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networks multi-layer perceptron</title> <meta name="description" content="Search results for: neural networks multi-layer perceptron"> <meta name="keywords" content="neural networks multi-layer perceptron"> <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 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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="neural networks multi-layer perceptron"> <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> 3869</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: neural networks multi-layer perceptron</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3869</span> Modeling of Daily Global Solar Radiation Using Ann Techniques: A Case of Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Said%20Benkaciali">Said Benkaciali</a>, <a href="https://publications.waset.org/abstracts/search?q=Mourad%20Haddadi"> Mourad Haddadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdallah%20Khellaf"> Abdallah Khellaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Kacem%20Gairaa"> Kacem Gairaa</a>, <a href="https://publications.waset.org/abstracts/search?q=Mawloud%20Guermoui"> Mawloud Guermoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, many experiments were carried out to assess the influence of the input parameters on the performance of multilayer perceptron which is one the configuration of the artificial neural networks. To estimate the daily global solar radiation on the horizontal surface, we have developed some models by using seven combinations of twelve meteorological and geographical input parameters collected from a radiometric station installed at Ghardaïa city (southern of Algeria). For selecting of best combination which provides a good accuracy, six statistical formulas (or statistical indicators) have been evaluated, such as the root mean square errors, mean absolute errors, correlation coefficient, and determination coefficient. We noted that multilayer perceptron techniques have the best performance, except when the sunshine duration parameter is not included in the input variables. The maximum of determination coefficient and correlation coefficient are equal to 98.20 and 99.11%. On the other hand, some empirical models were developed to compare their performances with those of multilayer perceptron neural networks. Results obtained show that the neural networks techniques give the best performance compared to the empirical models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20models" title="empirical models">empirical models</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20neural%20network" title=" multilayer perceptron neural network"> multilayer perceptron neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20radiation" title=" solar radiation"> solar radiation</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20formulas" title=" statistical formulas"> statistical formulas</a> </p> <a href="https://publications.waset.org/abstracts/60646/modeling-of-daily-global-solar-radiation-using-ann-techniques-a-case-of-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60646.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">345</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">3868</span> Algorithm and Software Based on Multilayer Perceptron Neural Networks for Estimating Channel Use in the Spectral Decision Stage in Cognitive Radio Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danilo%20L%C3%B3pez">Danilo López</a>, <a href="https://publications.waset.org/abstracts/search?q=Johana%20Hern%C3%A1ndez"> Johana Hernández</a>, <a href="https://publications.waset.org/abstracts/search?q=Edwin%20Rivas"> Edwin Rivas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</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=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20user" title=" primary user"> primary user</a> </p> <a href="https://publications.waset.org/abstracts/61993/algorithm-and-software-based-on-multilayer-perceptron-neural-networks-for-estimating-channel-use-in-the-spectral-decision-stage-in-cognitive-radio-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61993.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">371</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">3867</span> Analysis of Multilayer Neural Network Modeling and Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danilo%20L%C3%B3pez">Danilo López</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelson%20Vera"> Nelson Vera</a>, <a href="https://publications.waset.org/abstracts/search?q=Luis%20Pedraza"> Luis Pedraza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title=" long short-term memory"> long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neuronal%20network" title=" recurrent neuronal network"> recurrent neuronal network</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20analysis" title=" mathematical analysis"> mathematical analysis</a> </p> <a href="https://publications.waset.org/abstracts/63507/analysis-of-multilayer-neural-network-modeling-and-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63507.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">420</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">3866</span> Multilayer Perceptron Neural Network for Rainfall-Water Level Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thohidul%20Islam">Thohidul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Md.%20Hamidul%20Haque"> Md. Hamidul Haque</a>, <a href="https://publications.waset.org/abstracts/search?q=Robin%20Kumar%20Biswas"> Robin Kumar Biswas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood%20forecasting" title="flood forecasting">flood forecasting</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=multilayer%20perceptron%20network" title=" multilayer perceptron network"> multilayer perceptron network</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/111291/multilayer-perceptron-neural-network-for-rainfall-water-level-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111291.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">172</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">3865</span> Signal Restoration Using Neural Network Based Equalizer for Nonlinear channels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Z.%20Zerdoumi">Z. Zerdoumi</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Benatia"> D. Benatia</a>, <a href="https://publications.waset.org/abstracts/search?q="></a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Chicouche">D. Chicouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP-DFE) trained with the back propagation algorithm. The capacity of the MLP-DFE to deal with nonlinear channels is evaluated. From simulation results it can be noted that the MLP based DFE improves significantly the restored signal quality, the steady state mean square error (MSE), and minimum Bit Error Rate (BER), when comparing with its conventional counterpart. <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=signal%20restoration" title=" signal restoration"> signal restoration</a>, <a href="https://publications.waset.org/abstracts/search?q=Nonlinear%20Channel%20equalization" title=" Nonlinear Channel equalization"> Nonlinear Channel equalization</a>, <a href="https://publications.waset.org/abstracts/search?q=equalization" title=" equalization "> equalization </a> </p> <a href="https://publications.waset.org/abstracts/24223/signal-restoration-using-neural-network-based-equalizer-for-nonlinear-channels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24223.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3864</span> Predicting Global Solar Radiation Using Recurrent Neural Networks and Climatological Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rami%20El-Hajj%20Mohamad">Rami El-Hajj Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Skafi"> Mahmoud Skafi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Massoud%20Haidar"> Ali Massoud Haidar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Several meteorological parameters were used for the prediction of monthly average daily global solar radiation on horizontal using recurrent neural networks (RNNs). Climatological data and measures, mainly air temperature, humidity, sunshine duration, and wind speed between 1995 and 2007 were used to design and validate a feed forward and recurrent neural network based prediction systems. In this paper we present our reference system based on a feed-forward multilayer perceptron (MLP) as well as the proposed approach based on an RNN model. The obtained results were promising and comparable to those obtained by other existing empirical and neural models. The experimental results showed the advantage of RNNs over simple MLPs when we deal with time series solar radiation predictions based on daily climatological data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20networks" title="recurrent neural networks">recurrent neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20solar%20radiation" title=" global solar radiation"> global solar radiation</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=gradient" title=" gradient"> gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20mean%20square%20error" title=" root mean square error"> root mean square error</a> </p> <a href="https://publications.waset.org/abstracts/2385/predicting-global-solar-radiation-using-recurrent-neural-networks-and-climatological-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2385.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">444</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">3863</span> Classification of Myoelectric Signals Using Multilayer Perceptron Neural Network with Back-Propagation Algorithm in a Wireless Surface Myoelectric Prosthesis of the Upper-Limb</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kevin%20D.%20Manalo">Kevin D. Manalo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jumelyn%20L.%20Torres"> Jumelyn L. Torres</a>, <a href="https://publications.waset.org/abstracts/search?q=Noel%20B.%20Linsangan"> Noel B. Linsangan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=field%20programmable%20gate%20array" title="field programmable gate array">field programmable gate array</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20neural%20network" title=" multilayer perceptron neural network"> multilayer perceptron neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=verilog" title=" verilog"> verilog</a>, <a href="https://publications.waset.org/abstracts/search?q=zigbee" title=" zigbee"> zigbee</a> </p> <a href="https://publications.waset.org/abstracts/19846/classification-of-myoelectric-signals-using-multilayer-perceptron-neural-network-with-back-propagation-algorithm-in-a-wireless-surface-myoelectric-prosthesis-of-the-upper-limb" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19846.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">389</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">3862</span> Maximum Power Point Tracking for Small Scale Wind Turbine Using Multilayer Perceptron Neural Network Implementation without Mechanical Sensor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piyangkun%20Kukutapan">Piyangkun Kukutapan</a>, <a href="https://publications.waset.org/abstracts/search?q=Siridech%20Boonsang"> Siridech Boonsang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article proposes maximum power point tracking without mechanical sensor using Multilayer Perceptron Neural Network (MLPNN). The aim of article is to reduce the cost and complexity but still retain efficiency. The experimental is that duty cycle is generated maximum power, if it has suitable qualification. The measured data from DC generator, voltage (V), current (I), power (P), turnover rate of power (dP), and turnover rate of voltage (dV) are used as input for MLPNN model. The output of this model is duty cycle for driving the converter. The experiment implemented using Arduino Uno board. This diagram is compared to MPPT using MLPNN and P&O control (Perturbation and Observation control). The experimental results show that the proposed MLPNN based approach is more efficiency than P&O algorithm for this application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=maximum%20power%20point%20tracking" title="maximum power point tracking">maximum power point tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20netural%20network" title=" multilayer perceptron netural network"> multilayer perceptron netural network</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20duty%20cycle" title=" optimal duty cycle"> optimal duty cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=DC%20generator" title=" DC generator"> DC generator</a> </p> <a href="https://publications.waset.org/abstracts/39653/maximum-power-point-tracking-for-small-scale-wind-turbine-using-multilayer-perceptron-neural-network-implementation-without-mechanical-sensor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39653.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">325</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">3861</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">3860</span> Combination of Artificial Neural Network Model and Geographic Information System for Prediction Water Quality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sirilak%20Areerachakul">Sirilak Areerachakul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly. <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=geographic%20information%20system" title=" geographic information system"> geographic information system</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality" title=" water quality"> water quality</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20science" title=" computer science"> computer science</a> </p> <a href="https://publications.waset.org/abstracts/8934/combination-of-artificial-neural-network-model-and-geographic-information-system-for-prediction-water-quality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8934.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">343</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">3859</span> A Multilayer Perceptron Neural Network Model Optimized by Genetic Algorithm for Significant Wave Height Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luis%20C.%20Parra">Luis C. Parra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=significant%20wave%20height" title="significant wave height">significant wave height</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20optimization" title=" machine learning optimization"> machine learning optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20neural%20networks" title=" multilayer perceptron neural networks"> multilayer perceptron neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/153526/a-multilayer-perceptron-neural-network-model-optimized-by-genetic-algorithm-for-significant-wave-height-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">107</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3858</span> A Neural Network Modelling Approach for Predicting Permeability from Well Logs Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chico%20Horacio%20Jose%20Sambo">Chico Horacio Jose Sambo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models. <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=permeability" title=" permeability"> permeability</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=well%20log" title=" well log "> well log </a> </p> <a href="https://publications.waset.org/abstracts/32636/a-neural-network-modelling-approach-for-predicting-permeability-from-well-logs-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32636.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">403</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">3857</span> Artificial Neural Networks and Geographic Information Systems for Coastal Erosion Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Angeliki%20Peponi">Angeliki Peponi</a>, <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Morgado"> Paulo Morgado</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Trindade"> Jorge Trindade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited. <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=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=coastal%20urban%20zones" title=" coastal urban zones"> coastal urban zones</a>, <a href="https://publications.waset.org/abstracts/search?q=erosion%20prediction" title=" erosion prediction"> erosion prediction</a> </p> <a href="https://publications.waset.org/abstracts/84741/artificial-neural-networks-and-geographic-information-systems-for-coastal-erosion-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84741.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">392</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">3856</span> Forecasting the Temperature at a Weather Station Using Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Debneil%20Saha%20Roy">Debneil Saha Roy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast horizon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short%20term%20memory" title=" long short term memory"> long short term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a> </p> <a href="https://publications.waset.org/abstracts/124787/forecasting-the-temperature-at-a-weather-station-using-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124787.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">177</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">3855</span> An Algorithm for Determining the Arrival Behavior of a Secondary User to a Base Station in Cognitive Radio Networks </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danilo%20L%C3%B3pez">Danilo López</a>, <a href="https://publications.waset.org/abstracts/search?q=Edwin%20Rivas"> Edwin Rivas</a>, <a href="https://publications.waset.org/abstracts/search?q=Leyla%20L%C3%B3pez"> Leyla López</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the development of an algorithm that predicts the arrival of a secondary user (SU) to a base station (BS) in a cognitive network based on infrastructure, requesting a Best Effort (BE) or Real Time (RT) type of service with a determined bandwidth (BW) implementing neural networks. The algorithm dynamically uses a neural network construction technique using the geometric pyramid topology and trains a Multilayer Perceptron Neural Networks (MLPNN) based on the historical arrival of an SU to estimate future applications. This will allow efficiently managing the information in the BS, since it precedes the arrival of the SUs in the stage of selection of the best channel in CRN. As a result, the software application determines the probability of arrival at a future time point and calculates the performance metrics to measure the effectiveness of the predictions made. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cognitive%20radio" title="cognitive radio">cognitive radio</a>, <a href="https://publications.waset.org/abstracts/search?q=base%20station" title=" base station"> base station</a>, <a href="https://publications.waset.org/abstracts/search?q=best%20effort" title=" best effort"> best effort</a>, <a href="https://publications.waset.org/abstracts/search?q=MLPNN" title=" MLPNN"> MLPNN</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time" title=" real time"> real time</a> </p> <a href="https://publications.waset.org/abstracts/62227/an-algorithm-for-determining-the-arrival-behavior-of-a-secondary-user-to-a-base-station-in-cognitive-radio-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62227.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">331</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">3854</span> Identifying a Drug Addict Person 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=Mustafa%20Al%20Sukar">Mustafa Al Sukar</a>, <a href="https://publications.waset.org/abstracts/search?q=Azzam%20Sleit"> Azzam Sleit</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullatif%20Abu-Dalhoum"> Abdullatif Abu-Dalhoum</a>, <a href="https://publications.waset.org/abstracts/search?q=Bassam%20Al-Kasasbeh"> Bassam Al-Kasasbeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drug%20addiction" title="drug addiction">drug addiction</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20%28MLP%29" title=" multilayer perceptron (MLP)"> multilayer perceptron (MLP)</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20system" title=" decision support system"> decision support system</a> </p> <a href="https://publications.waset.org/abstracts/42977/identifying-a-drug-addict-person-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42977.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">299</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">3853</span> Assisted Prediction of Hypertension Based on Heart Rate Variability and Improved Residual Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yong%20Zhao">Yong Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20He"> Jian He</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Zhang"> Cheng Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cardiovascular diseases caused by hypertension are extremely threatening to human health, and early diagnosis of hypertension can save a large number of lives. Traditional hypertension detection methods require special equipment and are difficult to detect continuous blood pressure changes. In this regard, this paper first analyzes the principle of heart rate variability (HRV) and introduces sliding window and power spectral density (PSD) to analyze the time domain features and frequency domain features of HRV, and secondly, designs an HRV-based hypertension prediction network by combining Resnet, attention mechanism, and multilayer perceptron, which extracts the frequency domain through the improved ResNet18 features through a modified ResNet18, its fusion with time-domain features through an attention mechanism, and the auxiliary prediction of hypertension through a multilayer perceptron. Finally, the network was trained and tested using the publicly available SHAREE dataset on PhysioNet, and the test results showed that this network achieved 92.06% prediction accuracy for hypertension and outperformed K Near Neighbor(KNN), Bayes, Logistic, and traditional Convolutional Neural Network(CNN) models in prediction performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title="feature extraction">feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20rate%20variability" title=" heart rate variability"> heart rate variability</a>, <a href="https://publications.waset.org/abstracts/search?q=hypertension" title=" hypertension"> hypertension</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20networks" title=" residual networks"> residual networks</a> </p> <a href="https://publications.waset.org/abstracts/165227/assisted-prediction-of-hypertension-based-on-heart-rate-variability-and-improved-residual-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165227.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3852</span> Detecting Earnings Management via Statistical and Neural Networks Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Namazi">Mohammad Namazi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Sadeghzadeh%20Maharluie"> Mohammad Sadeghzadeh Maharluie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earnings%20management" title="earnings management">earnings management</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20linear%20regression" title=" generalized linear regression"> generalized linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks%20multi-layer%20perceptron" title=" neural networks multi-layer perceptron"> neural networks multi-layer perceptron</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/29730/detecting-earnings-management-via-statistical-and-neural-networks-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29730.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">421</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">3851</span> Interbank Networks and the Benefits of Using Multilayer Structures </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danielle%20Sandler%20dos%20Passos">Danielle Sandler dos Passos</a>, <a href="https://publications.waset.org/abstracts/search?q=Helder%20Coelho"> Helder Coelho</a>, <a href="https://publications.waset.org/abstracts/search?q=Fl%C3%A1via%20Mori%20Sarti"> Flávia Mori Sarti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Complexity science seeks the understanding of systems adopting diverse theories from various areas. Network analysis has been gaining space and credibility, namely with the biological, social and economic systems. Significant part of the literature focuses only monolayer representations of connections among agents considering one level of their relationships, and excludes other levels of interactions, leading to simplistic results in network analysis. Therefore, this work aims to demonstrate the advantages of the use of multilayer networks for the representation and analysis of networks. For this, we analyzed an interbank network, composed of 42 banks, comparing the centrality measures of the agents (degree and PageRank) resulting from each method (monolayer <em>x</em> multilayer). This proved to be the most reliable and efficient the multilayer analysis for the study of the current networks and highlighted JP Morgan and Deutsche Bank as the most important banks of the analyzed network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complexity" title="complexity">complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=interbank%20networks" title=" interbank networks"> interbank networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20networks" title=" multilayer networks"> multilayer networks</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20analysis" title=" network analysis"> network analysis</a> </p> <a href="https://publications.waset.org/abstracts/92811/interbank-networks-and-the-benefits-of-using-multilayer-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92811.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">282</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">3850</span> A Neuro-Automata Decision Support System for the Control of Late Blight in Tomato Crops</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gizelle%20K.%20Vianna">Gizelle K. Vianna</a>, <a href="https://publications.waset.org/abstracts/search?q=Gustavo%20S.%20Oliveira"> Gustavo S. Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20V.%20Cunha"> Gabriel V. Cunha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of decision support systems in agriculture may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. In our work, we designed and implemented a decision support system for small tomatoes producers. This work investigates ways to recognize the late blight disease from the analysis of digital images of tomatoes, using a pair of multilayer perceptron neural networks. The networks outputs are used to generate repainted tomato images in which the injuries on the plant are highlighted, and to calculate the damage level of each plant. Those levels are then used to construct a situation map of a farm where a cellular automata simulates the outbreak evolution over the fields. The simulator can test different pesticides actions, helping in the decision on when to start the spraying and in the analysis of losses and gains of each choice of action. <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=cellular%20automata" title=" cellular automata"> cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20system" title=" decision support system"> decision support system</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/63771/a-neuro-automata-decision-support-system-for-the-control-of-late-blight-in-tomato-crops" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63771.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">455</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">3849</span> Online Authenticity Verification of a Biometric Signature Using Dynamic Time Warping Method and Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ga%C5%82ka%20Aleksandra">Gałka Aleksandra</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeli%C5%84ska%20Justyna"> Jelińska Justyna</a>, <a href="https://publications.waset.org/abstracts/search?q=Masiak%20Albert"> Masiak Albert</a>, <a href="https://publications.waset.org/abstracts/search?q=Walentukiewicz%20Krzysztof"> Walentukiewicz Krzysztof</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An offline signature is well-known however not the safest way to verify identity. Nowadays, to ensure proper authentication, i.e. in banking systems, multimodal verification is more widely used. In this paper the online signature analysis based on dynamic time warping (DTW) coupled with machine learning approaches has been presented. In our research signatures made with biometric pens were gathered. Signature features as well as their forgeries have been described. For verification of authenticity various methods were used including convolutional neural networks using DTW matrix and multilayer perceptron using sums of DTW matrix paths. System efficiency has been evaluated on signatures and signature forgeries collected on the same day. Results are presented and discussed in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20time%20warping" title="dynamic time warping">dynamic time warping</a>, <a href="https://publications.waset.org/abstracts/search?q=handwritten%20signature%20verification" title=" handwritten signature verification"> handwritten signature verification</a>, <a href="https://publications.waset.org/abstracts/search?q=feature-based%20recognition" title=" feature-based recognition"> feature-based recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20signature" title=" online signature"> online signature</a> </p> <a href="https://publications.waset.org/abstracts/153364/online-authenticity-verification-of-a-biometric-signature-using-dynamic-time-warping-method-and-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153364.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">175</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">3848</span> A Computer-Aided System for Detection and Classification of Liver Cirrhosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdel%20Hadi%20N.%20Ebraheim">Abdel Hadi N. Ebraheim</a>, <a href="https://publications.waset.org/abstracts/search?q=Eman%20Azomi"> Eman Azomi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nefisa%20A.%20Fahmy"> Nefisa A. Fahmy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=liver%20cirrhosis" title="liver cirrhosis">liver cirrhosis</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=support%20vector%20machine" title=" support vector machine"> support vector machine</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=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a> </p> <a href="https://publications.waset.org/abstracts/39260/a-computer-aided-system-for-detection-and-classification-of-liver-cirrhosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39260.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">461</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">3847</span> Optimization of Vertical Axis Wind Turbine Based on Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Affanuddin%20H.%20Siddique">Mohammed Affanuddin H. Siddique</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayesh%20S.%20Shukla"> Jayesh S. Shukla</a>, <a href="https://publications.waset.org/abstracts/search?q=Chetan%20B.%20Meshram"> Chetan B. Meshram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VAWT" title="VAWT">VAWT</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20design" title=" inverse design"> inverse design</a> </p> <a href="https://publications.waset.org/abstracts/91997/optimization-of-vertical-axis-wind-turbine-based-on-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91997.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">324</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">3846</span> A Neural Network Classifier for Estimation of the Degree of Infestation by Late Blight on Tomato Leaves</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gizelle%20K.%20Vianna">Gizelle K. Vianna</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20V.%20Cunha"> Gabriel V. Cunha</a>, <a href="https://publications.waset.org/abstracts/search?q=Gustavo%20S.%20Oliveira"> Gustavo S. Oliveira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight. <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=digital%20image%20processing" title=" digital image processing"> digital image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=phytosanitary" title=" phytosanitary"> phytosanitary</a> </p> <a href="https://publications.waset.org/abstracts/61726/a-neural-network-classifier-for-estimation-of-the-degree-of-infestation-by-late-blight-on-tomato-leaves" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61726.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">327</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">3845</span> Urban Land Cover from GF-2 Satellite Images Using Object Based and Neural Network Classifications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lamyaa%20Gamal%20El-Deen%20Taha">Lamyaa Gamal El-Deen Taha</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashraf%20Sharawi"> Ashraf Sharawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> China launched satellite GF-2 in 2014. This study deals with comparing nearest neighbor object-based classification and neural network classification methods for classification of the fused GF-2 image. Firstly, rectification of GF-2 image was performed. Secondly, a comparison between nearest neighbor object-based classification and neural network classification for classification of fused GF-2 was performed. Thirdly, the overall accuracy of classification and kappa index were calculated. Results indicate that nearest neighbor object-based classification is better than neural network classification for urban mapping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GF-2%20images" title="GF-2 images">GF-2 images</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction-rectification" title=" feature extraction-rectification"> feature extraction-rectification</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbour%20object%20based%20classification" title=" nearest neighbour object based classification"> nearest neighbour object based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation%20algorithms" title=" segmentation algorithms"> segmentation algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20classification" title=" neural network classification"> neural network classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a> </p> <a href="https://publications.waset.org/abstracts/84243/urban-land-cover-from-gf-2-satellite-images-using-object-based-and-neural-network-classifications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84243.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">389</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">3844</span> Intelligent System for Diagnosis Heart Attack Using Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oluwaponmile%20David%20Alao">Oluwaponmile David Alao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heart%20attack" title="heart attack">heart attack</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=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20system" title=" intelligent system"> intelligent system</a> </p> <a href="https://publications.waset.org/abstracts/33844/intelligent-system-for-diagnosis-heart-attack-using-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33844.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">655</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">3843</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">3842</span> INRAM-3DCNN: Multi-Scale Convolutional Neural Network Based on Residual and Attention Module Combined with Multilayer Perceptron for Hyperspectral Image Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianhong%20Xiang">Jianhong Xiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Rui%20Sun"> Rui Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Linyu%20Wang"> Linyu Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=INRAM-3DCNN" title="INRAM-3DCNN">INRAM-3DCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=residual" title=" residual"> residual</a>, <a href="https://publications.waset.org/abstracts/search?q=channel%20attention" title=" channel attention"> channel attention</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20image%20classification" title=" hyperspectral image classification"> hyperspectral image classification</a> </p> <a href="https://publications.waset.org/abstracts/177814/inram-3dcnn-multi-scale-convolutional-neural-network-based-on-residual-and-attention-module-combined-with-multilayer-perceptron-for-hyperspectral-image-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177814.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">79</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">3841</span> Application of Deep Neural Networks to Assess Corporate Credit Rating</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Parisa%20Golbayani">Parisa Golbayani</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20Wang"> Dan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ionut%C2%B8%20Florescu"> Ionut¸ Florescu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short%20term%20memory" title=" long short term memory"> long short term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20rating" title=" credit rating"> credit rating</a> </p> <a href="https://publications.waset.org/abstracts/130950/application-of-deep-neural-networks-to-assess-corporate-credit-rating" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130950.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">235</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">3840</span> A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks: Prediction of Influential Factors on Eating Behaviors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Kheirollahpour">Maryam Kheirollahpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Danaee"> Mahmoud Danaee</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Faisal%20Merican"> Amir Faisal Merican</a>, <a href="https://publications.waset.org/abstracts/search?q=Asma%20Ahmad%20Shariff"> Asma Ahmad Shariff</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20model" title="hybrid model">hybrid model</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20equation%20modeling" title=" structural equation modeling"> structural equation modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=eating%20behavior%20patterns" title=" eating behavior patterns"> eating behavior patterns</a> </p> <a 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