu</a>, <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Kwame%20Dontwi"> Isaac Kwame Dontwi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In selecting a Statistical Neural Network model, the Network Information Criterion (NIC) has been observed to be sample biased, because it does not account for sample sizes. The selection of a model from a set of fitted candidate models requires objective data-driven criteria. In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC), based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The analyses show that on a general note, the ANIC improves model selection in more sample sizes than does the NIC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20neural%20network" title="statistical neural network">statistical neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20information%20criterion" title=" network information criterion"> network information criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=adjusted%20network" title=" adjusted network"> adjusted network</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20criterion" title=" information criterion"> information criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20function" title=" transfer function"> transfer function</a> </p> <a href="https://publications.waset.org/abstracts/28771/an-adjusted-network-information-criterion-for-model-selection-in-statistical-neural-network-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28771.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">566</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">11111</span> Dry Relaxation Shrinkage Prediction of Bordeaux Fiber Using a Feed Forward Neural</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Baeza%20S.%20Roberto">Baeza S. Roberto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation. <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=dry%20relaxation" title=" dry relaxation"> dry relaxation</a>, <a href="https://publications.waset.org/abstracts/search?q=knitting" title=" knitting"> knitting</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a> </p> <a href="https://publications.waset.org/abstracts/30179/dry-relaxation-shrinkage-prediction-of-bordeaux-fiber-using-a-feed-forward-neural" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30179.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">585</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">11110</span> Assessing Artificial Neural Network Models on Forecasting the Return of Stock Market Index</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Rostami%20Jaz">Hamid Rostami Jaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamran%20Ameri%20Siahooei"> Kamran Ameri Siahooei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exchange%20index" title="exchange index">exchange index</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=perceptron%20neural%20network" title=" perceptron neural network"> perceptron neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=Tehran%20stock%20exchange" title=" Tehran stock exchange"> Tehran stock exchange</a> </p> <a href="https://publications.waset.org/abstracts/51503/assessing-artificial-neural-network-models-on-forecasting-the-return-of-stock-market-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51503.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">464</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">11109</span> Decision Support System for Fetus Status Evaluation Using Cardiotocograms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oyebade%20K.%20Oyedotun">Oyebade K. Oyedotun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20support" title="decision support">decision support</a>, <a href="https://publications.waset.org/abstracts/search?q=cardiotocogram" title=" cardiotocogram"> cardiotocogram</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</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/32333/decision-support-system-for-fetus-status-evaluation-using-cardiotocograms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32333.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">332</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">11108</span> A Two-Step Framework for Unsupervised Speaker Segmentation Using BIC 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=Ahmad%20Alwosheel">Ahmad Alwosheel</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Alqaraawi"> Ahmed Alqaraawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work proposes a new speaker segmentation approach for two speakers. It is an online approach that does not require a prior information about speaker models. It has two phases, a conventional approach such as unsupervised BIC-based is utilized in the first phase to detect speaker changes and train a Neural Network, while in the second phase, the output trained parameters from the Neural Network are used to predict next incoming audio stream. Using this approach, a comparable accuracy to similar BIC-based approaches is achieved with a significant improvement in terms of computation 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=diarization" title=" diarization"> diarization</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20indexing" title=" speaker indexing"> speaker indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20segmentation" title=" speaker segmentation"> speaker segmentation</a> </p> <a href="https://publications.waset.org/abstracts/27191/a-two-step-framework-for-unsupervised-speaker-segmentation-using-bic-and-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27191.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">11107</span> Identification System for Grading Banana in Food Processing Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <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=Oyebade%20K.%20Oyedotun"> Oyebade K. Oyedotun</a>, <a href="https://publications.waset.org/abstracts/search?q=Khashman%20Adnan"> Khashman Adnan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=banana" title="banana">banana</a>, <a href="https://publications.waset.org/abstracts/search?q=food%20processing" title=" food processing"> food processing</a>, <a href="https://publications.waset.org/abstracts/search?q=identification%20system" title=" identification system"> identification system</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/31869/identification-system-for-grading-banana-in-food-processing-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31869.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">471</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">11106</span> Assessment the Quality of Telecommunication Services by Fuzzy Inferences System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oktay%20Nusratov">Oktay Nusratov</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Rzaev"> Ramin Rzaev</a>, <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Goyushov"> Aydin Goyushov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fuzzy inference method based approach to the forming of modular intellectual system of assessment the quality of communication services is proposed. Developed under this approach the basic fuzzy estimation model takes into account the recommendations of the International Telecommunication Union in respect of the operation of packet switching networks based on IP-protocol. To implement the main features and functions of the fuzzy control system of quality telecommunication services it is used multilayer feedforward neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20communication" title="quality of communication">quality of communication</a>, <a href="https://publications.waset.org/abstracts/search?q=IP-telephony" title=" IP-telephony"> IP-telephony</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20set" title=" fuzzy set"> fuzzy set</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20implication" title=" fuzzy implication"> fuzzy implication</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/15543/assessment-the-quality-of-telecommunication-services-by-fuzzy-inferences-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15543.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">468</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">11105</span> Neural Network Based Path Loss Prediction for Global System for Mobile Communication in an Urban Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danladi%20Ali">Danladi Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we measured GSM signal strength in the Dnepropetrovsk city in order to predict path loss in study area using nonlinear autoregressive neural network prediction and we also, used neural network clustering to determine average GSM signal strength receive at the study area. The nonlinear auto-regressive neural network predicted that the GSM signal is attenuated with the mean square error (MSE) of 2.6748dB, this attenuation value is used to modify the COST 231 Hata and the Okumura-Hata models. The neural network clustering revealed that -75dB to -95dB is received more frequently. This means that the signal strength received at the study is mostly weak signal <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=one-dimensional%20multilevel%20wavelets" title="one-dimensional multilevel wavelets">one-dimensional multilevel wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20loss" title=" path loss"> path loss</a>, <a href="https://publications.waset.org/abstracts/search?q=GSM%20signal%20strength" title=" GSM signal strength"> GSM signal strength</a>, <a href="https://publications.waset.org/abstracts/search?q=propagation" title=" propagation"> propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20environment%20and%20model" title=" urban environment and model"> urban environment and model</a> </p> <a href="https://publications.waset.org/abstracts/14119/neural-network-based-path-loss-prediction-for-global-system-for-mobile-communication-in-an-urban-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14119.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">382</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">11104</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">11103</span> Algorithms Inspired from Human Behavior Applied to Optimization of a Complex Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Curteanu">S. Curteanu</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Leon"> F. Leon</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gavrilescu"> M. Gavrilescu</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Floria"> S. A. Floria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models. <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=human%20behaviors%20of%20learning%20and%20cooperation" title=" human behaviors of learning and cooperation"> human behaviors of learning and cooperation</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20competitive%20behavior" title=" human competitive behavior"> human competitive behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithms" title=" optimization algorithms"> optimization algorithms</a> </p> <a href="https://publications.waset.org/abstracts/149011/algorithms-inspired-from-human-behavior-applied-to-optimization-of-a-complex-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149011.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">108</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">11102</span> Integrating Knowledge Distillation of Multiple Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Jindong">Min Jindong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Mingxia"> Wang Mingxia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance. <p class="card-text"><strong>Keywords:</strong> <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=knowledge%20distillation" title=" knowledge distillation"> knowledge distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20network" title=" convolutional network"> convolutional network</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20compression" title=" model compression"> model compression</a> </p> <a href="https://publications.waset.org/abstracts/148652/integrating-knowledge-distillation-of-multiple-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148652.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">278</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">11101</span> Person Re-Identification using Siamese Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sello%20Mokwena">Sello Mokwena</a>, <a href="https://publications.waset.org/abstracts/search?q=Monyepao%20Thabang"> Monyepao Thabang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20network" title="camera network">camera network</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network%20topology" title=" convolutional neural network topology"> convolutional neural network topology</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20tracking" title=" person tracking"> person tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20re-identification" title=" person re-identification"> person re-identification</a>, <a href="https://publications.waset.org/abstracts/search?q=siamese" title=" siamese"> siamese</a> </p> <a href="https://publications.waset.org/abstracts/171989/person-re-identification-using-siamese-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171989.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">72</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">11100</span> Comparative Study of Bending Angle in Laser Forming Process Using Artificial Neural Network and Fuzzy Logic System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Hassani">M. Hassani</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Hassani"> Y. Hassani</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Ajudanioskooei"> N. Ajudanioskooei</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20N.%20Benvid"> N. N. Benvid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Laser Forming process as a non-contact thermal forming process is widely used to forming and bending of metallic and non-metallic sheets. In this process, according to laser irradiation along a specific path, sheet is bent. One of the most important output parameters in laser forming is bending angle that depends on process parameters such as physical and mechanical properties of materials, laser power, laser travel speed and the number of scan passes. In this paper, Artificial Neural Network and Fuzzy Logic System were used to predict of bending angle in laser forming process. Inputs to these models were laser travel speed and laser power. The comparison between artificial neural network and fuzzy logic models with experimental results has been shown both of these models have high ability to prediction of bending angles with minimum errors. <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=bending%20angle" title=" bending angle"> bending angle</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=laser%20forming" title=" laser forming"> laser forming</a> </p> <a href="https://publications.waset.org/abstracts/34045/comparative-study-of-bending-angle-in-laser-forming-process-using-artificial-neural-network-and-fuzzy-logic-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34045.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">597</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">11099</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">11098</span> Transport Related Air Pollution Modeling 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=K.%20D.%20Sharma">K. D. Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Parida"> M. Parida</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20S.%20Jain"> S. S. Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Anju%20Saini"> Anju Saini</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20K.%20Katiyar"> V. K. Katiyar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20quality%20management" title="air quality management">air quality management</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=meteorological%20variables" title=" meteorological variables"> meteorological variables</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20modeling" title=" statistical modeling"> statistical modeling</a> </p> <a href="https://publications.waset.org/abstracts/6439/transport-related-air-pollution-modeling-using-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6439.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">524</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">11097</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">11096</span> Neural Network Models for Actual Cost and Actual Duration Estimation in Construction Projects: Findings from Greece </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Panagiotis%20Karadimos">Panagiotis Karadimos</a>, <a href="https://publications.waset.org/abstracts/search?q=Leonidas%20Anthopoulos"> Leonidas Anthopoulos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=actual%20cost%20and%20duration" title="actual cost and duration">actual cost and duration</a>, <a href="https://publications.waset.org/abstracts/search?q=attribute%20selection" title=" attribute selection"> attribute selection</a>, <a href="https://publications.waset.org/abstracts/search?q=bridge%20construction" title=" bridge construction"> bridge construction</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=predicting%20models" title=" predicting models"> predicting models</a>, <a href="https://publications.waset.org/abstracts/search?q=FANN%20TOOL" title=" FANN TOOL"> FANN TOOL</a>, <a href="https://publications.waset.org/abstracts/search?q=WEKA" title=" WEKA"> WEKA</a> </p> <a href="https://publications.waset.org/abstracts/130972/neural-network-models-for-actual-cost-and-actual-duration-estimation-in-construction-projects-findings-from-greece" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130972.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">134</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">11095</span> Message Passing Neural Network (MPNN) Approach to Multiphase Diffusion in Reservoirs for Well Interconnection Assessments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Margarita%20Mayoral-Villa">Margarita Mayoral-Villa</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Klapp"> J. Klapp</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Di%20G.%20Sigalotti"> L. Di G. Sigalotti</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20E.%20V.%20Guzm%C3%A1n"> J. E. V. Guzmán</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automated learning techniques are widely applied in the energy sector to address challenging problems from a practical point of view. To this end, we discuss the implementation of a Message Passing algorithm (MPNN)within a Graph Neural Network(GNN)to leverage the neighborhood of a set of nodes during the aggregation process. This approach enables the characterization of multiphase diffusion processes in the reservoir, such that the flow paths underlying the interconnections between multiple wells may be inferred from previously available data on flow rates and bottomhole pressures. The results thus obtained compare favorably with the predictions produced by the Reduced Order Capacitance-Resistance Models (CRM) and suggest the potential of MPNNs to enhance the robustness of the forecasts while improving the computational efficiency. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiphase%20diffusion" title="multiphase diffusion">multiphase diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=message%20passing%20neural%20network" title=" message passing neural network"> message passing neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=well%20interconnection" title=" well interconnection"> well interconnection</a>, <a href="https://publications.waset.org/abstracts/search?q=interwell%20connectivity" title=" interwell connectivity"> interwell connectivity</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20neural%20network" title=" graph neural network"> graph neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=capacitance-resistance%20models" title=" capacitance-resistance models"> capacitance-resistance models</a> </p> <a href="https://publications.waset.org/abstracts/146840/message-passing-neural-network-mpnn-approach-to-multiphase-diffusion-in-reservoirs-for-well-interconnection-assessments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146840.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">149</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">11094</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">11093</span> MarginDistillation: Distillation for Face Recognition Neural Networks with Margin-Based Softmax</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Svitov%20David">Svitov David</a>, <a href="https://publications.waset.org/abstracts/search?q=Alyamkin%20Sergey"> Alyamkin Sergey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ArcFace" title="ArcFace">ArcFace</a>, <a href="https://publications.waset.org/abstracts/search?q=distillation" title=" distillation"> distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=margin-based%20softmax" title=" margin-based softmax"> margin-based softmax</a> </p> <a href="https://publications.waset.org/abstracts/127812/margindistillation-distillation-for-face-recognition-neural-networks-with-margin-based-softmax" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127812.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">146</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">11092</span> A Model for Diagnosis and Prediction of Coronavirus Using Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajjad%20Baghernezhad">Sajjad Baghernezhad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title="COVID-19">COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20technique" title=" decision support technique"> decision support technique</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=memetic%20algorithm" title=" memetic algorithm"> memetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/168424/a-model-for-diagnosis-and-prediction-of-coronavirus-using-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168424.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">66</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=feed-forward%20neural%20network%20models&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=feed-forward%20neural%20network%20models&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=feed-forward%20neural%20network%20models&page=4">4</a></li> <li class="page-item"><a class="page-link" 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