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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="Grey prediction model"> <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> 17976</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Grey prediction model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17976</span> Life Prediction Method of Lithium-Ion Battery Based on Grey Support Vector Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaogang%20Li">Xiaogang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Jieqiong%20Miao"> Jieqiong Miao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Grey%20prediction%20model" title="Grey prediction model">Grey prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=trigonometric%20functions" title=" trigonometric functions"> trigonometric functions</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20mean%20square%20error" title=" root mean square error"> root mean square error</a> </p> <a href="https://publications.waset.org/abstracts/29370/life-prediction-method-of-lithium-ion-battery-based-on-grey-support-vector-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29370.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">17975</span> Grey Prediction of Atmospheric Pollutants in Shanghai Based on GM(1,1) Model Group</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diqin%20Qi">Diqin Qi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiaming%20Li"> Jiaming Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Siman%20Li"> Siman Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on the use of the three-point smoothing method for selectively processing original data columns, this paper establishes a group of grey GM(1,1) models to predict the concentration ranges of four major air pollutants in Shanghai from 2023 to 2024. The results indicate that PM₁₀, SO₂, and NO₂ maintain the national Grade I standards, while the concentration of PM₂.₅ has decreased but still remains within the national Grade II standards. Combining the forecast results, recommendations are provided for the Shanghai municipal government's efforts in air pollution prevention and control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=atmospheric%20pollutant%20prediction" title="atmospheric pollutant prediction">atmospheric pollutant prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=Grey%20GM%281" title=" Grey GM(1"> Grey GM(1</a>, <a href="https://publications.waset.org/abstracts/search?q=1%29" title=" 1)"> 1)</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20group" title=" model group"> model group</a>, <a href="https://publications.waset.org/abstracts/search?q=three-point%20smoothing%20method" title=" three-point smoothing method"> three-point smoothing method</a> </p> <a href="https://publications.waset.org/abstracts/185942/grey-prediction-of-atmospheric-pollutants-in-shanghai-based-on-gm11-model-group" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185942.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">35</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">17974</span> Grey Wolf Optimization Technique for Predictive Analysis of Products in E-Commerce: An Adaptive Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shital%20Suresh%20Borse">Shital Suresh Borse</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayalaxmi%20Kadroli"> Vijayalaxmi Kadroli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction%20analysis" title="prediction analysis">prediction analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title=" e-commerce"> e-commerce</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=grey%20wolf%20optimization" title=" grey wolf optimization"> grey wolf optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/148039/grey-wolf-optimization-technique-for-predictive-analysis-of-products-in-e-commerce-an-adaptive-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148039.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">113</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">17973</span> A New Model for Production Forecasting in ERP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20F.%20Wong">S. F. Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20I.%20Ho"> W. I. Ho</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Lin"> B. Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Q.%20Huang"> Q. Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ERP has been used in many enterprises for management, the accuracy of the production forecasting module is vital to the decision making of the enterprise, and the profit is affected directly. Therefore, enhancing the accuracy of the production forecasting module can also increase the efficiency and profitability. To deal with a lot of data, a suitable, reliable and accurate statistics model is necessary. LSSVM and Grey System are two main models to be studied in this paper, and a case study is used to demonstrate how the combination model is effective to the result of forecasting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ERP" title="ERP">ERP</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20system" title=" grey system"> grey system</a>, <a href="https://publications.waset.org/abstracts/search?q=LSSVM" title=" LSSVM"> LSSVM</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20forecasting" title=" production forecasting"> production forecasting</a> </p> <a href="https://publications.waset.org/abstracts/3348/a-new-model-for-production-forecasting-in-erp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3348.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">463</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">17972</span> Automatic Flood Prediction Using Rainfall Runoff Model in Moravian-Silesian Region</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sir">B. Sir</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Podhoranyi"> M. Podhoranyi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kuchar"> S. Kuchar</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kocyan"> T. Kocyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood" title="flood">flood</a>, <a href="https://publications.waset.org/abstracts/search?q=HEC-HMS" title=" HEC-HMS"> HEC-HMS</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=runoff" title=" runoff "> runoff </a> </p> <a href="https://publications.waset.org/abstracts/20151/automatic-flood-prediction-using-rainfall-runoff-model-in-moravian-silesian-region" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20151.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">395</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">17971</span> SEMCPRA-Sar-Esembled Model for Climate Prediction in Remote Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamalpreet%20Kaur">Kamalpreet Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Renu%20Dhir"> Renu Dhir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate" title="climate">climate</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/178864/semcpra-sar-esembled-model-for-climate-prediction-in-remote-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178864.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">75</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">17970</span> Analyzing the Results of Buildings Energy Audit by Using Grey Set Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tooraj%20Karimi">Tooraj Karimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Sadeghi%20Moghadam"> Mohammadreza Sadeghi Moghadam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Grey set theory has the advantage of using fewer data to analyze many factors, and it is therefore more appropriate for system study rather than traditional statistical regression which require massive data, normal distribution in the data and few variant factors. So, in this paper grey clustering and entropy of coefficient vector of grey evaluations are used to analyze energy consumption in buildings of the Oil Ministry in Tehran. In fact, this article intends to analyze the results of energy audit reports and defines most favorable characteristics of system, which is energy consumption of buildings, and most favorable factors affecting these characteristics in order to modify and improve them. According to the results of the model, ‘the real Building Load Coefficient’ has been selected as the most important system characteristic and ‘uncontrolled area of the building’ has been diagnosed as the most favorable factor which has the greatest effect on energy consumption of building. Grey clustering in this study has been used for two purposes: First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, grey clustering with variable weights has been used to classify all buildings in three categories named ‘no standard deviation’, ‘low standard deviation’ and ‘non- standard’. Entropy of coefficient vector of Grey evaluations is calculated to investigate greyness of results. It shows that among the 38 buildings surveyed in terms of energy consumption, 3 cases are in standard group, 24 cases are in ‘low standard deviation’ group and 11 buildings are completely non-standard. In addition, clustering greyness of 13 buildings is less than 0.5 and average uncertainly of clustering results is 66%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20audit" title="energy audit">energy audit</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20set%20theory" title=" grey set theory"> grey set theory</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20incidence%20matrixes" title=" grey incidence matrixes"> grey incidence matrixes</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20clustering" title=" grey clustering"> grey clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=Iran%20oil%20ministry" title=" Iran oil ministry"> Iran oil ministry</a> </p> <a href="https://publications.waset.org/abstracts/9423/analyzing-the-results-of-buildings-energy-audit-by-using-grey-set-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9423.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">373</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">17969</span> Influence Analysis of Macroeconomic Parameters on Real Estate Price Variation in Taipei, Taiwan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Li">Li Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai-Hsuan%20Chu"> Kai-Hsuan Chu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well known that the real estate price depends on a lot of factors. Each house current value is dependent on the location, room number, transportation, living convenience, year and surrounding environments. Although, there are different experienced models for housing agent to estimate the price, it is a case by case study without overall dynamic variation investigation. However, many economic parameters may more or less influence the real estate price variation. Here, the influences of most macroeconomic parameters on real estate price are investigated individually based on least-square scheme and grey correlation strategy. Then those parameters are classified into leading indices, simultaneous indices and laggard indices. In addition, the leading time period is evaluated based on least square method. The important leading and simultaneous indices can be used to establish an artificial intelligent neural network model for real estate price variation prediction. The real estate price variation of Taipei, Taiwan during 2005 ~ 2017 are chosen for this research data analysis and validation. The results show that the proposed method has reasonable prediction function for real estate business reference. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=real%20estate%20price" title="real estate price">real estate price</a>, <a href="https://publications.waset.org/abstracts/search?q=least-square" title=" least-square"> least-square</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20correlation" title=" grey correlation"> grey correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=macroeconomics" title=" macroeconomics"> macroeconomics</a> </p> <a href="https://publications.waset.org/abstracts/100459/influence-analysis-of-macroeconomic-parameters-on-real-estate-price-variation-in-taipei-taiwan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100459.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">198</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17968</span> Multi-Response Optimization of CNC Milling Parameters Using Taguchi Based Grey Relational Analysis for AA6061 T6 Aluminium Alloy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varsha%20Singh">Varsha Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Kishan%20Fuse"> Kishan Fuse</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a study of the grey-Taguchi method to optimize CNC milling parameters of AA6061 T6 aluminium alloy. Grey-Taguchi method combines Taguchi method based design of experiments (DOE) with grey relational analysis (GRA). Multi-response optimization of different quality characteristics as surface roughness, material removal rate, cutting forces is done using grey relational analysis (GRA). The milling parameters considered for experiments include cutting speed, feed per tooth, and depth of cut. Each parameter with three levels is selected. A grey relational grade is used to estimate overall quality characteristics performance. The Taguchi’s L9 orthogonal array is used for design of experiments. MINITAB 17 software is used for optimization. Analysis of variance (ANOVA) is used to identify most influencing parameter. The experimental results show that grey relational analysis is effective method for optimizing multi-response characteristics. Optimum results are finally validated by performing confirmation test. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANOVA" title="ANOVA">ANOVA</a>, <a href="https://publications.waset.org/abstracts/search?q=CNC%20milling" title=" CNC milling"> CNC milling</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20relational%20analysis" title=" grey relational analysis"> grey relational analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-response%20optimization" title=" multi-response optimization"> multi-response optimization</a> </p> <a href="https://publications.waset.org/abstracts/61719/multi-response-optimization-of-cnc-milling-parameters-using-taguchi-based-grey-relational-analysis-for-aa6061-t6-aluminium-alloy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61719.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">309</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">17967</span> Multistage Data Envelopment Analysis Model for Malmquist Productivity Index Using Grey's System Theory to Evaluate Performance of Electric Power Supply Chain in Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mesbaholdin%20Salami">Mesbaholdin Salami</a>, <a href="https://publications.waset.org/abstracts/search?q=Farzad%20Movahedi%20Sobhani"> Farzad Movahedi Sobhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Sadegh%20Ghazizadeh"> Mohammad Sadegh Ghazizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evaluation of organizational performance is among the most important measures that help organizations and entities continuously improve their efficiency. Organizations can use the existing data and results from the comparison of units under investigation to obtain an estimation of their performance. The Malmquist Productivity Index (MPI) is an important index in the evaluation of overall productivity, which considers technological developments and technical efficiency at the same time. This article proposed a model based on the multistage MPI, considering limited data (Grey’s theory). This model can evaluate the performance of units using limited and uncertain data in a multistage process. It was applied by the electricity market manager to Iran’s electric power supply chain (EPSC), which contains uncertain data, to evaluate the performance of its actors. Results from solving the model showed an improvement in the accuracy of future performance of the units under investigation, using the Grey’s system theory. This model can be used in all case studies, in which MPI is used and there are limited or uncertain data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malmquist%20Index" title="Malmquist Index">Malmquist Index</a>, <a href="https://publications.waset.org/abstracts/search?q=Grey%27s%20Theory" title=" Grey's Theory"> Grey's Theory</a>, <a href="https://publications.waset.org/abstracts/search?q=CCR%20Model" title=" CCR Model"> CCR Model</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20data%20envelopment%20analysis" title=" network data envelopment analysis"> network data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Iran%20electricity%20power%20chain" title=" Iran electricity power chain"> Iran electricity power chain</a> </p> <a href="https://publications.waset.org/abstracts/102411/multistage-data-envelopment-analysis-model-for-malmquist-productivity-index-using-greys-system-theory-to-evaluate-performance-of-electric-power-supply-chain-in-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102411.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">165</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">17966</span> Diesel Fault Prediction Based on Optimized Gray Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Han%20Bing">Han Bing</a>, <a href="https://publications.waset.org/abstracts/search?q=Yin%20Zhenjie"> Yin Zhenjie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20prediction" title="fault prediction">fault prediction</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=GM%281" title=" GM(1"> GM(1</a>, <a href="https://publications.waset.org/abstracts/search?q=5%29%20genetic%20algorithm" title="5) genetic algorithm">5) genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=GBPGA" title=" GBPGA"> GBPGA</a> </p> <a href="https://publications.waset.org/abstracts/48844/diesel-fault-prediction-based-on-optimized-gray-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48844.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">306</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">17965</span> Using Combination of Sets of Features of Molecules for Aqueous Solubility Prediction: A Random Forest Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammet%20Baldan">Muhammet Baldan</a>, <a href="https://publications.waset.org/abstracts/search?q=Emel%20Timu%C3%A7in"> Emel Timuçin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solubility" title="solubility">solubility</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=molecular%20descriptors" title=" molecular descriptors"> molecular descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=maccs%20keys" title=" maccs keys"> maccs keys</a> </p> <a href="https://publications.waset.org/abstracts/186736/using-combination-of-sets-of-features-of-molecules-for-aqueous-solubility-prediction-a-random-forest-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186736.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">47</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17964</span> Analysis on Prediction Models of TBM Performance and Selection of Optimal Input Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hang%20Lo%20Lee">Hang Lo Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ki%20Il%20Song"> Ki Il Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Hee%20Hwan%20Ryu"> Hee Hwan Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TBM%20performance%20prediction%20model" title="TBM performance prediction model">TBM performance prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20system" title=" classification system"> classification system</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20regression%20analysis" title=" simple regression analysis"> simple regression analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20analysis" title=" residual analysis"> residual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20input%20parameters" title=" optimal input parameters"> optimal input parameters</a> </p> <a href="https://publications.waset.org/abstracts/52738/analysis-on-prediction-models-of-tbm-performance-and-selection-of-optimal-input-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52738.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">309</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">17963</span> Study on Optimal Control Strategy of PM2.5 in Wuhan, China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiuling%20Xie">Qiuling Xie</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanliang%20Zhu"> Shanliang Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zongdi%20Sun"> Zongdi Sun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we analyzed the correlation relationship among PM2.5 from other five Air Quality Indices (AQIs) based on the grey relational degree, and built a multivariate nonlinear regression equation model of PM2.5 and the five monitoring indexes. For the optimal control problem of PM2.5, we took the partial large Cauchy distribution of membership equation as satisfaction function. We established a nonlinear programming model with the goal of maximum performance to price ratio. And the optimal control scheme is given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grey%20relational%20degree" title="grey relational degree">grey relational degree</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20linear%20regression" title=" multiple linear regression"> multiple linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20function" title=" membership function"> membership function</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20programming" title=" nonlinear programming"> nonlinear programming</a> </p> <a href="https://publications.waset.org/abstracts/54538/study-on-optimal-control-strategy-of-pm25-in-wuhan-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">300</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17962</span> An Improved Prediction Model of Ozone Concentration Time Series Based on Chaotic Approach </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nor%20Zila%20Abd%20Hamid">Nor Zila Abd Hamid</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Salmi%20M.%20Noorani"> Mohd Salmi M. Noorani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chaotic%20approach" title="chaotic approach">chaotic approach</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space" title=" phase space"> phase space</a>, <a href="https://publications.waset.org/abstracts/search?q=Cao%20method" title=" Cao method"> Cao method</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20linear%20approximation%20method" title=" local linear approximation method"> local linear approximation method</a> </p> <a href="https://publications.waset.org/abstracts/2015/an-improved-prediction-model-of-ozone-concentration-time-series-based-on-chaotic-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2015.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17961</span> Development of Imprinting and Replica Molding of Soft Mold Curved Surface</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yung-Jin%20Weng">Yung-Jin Weng</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Chi%20Chang"> Chia-Chi Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun-Yu%20Tsai"> Chun-Yu Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is focused on the research of imprinting and replica molding of quasi-grey scale soft mold curved surface microstructure mold. In this paper, a magnetic photocuring forming system is first developed and built independently, then the magnetic curved surface microstructure soft mode is created; moreover, the magnetic performance of the magnetic curved surface at different heights is tested and recorded, and through experimentation and simulation, the magnetic curved surface microstructure soft mold is used in the research of quasi-grey scale soft mold curved surface microstructure imprinting and replica molding. The experimental results show that, under different surface curvatures and voltage control conditions, different quasi-grey scale array microstructures take shape. In addition, this paper conducts research on the imprinting and replica molding of photoresist composite magnetic powder in order to discuss the forming performance of magnetic photoresist, and finally, the experimental result is compared with the simulation to obtain more accurate prediction and results. This research is predicted to provide microstructure component preparation technology with heterogeneity and controllability, and is a kind of valid shaping quasi-grey scale microstructure manufacturing technology method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=soft%20mold" title="soft mold">soft mold</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic" title=" magnetic"> magnetic</a>, <a href="https://publications.waset.org/abstracts/search?q=microstructure" title=" microstructure"> microstructure</a>, <a href="https://publications.waset.org/abstracts/search?q=curved%20surface" title=" curved surface"> curved surface</a> </p> <a href="https://publications.waset.org/abstracts/63938/development-of-imprinting-and-replica-molding-of-soft-mold-curved-surface" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63938.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">17960</span> A Prediction Model of Adopting IPTV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeonghwan%20Jeon">Jeonghwan Jeon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the advent of IPTV in the fierce competition with existing broadcasting system, it is emerged as an important issue to predict how much the adoption of IPTV service will be. This paper aims to suggest a prediction model for adopting IPTV using classification and Ranking Belief Simplex (CaRBS). A simplex plot method of representing data allows a clear visual representation to the degree of interaction of the support from the variables to the prediction of the objects. CaRBS is applied to the survey data on the IPTV adoption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=adoption" title=" adoption"> adoption</a>, <a href="https://publications.waset.org/abstracts/search?q=IPTV" title=" IPTV"> IPTV</a>, <a href="https://publications.waset.org/abstracts/search?q=CaRBS" title=" CaRBS"> CaRBS</a> </p> <a href="https://publications.waset.org/abstracts/2971/a-prediction-model-of-adopting-iptv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2971.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17959</span> Prediction on Housing Price Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Yu">Li Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chenlu%20Jiao"> Chenlu Jiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongrun%20Xin"> Hongrun Xin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Wang"> Yan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaiyang%20Wang"> Kaiyang Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry. <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=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=housing%20prediction" title=" housing prediction"> housing prediction</a> </p> <a href="https://publications.waset.org/abstracts/84747/prediction-on-housing-price-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84747.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">307</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">17958</span> The Network Relative Model Accuracy (NeRMA) Score: A Method to Quantify the Accuracy of Prediction Models in a Concurrent External Validation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carl%20van%20Walraven">Carl van Walraven</a>, <a href="https://publications.waset.org/abstracts/search?q=Meltem%20Tuna"> Meltem Tuna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction%20model%20accuracy" title="prediction model accuracy">prediction model accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=scaled%20brier%20score" title=" scaled brier score"> scaled brier score</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20effects%20methods" title=" fixed effects methods"> fixed effects methods</a>, <a href="https://publications.waset.org/abstracts/search?q=concurrent%20external%20validation" title=" concurrent external validation"> concurrent external validation</a> </p> <a href="https://publications.waset.org/abstracts/142792/the-network-relative-model-accuracy-nerma-score-a-method-to-quantify-the-accuracy-of-prediction-models-in-a-concurrent-external-validation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142792.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">236</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">17957</span> Study on the Model Predicting Post-Construction Settlement of Soft Ground</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pingshan%20Chen">Pingshan Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiliang%20Dong"> Zhiliang Dong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to estimate the post-construction settlement more objectively, the power-polynomial model is proposed, which can reflect the trend of settlement development based on the observed settlement data. It was demonstrated by an actual case history of an embankment, and during the prediction. Compared with the other three prediction models, the power-polynomial model can estimate the post-construction settlement more accurately with more simple calculation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=post-construction%20settlement" title=" post-construction settlement"> post-construction settlement</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20ground" title=" soft ground"> soft ground</a> </p> <a href="https://publications.waset.org/abstracts/2187/study-on-the-model-predicting-post-construction-settlement-of-soft-ground" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2187.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">17956</span> Enhanced Extra Trees Classifier for Epileptic Seizure Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maurice%20Ntahobari">Maurice Ntahobari</a>, <a href="https://publications.waset.org/abstracts/search?q=Levin%20Kuhlmann"> Levin Kuhlmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20Boley"> Mario Boley</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhinoos%20Razavi%20Hesabi"> Zhinoos Razavi Hesabi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection. <p class="card-text"><strong>Keywords:</strong> <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=seizure%20prediction" title=" seizure prediction"> seizure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=extra%20tree%20classifier" title=" extra tree classifier"> extra tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=SHAP" title=" SHAP"> SHAP</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a> </p> <a href="https://publications.waset.org/abstracts/155126/enhanced-extra-trees-classifier-for-epileptic-seizure-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155126.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">113</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">17955</span> Approved Cyclic Treatment System of Grey Water</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hanen%20Filali">Hanen Filali</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Hachicha"> Mohamed Hachicha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Treated grey water (TGW) reuse emerged as an alternative resource to meet the growing demand for water for agricultural irrigation and reduce the pressure on limited existing fresh water. However, this reuse needs adapted management in order to avoid environmental and health risks. In this work, the treatment of grey water (GW) was studied from a cyclic treatment system that we designed and implemented in the greenhouse of National Research Institute for Rural Engineering, Water and Forests (INRGREF). This system is composed of three levels for treatment (TGW 1, TGW 2, and TGW 3). Each level includes a sandy soil box. The use of grey water was moderated depending on the chemical and microbiological quality obtained. Different samples of soils and treated grey water were performed and analyzed for 14 irrigation cycles. TGW through cyclic treatment showed physicochemical parameters like pH, electrical conductivity (EC), chemical oxygen demand (COD), biological oxygen demand (BOD5) in the range of 7,35-7,91, 1,69-5,03 dS/m, 102,6-54,2 mgO2/l, and 31,33-15,74 mgO2/l, respectively. Results showed a reduction in the pollutant load with a significant effect on the three treatment levels; however, an increase in salinity was observed during all irrigation cycles. Microbiological results showed good grey water treatment with low health risk on irrigated soil. Treated water quality was below permissible Tunisian standards (NT106.03), and treated water is suitable for non-potable options. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=treated%20grey%20water" title="treated grey water">treated grey water</a>, <a href="https://publications.waset.org/abstracts/search?q=irrigation" title=" irrigation"> irrigation</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20treatment" title=" cyclic treatment"> cyclic treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=soils" title=" soils"> soils</a>, <a href="https://publications.waset.org/abstracts/search?q=physico-chemical%20parameters" title=" physico-chemical parameters"> physico-chemical parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=microbiological%20parameters" title=" microbiological parameters"> microbiological parameters</a> </p> <a href="https://publications.waset.org/abstracts/155731/approved-cyclic-treatment-system-of-grey-water" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155731.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17954</span> Stock Market Prediction by Regression Model with Social Moods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masahiro%20Ohmura">Masahiro Ohmura</a>, <a href="https://publications.waset.org/abstracts/search?q=Koh%20Kakusho"> Koh Kakusho</a>, <a href="https://publications.waset.org/abstracts/search?q=Takeshi%20Okadome"> Takeshi Okadome</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20prediction" title="stock market prediction">stock market prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20moods" title=" social moods"> social moods</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20model" title=" regression model"> regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=DJIA" title=" DJIA"> DJIA</a> </p> <a href="https://publications.waset.org/abstracts/8713/stock-market-prediction-by-regression-model-with-social-moods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8713.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">549</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">17953</span> Calibration Model of %Titratable Acidity (Citric Acid) for Intact Tomato by Transmittance SW-NIR Spectroscopy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Petcharaporn">K. Petcharaporn</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kumchoo"> S. Kumchoo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tomato" title="tomato">tomato</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=transmittance" title=" transmittance"> transmittance</a>, <a href="https://publications.waset.org/abstracts/search?q=titratable%20acidity" title=" titratable acidity"> titratable acidity</a>, <a href="https://publications.waset.org/abstracts/search?q=citric%20acid" title=" citric acid"> citric acid</a> </p> <a href="https://publications.waset.org/abstracts/11536/calibration-model-of-titratable-acidity-citric-acid-for-intact-tomato-by-transmittance-sw-nir-spectroscopy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11536.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">273</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">17952</span> Greywater Water Reuse in South Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Onyeka%20Nkwonta">Onyeka Nkwonta</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Iheukwumere"> Christopher Iheukwumere</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is a waste to irrigate with great quantities of drinking water when plants thrive on used water containing small bits of compost. Unlike a lot of ecological stopgap measures, greywater reuse is a part of the fundamental solution to many ecological problems and will probably remain essentially unchanged in the distant future. Water is abused and wasted by both the wealthy and the poor. Education about water conservation is also needed. This study gives an outline of the sources of grey water in our home and provides a process of creating awareness on the importance of re-using grey water in our home, in order to achieve the 7th aim of the millennium development goals by 2015, which is ensuring environmental sustainability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tickling%20filter" title="tickling filter">tickling filter</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20water" title=" grey water"> grey water</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20sustainability" title=" environmental sustainability"> environmental sustainability</a> </p> <a href="https://publications.waset.org/abstracts/62636/greywater-water-reuse-in-south-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62636.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17951</span> A New Prediction Model for Soil Compression Index </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Mohammadzadeh%20S.">D. Mohammadzadeh S.</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Bolouri%20Bazaz"> J. Bolouri Bazaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new prediction model for compression index of fine-grained soils using multi-gene genetic programming (MGGP) technique. The proposed model relates the soil compression index to its liquid limit, plastic limit and void ratio. Several laboratory test results for fine-grained were used to develop the models. Various criteria were considered to check the validity of the model. The parametric and sensitivity analyses were performed and discussed. The MGGP method was found to be very effective for predicting the soil compression index. A comparative study was further performed to prove the superiority of the MGGP model to the existing soft computing and traditional empirical equations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=new%20prediction%20model" title="new prediction model">new prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=compression%20index%20soil" title=" compression index soil"> compression index soil</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-gene%20genetic%20programming" title=" multi-gene genetic programming"> multi-gene genetic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=MGGP" title=" MGGP"> MGGP</a> </p> <a href="https://publications.waset.org/abstracts/35247/a-new-prediction-model-for-soil-compression-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35247.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">375</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">17950</span> On Improving Breast Cancer Prediction Using GRNN-CP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kefaya%20Qaddoum">Kefaya Qaddoum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice. <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=conformal%20prediction" title=" conformal prediction"> conformal prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20classification" title=" cancer classification"> cancer classification</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/74483/on-improving-breast-cancer-prediction-using-grnn-cp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74483.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">291</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">17949</span> Optimization of Electrical Discharge Machining Parameters in Machining AISI D3 Tool Steel by Grey Relational Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Othman%20Mohamed%20Altheni">Othman Mohamed Altheni</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdurrahman%20Abusaada"> Abdurrahman Abusaada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents optimization of multiple performance characteristics [material removal rate (MRR), surface roughness (Ra), and overcut (OC)] of hardened AISI D3 tool steel in electrical discharge machining (EDM) using Taguchi method and Grey relational analysis. Machining process parameters selected were pulsed current Ip, pulse-on time Ton, pulse-off time Toff and gap voltage Vg. Based on ANOVA, pulse current is found to be the most significant factor affecting EDM process. Optimized process parameters are simultaneously leading to a higher MRR, lower Ra, and lower OC are then verified through a confirmation experiment. Validation experiment shows an improved MRR, Ra and OC when Taguchi method and grey relational analysis were used <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edm%20parameters" title="edm parameters">edm parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20relational%20analysis" title=" grey relational analysis"> grey relational analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a>, <a href="https://publications.waset.org/abstracts/search?q=ANOVA" title=" ANOVA"> ANOVA</a> </p> <a href="https://publications.waset.org/abstracts/52166/optimization-of-electrical-discharge-machining-parameters-in-machining-aisi-d3-tool-steel-by-grey-relational-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52166.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">294</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">17948</span> Urban Growth Prediction Using Artificial Neural Networks in Athens, Greece </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20Triantakonstantis">Dimitrios Triantakonstantis</a>, <a href="https://publications.waset.org/abstracts/search?q=Demetris%20Stathakis"> Demetris Stathakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change. <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=CORINE" title=" CORINE"> CORINE</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20atlas" title=" urban atlas"> urban atlas</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20growth%20prediction" title=" urban growth prediction"> urban growth prediction</a> </p> <a href="https://publications.waset.org/abstracts/24994/urban-growth-prediction-using-artificial-neural-networks-in-athens-greece" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24994.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">529</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">17947</span> Remaining Useful Life (RUL) Assessment Using Progressive Bearing Degradation Data and ANN Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20R.%20Bhende">Amit R. Bhende</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20K.%20Awari"> G. K. Awari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Bearing degradation data at three different conditions from run to failure is used. A RUL prediction model is separately built in each condition. Feed forward back propagation neural network models are developed for prediction modeling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bearing%20degradation%20data" title="bearing degradation data">bearing degradation data</a>, <a href="https://publications.waset.org/abstracts/search?q=remaining%20useful%20life%20%28RUL%29" title=" remaining useful life (RUL)"> remaining useful life (RUL)</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation" title=" back propagation"> back propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=prognosis" title=" prognosis"> prognosis</a> </p> <a href="https://publications.waset.org/abstracts/45708/remaining-useful-life-rul-assessment-using-progressive-bearing-degradation-data-and-ann-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45708.pdf" target="_blank" class="btn btn-primary 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