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prediction</title> <meta name="description" content="Search results for: churn prediction"> <meta name="keywords" content="churn prediction"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="churn prediction"> <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> 2245</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: churn prediction</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2155</span> Development of the Structure of the Knowledgebase for Countermeasures in the Knowledge Acquisition Process for Trouble Prediction in Healthcare Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shogo%20Kato">Shogo Kato</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisuke%20Okamoto"> Daisuke Okamoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Satoko%20Tsuru"> Satoko Tsuru</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshinori%20Iizuka"> Yoshinori Iizuka</a>, <a href="https://publications.waset.org/abstracts/search?q=Ryoko%20Shimono"> Ryoko Shimono</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Healthcare safety has been perceived important. It is essential to prevent troubles in healthcare processes for healthcare safety. Trouble prevention is based on trouble prediction using accumulated knowledge on processes, troubles, and countermeasures. However, information on troubles has not been accumulated in hospitals in the appropriate structure, and it has not been utilized effectively to prevent troubles. In the previous study, though a detailed knowledge acquisition process for trouble prediction was proposed, the knowledgebase for countermeasures was not involved. In this paper, we aim to propose the structure of the knowledgebase for countermeasures in the knowledge acquisition process for trouble prediction in healthcare process. We first design the structure of countermeasures and propose the knowledge representation form on countermeasures. Then, we evaluate the validity of the proposal, by applying it into an actual hospital. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trouble%20prevention" title="trouble prevention">trouble prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20structure" title=" knowledge structure"> knowledge structure</a>, <a href="https://publications.waset.org/abstracts/search?q=structured%20knowledge" title=" structured knowledge"> structured knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=reusable%20knowledge" title=" reusable knowledge"> reusable knowledge</a> </p> <a href="https://publications.waset.org/abstracts/37281/development-of-the-structure-of-the-knowledgebase-for-countermeasures-in-the-knowledge-acquisition-process-for-trouble-prediction-in-healthcare-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37281.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">367</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">2154</span> Intelligent Prediction System for Diagnosis of Heart Attack</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> Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heart%20disease" title="heart disease">heart disease</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=prediction%20system" title=" prediction system"> prediction system</a> </p> <a href="https://publications.waset.org/abstracts/33508/intelligent-prediction-system-for-diagnosis-of-heart-attack" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33508.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">450</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2153</span> Research on Air pollution Spatiotemporal Forecast Model Based on LSTM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=JingWei%20Yu">JingWei Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hong%20Yang%20Yu"> Hong Yang Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> At present, the increasingly serious air pollution in various cities of China has made people pay more attention to the air quality index(hereinafter referred to as AQI) of their living areas. To face this situation, it is of great significance to predict air pollution in heavily polluted areas. In this paper, based on the time series model of LSTM, a spatiotemporal prediction model of PM2.5 concentration in Mianyang, Sichuan Province, is established. The model fully considers the temporal variability and spatial distribution characteristics of PM2.5 concentration. The spatial correlation of air quality at different locations is based on the Air quality status of other nearby monitoring stations, including AQI and meteorological data to predict the air quality of a monitoring station. The experimental results show that the method has good prediction accuracy that the fitting degree with the actual measured data reaches more than 0.7, which can be applied to the modeling and prediction of the spatial and temporal distribution of regional PM2.5 concentration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LSTM" title="LSTM">LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=PM2.5" title=" PM2.5"> PM2.5</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=spatio-temporal%20prediction" title=" spatio-temporal prediction"> spatio-temporal prediction</a> </p> <a href="https://publications.waset.org/abstracts/147644/research-on-air-pollution-spatiotemporal-forecast-model-based-on-lstm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147644.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">2152</span> Multilayer Neural Network and Fuzzy Logic Based Software Quality Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sadaf%20Sahar">Sadaf Sahar</a>, <a href="https://publications.waset.org/abstracts/search?q=Usman%20Qamar"> Usman Qamar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadaf%20Ayaz"> Sadaf Ayaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the software development lifecycle, the quality prediction techniques hold a prime importance in order to minimize future design errors and expensive maintenance. There are many techniques proposed by various researchers, but with the increasing complexity of the software lifecycle model, it is crucial to develop a flexible system which can cater for the factors which in result have an impact on the quality of the end product. These factors include properties of the software development process and the product along with its operation conditions. In this paper, a neural network (perceptron) based software quality prediction technique is proposed. Using this technique, the stakeholders can predict the quality of the resulting software during the early phases of the lifecycle saving time and resources on future elimination of design errors and costly maintenance. This technique can be brought into practical use using successful training. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20quality" title="software quality">software quality</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=perception" title=" perception"> perception</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/78014/multilayer-neural-network-and-fuzzy-logic-based-software-quality-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78014.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">317</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">2151</span> Regional Adjustment to the Analytical Attenuation Coefficient in the GMPM BSSA 14 for the Region of Spain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gonzalez%20Carlos">Gonzalez Carlos</a>, <a href="https://publications.waset.org/abstracts/search?q=Martinez%20Fransisco"> Martinez Fransisco</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are various types of analysis that allow us to involve seismic phenomena that cause strong requirements for structures that are designed by society; one of them is a probabilistic analysis which works from prediction equations that have been created based on metadata seismic compiled in different regions. These equations form models that are used to describe the 5% damped pseudo spectra response for the various zones considering some easily known input parameters. The biggest problem for the creation of these models requires data with great robust statistics that support the results, and there are several places where this type of information is not available, for which the use of alternative methodologies helps to achieve adjustments to different models of seismic prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GMPM" title="GMPM">GMPM</a>, <a href="https://publications.waset.org/abstracts/search?q=5%25%20damped%20pseudo-response%20spectra" title=" 5% damped pseudo-response spectra"> 5% damped pseudo-response spectra</a>, <a href="https://publications.waset.org/abstracts/search?q=models%20of%20seismic%20prediction" title=" models of seismic prediction"> models of seismic prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=PSHA" title=" PSHA"> PSHA</a> </p> <a href="https://publications.waset.org/abstracts/151393/regional-adjustment-to-the-analytical-attenuation-coefficient-in-the-gmpm-bssa-14-for-the-region-of-spain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151393.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">76</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">2150</span> Market Index Trend Prediction using Deep Learning and Risk Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shervin%20Alaei">Shervin Alaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Moradi"> Reza Moradi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends. <p class="card-text"><strong>Keywords:</strong> <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=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=trend%20prediction" title=" trend prediction"> trend prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a> </p> <a href="https://publications.waset.org/abstracts/148318/market-index-trend-prediction-using-deep-learning-and-risk-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148318.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">156</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">2149</span> Performance and Emission Prediction in a Biodiesel Engine Fuelled with Honge Methyl Ester Using RBF Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiva%20Kumar">Shiva Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20S.%20Vijay"> G. S. Vijay</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20Pai%20P."> Srinivas Pai P.</a>, <a href="https://publications.waset.org/abstracts/search?q=Shrinivasa%20Rao%20B.%20R."> Shrinivasa Rao B. R.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present study RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tech and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20networks" title="radial basis function networks">radial basis function networks</a>, <a href="https://publications.waset.org/abstracts/search?q=emissions" title=" emissions"> emissions</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20parameters" title=" performance parameters"> performance parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20c%20means" title=" fuzzy c means"> fuzzy c means</a> </p> <a href="https://publications.waset.org/abstracts/26507/performance-and-emission-prediction-in-a-biodiesel-engine-fuelled-with-honge-methyl-ester-using-rbf-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26507.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">558</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">2148</span> Developing and Evaluating Clinical Risk Prediction Models for Coronary Artery Bypass Graft Surgery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Mohebbi">Mohammadreza Mohebbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Masoumeh%20Sanagou"> Masoumeh Sanagou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clinical%20prediction%20models" title="clinical prediction models">clinical prediction models</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical%20decision%20rule" title=" clinical decision rule"> clinical decision rule</a>, <a href="https://publications.waset.org/abstracts/search?q=prognosis" title=" prognosis"> prognosis</a>, <a href="https://publications.waset.org/abstracts/search?q=external%20validation" title=" external validation"> external validation</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20calibration" title=" model calibration"> model calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=biostatistics" title=" biostatistics"> biostatistics</a> </p> <a href="https://publications.waset.org/abstracts/73483/developing-and-evaluating-clinical-risk-prediction-models-for-coronary-artery-bypass-graft-surgery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73483.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">297</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">2147</span> A-Score, Distress Prediction Model with Earning Response during the Financial Crisis: Evidence from Emerging Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sumaira%20Ashraf">Sumaira Ashraf</a>, <a href="https://publications.waset.org/abstracts/search?q=Elisabete%20G.S.%20F%C3%A9lix"> Elisabete G.S. Félix</a>, <a href="https://publications.waset.org/abstracts/search?q=Z%C3%A9lia%20Serrasqueiro"> Zélia Serrasqueiro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional financial distress prediction models performed well to predict bankrupt and insolvent firms of the developed markets. Previous studies particularly focused on the predictability of financial distress, financial failure, and bankruptcy of firms. This paper contributes to the literature by extending the definition of financial distress with the inclusion of early warning signs related to quotation of face value, dividend/bonus declaration, annual general meeting, and listing fee. The study used five well-known distress prediction models to see if they have the ability to predict early warning signs of financial distress. Results showed that the predictive ability of the models varies over time and decreases specifically for the sample with early warning signs of financial distress. Furthermore, the study checked the differences in the predictive ability of the models with respect to the financial crisis. The results conclude that the predictive ability of the traditional financial distress prediction models decreases for the firms with early warning signs of financial distress and during the time of financial crisis. The study developed a new model comprising significant variables from the five models and one new variable earning response. This new model outperforms the old distress prediction models before, during and after the financial crisis. Thus, it can be used by researchers, organizations and all other concerned parties to indicate early warning signs for the emerging markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20distress" title="financial distress">financial distress</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20market" title=" emerging market"> emerging market</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20models" title=" prediction models"> prediction models</a>, <a href="https://publications.waset.org/abstracts/search?q=Z-Score" title=" Z-Score"> Z-Score</a>, <a href="https://publications.waset.org/abstracts/search?q=logit%20analysis" title=" logit analysis"> logit analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=probit%20model" title=" probit model"> probit model</a> </p> <a href="https://publications.waset.org/abstracts/81316/a-score-distress-prediction-model-with-earning-response-during-the-financial-crisis-evidence-from-emerging-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81316.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">243</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">2146</span> Research on Reservoir Lithology Prediction Based on Residual Neural Network and Squeeze-and- Excitation Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Kewen">Li Kewen</a>, <a href="https://publications.waset.org/abstracts/search?q=Su%20Zhaoxin"> Su Zhaoxin</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Xingmou"> Wang Xingmou</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhu%20Jian%20Bing"> Zhu Jian Bing </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development. <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=lithology" title=" lithology"> lithology</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20of%20reservoir" title=" prediction of reservoir"> prediction of reservoir</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20attributes" title=" seismic attributes "> seismic attributes </a> </p> <a href="https://publications.waset.org/abstracts/121343/research-on-reservoir-lithology-prediction-based-on-residual-neural-network-and-squeeze-and-excitation-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121343.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">2145</span> EDM for Prediction of Academic Trends and Patterns</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Trupti%20Diwan">Trupti Diwan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting student failure at school has changed into a difficult challenge due to both the large number of factors that can affect the reduced performance of students and the imbalanced nature of these kinds of data sets. This paper surveys the two elements needed to make prediction on Students’ Academic Performances which are parameters and methods. This paper also proposes a framework for predicting the performance of engineering students. Genetic programming can be used to predict student failure/success. Ranking algorithm is used to rank students according to their credit points. The framework can be used as a basis for the system implementation & prediction of students’ Academic Performance in Higher Learning Institute. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20data%20mining" title=" educational data mining"> educational data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20failure" title=" student failure"> student failure</a>, <a href="https://publications.waset.org/abstracts/search?q=grammar-based%20genetic%20programming" title=" grammar-based genetic programming"> grammar-based genetic programming</a> </p> <a href="https://publications.waset.org/abstracts/20702/edm-for-prediction-of-academic-trends-and-patterns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20702.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">422</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">2144</span> Discrete State Prediction Algorithm Design with Self Performance Enhancement Capacity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smail%20Tigani">Smail Tigani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ouzzif"> Mohamed Ouzzif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a discrete quantitative state prediction algorithm with intelligent behavior making it able to self-improve some performance aspects. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before the final decision. The auto-rectification mechanism is based on two parallel mathematical models. In one hand, the algorithm predicts the next state based on event transition matrix updated after each observation. In the other hand, the algorithm extracts its residues trend with a linear regression representing historical residues data-points in order to rectify the first decision if needs. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. Designed key performance indicator, computed during a Monte Carlo simulation, shows the advantages of the proposed approach compared with traditional one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20state" title="discrete state">discrete state</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Chains" title=" Markov Chains"> Markov Chains</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=auto-adaptive%20systems" title=" auto-adaptive systems"> auto-adaptive systems</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20Simulation" title=" Monte Carlo Simulation"> Monte Carlo Simulation</a> </p> <a href="https://publications.waset.org/abstracts/20238/discrete-state-prediction-algorithm-design-with-self-performance-enhancement-capacity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20238.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">498</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">2143</span> A Comparative Soft Computing Approach to Supplier Performance Prediction Using GEP and ANN Models: An Automotive Case Study </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Esmail%20Seyedi%20Bariran">Seyed Esmail Seyedi Bariran</a>, <a href="https://publications.waset.org/abstracts/search?q=Khairul%20Salleh%20Mohamed%20Sahari"> Khairul Salleh Mohamed Sahari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Supplier%20Performance%20Prediction" title="Supplier Performance Prediction">Supplier Performance Prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=GEP" title=" GEP"> GEP</a>, <a href="https://publications.waset.org/abstracts/search?q=Automotive" title=" Automotive"> Automotive</a>, <a href="https://publications.waset.org/abstracts/search?q=SAPCO" title=" SAPCO"> SAPCO</a> </p> <a href="https://publications.waset.org/abstracts/13162/a-comparative-soft-computing-approach-to-supplier-performance-prediction-using-gep-and-ann-models-an-automotive-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13162.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">419</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">2142</span> New Machine Learning Optimization Approach Based on Input Variables Disposition Applied for Time Series Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hervice%20Rom%C3%A9o%20Fogno%20Fotsoa">Hervice Roméo Fogno Fotsoa</a>, <a href="https://publications.waset.org/abstracts/search?q=Germaine%20Djuidje%20Kenmoe"> Germaine Djuidje Kenmoe</a>, <a href="https://publications.waset.org/abstracts/search?q=Claude%20Vidal%20Aloyem%20Kaz%C3%A9"> Claude Vidal Aloyem Kazé</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=input%20variable%20disposition" title="input variable disposition">input variable disposition</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=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20prediction" title=" time series prediction"> time series prediction</a> </p> <a href="https://publications.waset.org/abstracts/159742/new-machine-learning-optimization-approach-based-on-input-variables-disposition-applied-for-time-series-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159742.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">109</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">2141</span> Seismic Hazard Prediction Using Seismic Bumps: Artificial Neural Network Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Selma">Belkacem Selma</a>, <a href="https://publications.waset.org/abstracts/search?q=Boumediene%20Selma"> Boumediene Selma</a>, <a href="https://publications.waset.org/abstracts/search?q=Tourkia%20Guerzou"> Tourkia Guerzou</a>, <a href="https://publications.waset.org/abstracts/search?q=Abbes%20Labdelli"> Abbes Labdelli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earthquake%20prediction" title="earthquake prediction">earthquake prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20bumps" title=" seismic bumps"> seismic bumps</a> </p> <a href="https://publications.waset.org/abstracts/148564/seismic-hazard-prediction-using-seismic-bumps-artificial-neural-network-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148564.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">127</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">2140</span> Housing Price Prediction Using Machine Learning Algorithms: The Case of Melbourne City, Australia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=The%20Danh%20Phan">The Danh Phan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=house%20price%20prediction" title="house price prediction">house price prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20trees" title=" regression trees"> regression trees</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=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=stepwise" title=" stepwise"> stepwise</a> </p> <a href="https://publications.waset.org/abstracts/98230/housing-price-prediction-using-machine-learning-algorithms-the-case-of-melbourne-city-australia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98230.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">231</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">2139</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">2138</span> The Cardiac Diagnostic Prediction Applied to a Designed Holter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leonardo%20Juan%20Ram%C3%ADrez%20L%C3%B3pez">Leonardo Juan Ramírez López</a>, <a href="https://publications.waset.org/abstracts/search?q=Javier%20Oswaldo%20Rodriguez%20Velasquez"> Javier Oswaldo Rodriguez Velasquez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We have designed a Holter that measures the heart´s activity for over 24 hours, implemented a prediction methodology, and generate alarms as well as indicators to patients and treating physicians. Various diagnostic advances have been developed in clinical cardiology thanks to Holter implementation; however, their interpretation has largely been conditioned to clinical analysis and measurements adjusted to diverse population characteristics, thus turning it into a subjective examination. This, however, requires vast population studies to be validated that, in turn, have not achieved the ultimate goal: mortality prediction. Given this context, our Insight Research Group developed a mathematical methodology that assesses cardiac dynamics through entropy and probability, creating a numerical and geometrical attractor which allows quantifying the normalcy of chronic and acute disease as well as the evolution between such states, and our Tigum Research Group developed a holter device with 12 channels and advanced computer software. This has been shown in different contexts with 100% sensitivity and specificity results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attractor" title="attractor ">attractor </a>, <a href="https://publications.waset.org/abstracts/search?q=cardiac" title=" cardiac"> cardiac</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=holter" title=" holter"> holter</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical" title=" mathematical "> mathematical </a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/115339/the-cardiac-diagnostic-prediction-applied-to-a-designed-holter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115339.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">169</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">2137</span> Variable Refrigerant Flow (VRF) Zonal Load Prediction Using a Transfer Learning-Based Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Junyu%20Chen">Junyu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Xu"> Peng Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the context of global efforts to enhance building energy efficiency, accurate thermal load forecasting is crucial for both device sizing and predictive control. Variable Refrigerant Flow (VRF) systems are widely used in buildings around the world, yet VRF zonal load prediction has received limited attention. Due to differences between VRF zones in building-level prediction methods, zone-level load forecasting could significantly enhance accuracy. Given that modern VRF systems generate high-quality data, this paper introduces transfer learning to leverage this data and further improve prediction performance. This framework also addresses the challenge of predicting load for building zones with no historical data, offering greater accuracy and usability compared to pure white-box models. The study first establishes an initial variable set of VRF zonal building loads and generates a foundational white-box database using EnergyPlus. Key variables for VRF zonal loads are identified using methods including SRRC, PRCC, and Random Forest. XGBoost and LSTM are employed to generate pre-trained black-box models based on the white-box database. Finally, real-world data is incorporated into the pre-trained model using transfer learning to enhance its performance in operational buildings. In this paper, zone-level load prediction was integrated with transfer learning, and a framework was proposed to improve the accuracy and applicability of VRF zonal load prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=zonal%20load%20prediction" title="zonal load prediction">zonal load prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20refrigerant%20flow%20%28VRF%29%20system" title=" variable refrigerant flow (VRF) system"> variable refrigerant flow (VRF) system</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=energyplus" title=" energyplus"> energyplus</a> </p> <a href="https://publications.waset.org/abstracts/191023/variable-refrigerant-flow-vrf-zonal-load-prediction-using-a-transfer-learning-based-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191023.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">28</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">2136</span> Stock Market Prediction Using Convolutional Neural Network That Learns from a Graph</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mo-Se%20Lee">Mo-Se Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheol-Hwi%20Ahn"> Cheol-Hwi Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Kee-Young%20Kwahk"> Kee-Young Kwahk</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyunchul%20Ahn"> Hyunchul Ahn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market. <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=Korean%20stock%20market" title=" Korean stock market"> Korean stock market</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20prediction" title=" stock market prediction"> stock market prediction</a> </p> <a href="https://publications.waset.org/abstracts/80318/stock-market-prediction-using-convolutional-neural-network-that-learns-from-a-graph" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80318.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">425</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">2135</span> Using Neural Networks for Click Prediction of Sponsored Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afroze%20Ibrahim%20Baqapuri">Afroze Ibrahim Baqapuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilya%20Trofimov"> Ilya Trofimov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). Click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture of solving CTR prediction problem by combining artificial neural networks (ANN) with decision trees. First, we compare ANN with respect to other popular machine learning models being used for this task. Then we go on to combine ANN with MatrixNet (proprietary implementation of boosted trees) and evaluate the performance of the system as a whole. The results show that our approach provides a significant improvement over existing models. <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=sponsored%20search" title=" sponsored search"> sponsored search</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20advertisement" title=" web advertisement"> web advertisement</a>, <a href="https://publications.waset.org/abstracts/search?q=click%20prediction" title=" click prediction"> click prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=click-through%20rate" title=" click-through rate"> click-through rate</a> </p> <a href="https://publications.waset.org/abstracts/22874/using-neural-networks-for-click-prediction-of-sponsored-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22874.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">572</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">2134</span> Residual Life Prediction for a System Subject to Condition Monitoring and Two Failure Modes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akram%20Khaleghei">Akram Khaleghei</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghosheh%20Balagh"> Ghosheh Balagh</a>, <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis"> Viliam Makis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we investigate the residual life prediction problem for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model with unknown parameters. The parameter estimation procedure based on an EM algorithm is developed and the formulas for the conditional reliability function and the mean residual life are derived, illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20system" title="partially observable system">partially observable system</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20Markov%20model" title=" hidden Markov model"> hidden Markov model</a>, <a href="https://publications.waset.org/abstracts/search?q=competing%20risks" title=" competing risks"> competing risks</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20life%20prediction" title=" residual life prediction"> residual life prediction</a> </p> <a href="https://publications.waset.org/abstracts/6352/residual-life-prediction-for-a-system-subject-to-condition-monitoring-and-two-failure-modes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6352.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">415</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2133</span> Loan Repayment Prediction Using Machine Learning: Model Development, Django Web Integration and Cloud Deployment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seun%20Mayowa%20Sunday">Seun Mayowa Sunday</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbor" title="k-nearest neighbor">k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=django" title=" django"> django</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=alibaba%20cloud" title=" alibaba cloud"> alibaba cloud</a> </p> <a href="https://publications.waset.org/abstracts/155162/loan-repayment-prediction-using-machine-learning-model-development-django-web-integration-and-cloud-deployment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155162.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">136</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">2132</span> Deadline Missing Prediction for Mobile Robots through the Use of Historical Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edwaldo%20R.%20B.%20Monteiro">Edwaldo R. B. Monteiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricia%20D.%20M.%20Plentz"> Patricia D. M. Plentz</a>, <a href="https://publications.waset.org/abstracts/search?q=Edson%20R.%20De%20Pieri"> Edson R. De Pieri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile robotics is gaining an increasingly important role in modern society. Several potentially dangerous or laborious tasks for human are assigned to mobile robots, which are increasingly capable. Many of these tasks need to be performed within a specified period, i.e., meet a deadline. Missing the deadline can result in financial and/or material losses. Mechanisms for predicting the missing of deadlines are fundamental because corrective actions can be taken to avoid or minimize the losses resulting from missing the deadline. In this work we propose a simple but reliable deadline missing prediction mechanism for mobile robots through the use of historical data and we use the Pioneer 3-DX robot for experiments and simulations, one of the most popular robots in academia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deadline%20missing" title="deadline missing">deadline missing</a>, <a href="https://publications.waset.org/abstracts/search?q=historical%20data" title=" historical data"> historical data</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20robots" title=" mobile robots"> mobile robots</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20mechanism" title=" prediction mechanism"> prediction mechanism</a> </p> <a href="https://publications.waset.org/abstracts/8352/deadline-missing-prediction-for-mobile-robots-through-the-use-of-historical-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8352.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">401</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">2131</span> Useful Lifetime Prediction of Rail Pads for High Speed Trains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chang%20Su%20Woo">Chang Su Woo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyun%20Sung%20Park"> Hyun Sung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Useful lifetime evaluations of rail-pads were very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of rail pads. In this study, we performed properties and accelerated heat aging tests of rail pads considering degradation factors and all environmental conditions including operation, and then derived a lifetime prediction equation according to changes in hardness, thickness, and static spring constants in the Arrhenius plot to establish how to estimate the aging of rail pads. With the useful lifetime prediction equation, the lifetime of e-clip pads was 2.5 years when the change in hardness was 10% at 25°C; and that of f-clip pads was 1.7 years. When the change in thickness was 10%, the lifetime of e-clip pads and f-clip pads is 2.6 years respectively. The results obtained in this study to estimate the useful lifetime of rail pads for high speed trains can be used for determining the maintenance and replacement schedule for rail pads. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rail%20pads" title="rail pads">rail pads</a>, <a href="https://publications.waset.org/abstracts/search?q=accelerated%20test" title=" accelerated test"> accelerated test</a>, <a href="https://publications.waset.org/abstracts/search?q=Arrhenius%20plot" title=" Arrhenius plot"> Arrhenius plot</a>, <a href="https://publications.waset.org/abstracts/search?q=useful%20lifetime%20prediction" title=" useful lifetime prediction"> useful lifetime prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanical%20engineering%20design" title=" mechanical engineering design"> mechanical engineering design</a> </p> <a href="https://publications.waset.org/abstracts/3182/useful-lifetime-prediction-of-rail-pads-for-high-speed-trains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3182.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">326</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">2130</span> Using Water Erosion Prediction Project Simulation Model for Studying Some Soil Properties in Egypt</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20A.%20Mansour">H. A. Mansour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this research work is studying the water use prediction, prediction technology for water use by action agencies, and others involved in conservation, planning, and environmental assessment of the Water Erosion Prediction Project (WEPP) simulation model. Models the important physical, processes governing erosion in Egypt (climate, infiltration, runoff, ET, detachment by raindrops, detachment by flowing water, deposition, etc.). Simulation of the non-uniform slope, soils, cropping/management., and Egyptian databases for climate, soils, and crops. The study included important parameters in Egyptian conditions as follows: Water Balance & Percolation, Soil Component (Tillage impacts), Plant Growth & Residue Decomposition, Overland Flow Hydraulics. It could be concluded that we can adapt the WEPP simulation model to determining the previous important parameters under Egyptian conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=WEPP" title="WEPP">WEPP</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptation" title=" adaptation"> adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20properties" title=" soil properties"> soil properties</a>, <a href="https://publications.waset.org/abstracts/search?q=tillage%20impacts" title=" tillage impacts"> tillage impacts</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20balance" title=" water balance"> water balance</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20percolation" title=" soil percolation"> soil percolation</a> </p> <a href="https://publications.waset.org/abstracts/55371/using-water-erosion-prediction-project-simulation-model-for-studying-some-soil-properties-in-egypt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55371.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">297</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">2129</span> Development of Fuzzy Logic and Neuro-Fuzzy Surface Roughness Prediction Systems Coupled with Cutting Current in Milling Operation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20C.%20Chen">Joseph C. Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Venkata%20Mohan%20Kudapa"> Venkata Mohan Kudapa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title="surface roughness">surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=input%20current" title=" input current"> input current</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=neuro-fuzzy" title=" neuro-fuzzy"> neuro-fuzzy</a>, <a href="https://publications.waset.org/abstracts/search?q=milling%20operations" title=" milling operations"> milling operations</a> </p> <a href="https://publications.waset.org/abstracts/105245/development-of-fuzzy-logic-and-neuro-fuzzy-surface-roughness-prediction-systems-coupled-with-cutting-current-in-milling-operation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105245.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">145</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">2128</span> Neural Network Based Approach of Software Maintenance Prediction for Laboratory Information System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vuk%20M.%20Popovic">Vuk M. Popovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Dunja%20D.%20Popovic"> Dunja D. Popovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software maintenance phase is started once a software project has been developed and delivered. After that, any modification to it corresponds to maintenance. Software maintenance involves modifications to keep a software project usable in a changed or a changing environment, to correct discovered faults, and modifications, and to improve performance or maintainability. Software maintenance and management of software maintenance are recognized as two most important and most expensive processes in a life of a software product. This research is basing the prediction of maintenance, on risks and time evaluation, and using them as data sets for working with neural networks. The aim of this paper is to provide support to project maintenance managers. They will be able to pass the issues planned for the next software-service-patch to the experts, for risk and working time evaluation, and afterward to put all data to neural networks in order to get software maintenance prediction. This process will lead to the more accurate prediction of the working hours needed for the software-service-patch, which will eventually lead to better planning of budget for the software maintenance projects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=laboratory%20information%20system" title="laboratory information system">laboratory information system</a>, <a href="https://publications.waset.org/abstracts/search?q=maintenance%20engineering" title=" maintenance engineering"> maintenance engineering</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=software%20maintenance" title=" software maintenance"> software maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20maintenance%20costs" title=" software maintenance costs"> software maintenance costs</a> </p> <a href="https://publications.waset.org/abstracts/68789/neural-network-based-approach-of-software-maintenance-prediction-for-laboratory-information-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68789.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">358</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">2127</span> Optimized Preprocessing for Accurate and Efficient Bioassay Prediction with Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeff%20Clarine">Jeff Clarine</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang-Shyh%20Peng"> Chang-Shyh Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisy%20Sang"> Daisy Sang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioassay" title="bioassay">bioassay</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=preprocessing" title=" preprocessing"> preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20screen" title=" virtual screen"> virtual screen</a> </p> <a href="https://publications.waset.org/abstracts/77481/optimized-preprocessing-for-accurate-and-efficient-bioassay-prediction-with-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77481.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2126</span> Discussing Embedded versus Central Machine Learning 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=Anne-Lena%20Kampen">Anne-Lena Kampen</a>, <a href="https://publications.waset.org/abstracts/search?q=%C3%98ivind%20Kure"> Øivind Kure</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=central%20machine%20learning" title="central machine learning">central machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=embedded%20machine%20learning" title=" embedded machine learning"> embedded machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption" title=" energy consumption"> energy consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20machine%20learning" title=" local machine learning"> local machine learning</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=WSN" title=" WSN"> WSN</a> </p> <a href="https://publications.waset.org/abstracts/127522/discussing-embedded-versus-central-machine-learning-in-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127522.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">153</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=churn%20prediction&page=3" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=churn%20prediction&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=churn%20prediction&page=2">2</a></li> <li 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