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modeling</title> <meta name="description" content="Search results for: prediction modeling"> <meta name="keywords" content="prediction modeling"> <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="prediction modeling"> <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> 5825</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: prediction modeling</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5825</span> Modeling and Shape Prediction for Elastic Kinematic Chains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiun%20Jeon">Jiun Jeon</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung-Ju%20Yi"> Byung-Ju Yi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates modeling and shape prediction of elastic kinematic chains such as colonoscopy. 2D and 3D models of elastic kinematic chains are suggested and their behaviors are demonstrated through simulation. To corroborate the effectiveness of those models, experimental work is performed using a magnetic sensor system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=elastic%20kinematic%20chain" title="elastic kinematic chain">elastic kinematic chain</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20prediction" title=" shape prediction"> shape prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=colonoscopy" title=" colonoscopy"> colonoscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a> </p> <a href="https://publications.waset.org/abstracts/4177/modeling-and-shape-prediction-for-elastic-kinematic-chains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4177.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">605</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">5824</span> ARIMA-GARCH, A Statistical Modeling 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=Salman%20Mohamadi">Salman Mohamadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mohammad%20Ali%20Tayaranian%20Hosseini"> Seyed Mohammad Ali Tayaranian Hosseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Amindavar"> Hamidreza Amindavar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure%20prediction" title="epileptic seizure prediction ">epileptic seizure prediction </a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH%20and%20GARCH%20modeling" title=" ARCH and GARCH modeling"> ARCH and GARCH modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=heteroskedasticity" title=" heteroskedasticity"> heteroskedasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a> </p> <a href="https://publications.waset.org/abstracts/59028/arima-garch-a-statistical-modeling-for-epileptic-seizure-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59028.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">406</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">5823</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">528</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">5822</span> Easymodel: Web-based Bioinformatics Software for Protein Modeling Based on Modeller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Dantism">Alireza Dantism</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Presently, describing the function of a protein sequence is one of the most common problems in biology. Usually, this problem can be facilitated by studying the three-dimensional structure of proteins. In the absence of a protein structure, comparative modeling often provides a useful three-dimensional model of the protein that is dependent on at least one known protein structure. Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) mainly based on its alignment with one or more proteins of known structure (templates). Comparative modeling consists of four main steps 1. Similarity between the target sequence and at least one known template structure 2. Alignment of target sequence and template(s) 3. Build a model based on alignment with the selected template(s). 4. Prediction of model errors 5. Optimization of the built model There are many computer programs and web servers that automate the comparative modeling process. One of the most important advantages of these servers is that it makes comparative modeling available to both experts and non-experts, and they can easily do their own modeling without the need for programming knowledge, but some other experts prefer using programming knowledge and do their modeling manually because by doing this they can maximize the accuracy of their modeling. In this study, a web-based tool has been designed to predict the tertiary structure of proteins using PHP and Python programming languages. This tool is called EasyModel. EasyModel can receive, according to the user's inputs, the desired unknown sequence (which we know as the target) in this study, the protein sequence file (template), etc., which also has a percentage of similarity with the primary sequence, and its third structure Predict the unknown sequence and present the results in the form of graphs and constructed protein files. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=structural%20bioinformatics" title="structural bioinformatics">structural bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20tertiary%20structure%20prediction" title=" protein tertiary structure prediction"> protein tertiary structure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20modeling" title=" comparative modeling"> comparative modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=modeller" title=" modeller"> modeller</a> </p> <a href="https://publications.waset.org/abstracts/156892/easymodel-web-based-bioinformatics-software-for-protein-modeling-based-on-modeller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156892.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">97</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">5821</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 btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">436</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">5820</span> Process Modeling of Electric Discharge Machining of Inconel 825 Using Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Himanshu%20Payal">Himanshu Payal</a>, <a href="https://publications.waset.org/abstracts/search?q=Sachin%20Maheshwari"> Sachin Maheshwari</a>, <a href="https://publications.waset.org/abstracts/search?q=Pushpendra%20S.%20Bharti"> Pushpendra S. Bharti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=EDM" title=" EDM"> EDM</a>, <a href="https://publications.waset.org/abstracts/search?q=metal%20removal%20rate" title=" metal removal rate"> metal removal rate</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a> </p> <a href="https://publications.waset.org/abstracts/69399/process-modeling-of-electric-discharge-machining-of-inconel-825-using-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69399.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">5819</span> Using High Performance Computing for Online Flood Monitoring and Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stepan%20Kuchar">Stepan Kuchar</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Golasowski"> Martin Golasowski</a>, <a href="https://publications.waset.org/abstracts/search?q=Radim%20Vavrik"> Radim Vavrik</a>, <a href="https://publications.waset.org/abstracts/search?q=Michal%20Podhoranyi"> Michal Podhoranyi</a>, <a href="https://publications.waset.org/abstracts/search?q=Boris%20Sir"> Boris Sir</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan%20Martinovic"> Jan Martinovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood%20prediction%20process" title="flood prediction process">flood prediction process</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20performance%20computing" title=" high performance computing"> high performance computing</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20flood%20prediction%20system" title=" online flood prediction system"> online flood prediction system</a>, <a href="https://publications.waset.org/abstracts/search?q=parallelization" title=" parallelization"> parallelization</a> </p> <a href="https://publications.waset.org/abstracts/21155/using-high-performance-computing-for-online-flood-monitoring-and-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21155.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">492</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5818</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">73</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">5817</span> Unsupervised Text Mining Approach to Early Warning System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ichihan%20Tai">Ichihan Tai</a>, <a href="https://publications.waset.org/abstracts/search?q=Bill%20Olson"> Bill Olson</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Blessner"> Paul Blessner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=early%20warning%20system" title="early warning system">early warning system</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20prediction" title=" market prediction"> market prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling." title=" topic modeling."> topic modeling.</a> </p> <a href="https://publications.waset.org/abstracts/46013/unsupervised-text-mining-approach-to-early-warning-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46013.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">338</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">5816</span> A Deep Learning-Based Pedestrian Trajectory Prediction Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haozhe%20Xiang">Haozhe Xiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results. <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=graph%20convolutional%20network" title=" graph convolutional network"> graph convolutional network</a>, <a href="https://publications.waset.org/abstracts/search?q=attention%20mechanism" title=" attention mechanism"> attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a> </p> <a href="https://publications.waset.org/abstracts/182188/a-deep-learning-based-pedestrian-trajectory-prediction-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182188.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">70</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">5815</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">394</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">5814</span> Statistical Classification, Downscaling and Uncertainty Assessment for Global Climate Model Outputs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Queen%20Suraajini%20Rajendran">Queen Suraajini Rajendran</a>, <a href="https://publications.waset.org/abstracts/search?q=Sai%20Hung%20Cheung"> Sai Hung Cheung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20downscaling" title="statistical downscaling">statistical downscaling</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20climate%20model" title=" global climate model"> global climate model</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title=" climate change"> climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/18056/statistical-classification-downscaling-and-uncertainty-assessment-for-global-climate-model-outputs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18056.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">368</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">5813</span> Mathematical Modeling for Diabetes Prediction: A Neuro-Fuzzy Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vijay%20Kr.%20Yadav">Vijay Kr. Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Nilam%20Rathi"> Nilam Rathi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=back-propagation" title="back-propagation">back-propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes%20mellitus" title=" diabetes mellitus"> diabetes mellitus</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=neuro-fuzzy" title=" neuro-fuzzy"> neuro-fuzzy</a> </p> <a href="https://publications.waset.org/abstracts/79054/mathematical-modeling-for-diabetes-prediction-a-neuro-fuzzy-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79054.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">257</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">5812</span> Monthly River Flow Prediction Using a Nonlinear Prediction Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20H.%20Adenan">N. H. Adenan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20M.%20Noorani"> M. S. M. Noorani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=river%20flow" title="river flow">river flow</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20prediction%20method" title=" nonlinear prediction method"> nonlinear prediction method</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=local%20linear%20approximation" title=" local linear approximation"> local linear approximation</a> </p> <a href="https://publications.waset.org/abstracts/2867/monthly-river-flow-prediction-using-a-nonlinear-prediction-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2867.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">5811</span> Air Dispersion Modeling for Prediction of Accidental Emission in the Atmosphere along Northern Coast of Egypt </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moustafa%20Osman">Moustafa Osman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modeling of air pollutants from the accidental release is performed for quantifying the impact of industrial facilities into the ambient air. The mathematical methods are requiring for the prediction of the accidental scenario in probability of failure-safe mode and analysis consequences to quantify the environmental damage upon human health. The initial statement of mitigation plan is supporting implementation during production and maintenance periods. In a number of mathematical methods, the flow rate at which gaseous and liquid pollutants might be accidentally released is determined from various types in term of point, line and area sources. These emissions are integrated meteorological conditions in simplified stability parameters to compare dispersion coefficients from non-continuous air pollution plumes. The differences are reflected in concentrations levels and greenhouse effect to transport the parcel load in both urban and rural areas. This research reveals that the elevation effect nearby buildings with other structure is higher 5 times more than open terrains. These results are agreed with Sutton suggestion for dispersion coefficients in different stability classes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20pollutants" title="air pollutants">air pollutants</a>, <a href="https://publications.waset.org/abstracts/search?q=dispersion%20modeling" title=" dispersion modeling"> dispersion modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20effect" title=" health effect"> health effect</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20planning" title=" urban planning"> urban planning</a> </p> <a href="https://publications.waset.org/abstracts/5517/air-dispersion-modeling-for-prediction-of-accidental-emission-in-the-atmosphere-along-northern-coast-of-egypt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5517.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">374</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">5810</span> Hybrid Wavelet-Adaptive Neuro-Fuzzy Inference System Model for a Greenhouse Energy Demand Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azzedine%20Hamza">Azzedine Hamza</a>, <a href="https://publications.waset.org/abstracts/search?q=Chouaib%20Chakour"> Chouaib Chakour</a>, <a href="https://publications.waset.org/abstracts/search?q=Messaoud%20Ramdani"> Messaoud Ramdani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title="wavelet transform">wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption%20prediction" title=" energy consumption prediction"> energy consumption prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=greenhouse" title=" greenhouse"> greenhouse</a> </p> <a href="https://publications.waset.org/abstracts/163632/hybrid-wavelet-adaptive-neuro-fuzzy-inference-system-model-for-a-greenhouse-energy-demand-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163632.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5809</span> Predicting Stack Overflow Accepted Answers Using Features and Models with Varying Degrees of Complexity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Osayande%20Pascal%20Omondiagbe">Osayande Pascal Omondiagbe</a>, <a href="https://publications.waset.org/abstracts/search?q=Sherlock%20a%20Licorish"> Sherlock a Licorish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stack Overflow is a popular community question and answer portal which is used by practitioners to solve technology-related challenges during software development. Previous studies have shown that this forum is becoming a substitute for official software programming languages documentation. While tools have looked to aid developers by presenting interfaces to explore Stack Overflow, developers often face challenges searching through many possible answers to their questions, and this extends the development time. To this end, researchers have provided ways of predicting acceptable Stack Overflow answers by using various modeling techniques. However, less interest is dedicated to examining the performance and quality of typically used modeling methods, and especially in relation to models’ and features’ complexity. Such insights could be of practical significance to the many practitioners that use Stack Overflow. This study examines the performance and quality of various modeling methods that are used for predicting acceptable answers on Stack Overflow, drawn from 2014, 2015 and 2016. Our findings reveal significant differences in models’ performance and quality given the type of features and complexity of models used. Researchers examining classifiers’ performance and quality and features’ complexity may leverage these findings in selecting suitable techniques when developing prediction models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling%20and%20prediction" title=" modeling and prediction"> modeling and 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=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=stack%20overflow" title=" stack overflow"> stack overflow</a> </p> <a href="https://publications.waset.org/abstracts/143309/predicting-stack-overflow-accepted-answers-using-features-and-models-with-varying-degrees-of-complexity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143309.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">132</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">5808</span> Mathematical Modeling of the Fouling Phenomenon in Ultrafiltration of Latex Effluent</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amira%20Abdelrasoul">Amira Abdelrasoul</a>, <a href="https://publications.waset.org/abstracts/search?q=Huu%20Doan"> Huu Doan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Lohi"> Ali Lohi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An efficient and well-planned ultrafiltration process is becoming a necessity for monetary returns in the industrial settings. The aim of the present study was to develop a mathematical model for an accurate prediction of ultrafiltration membrane fouling of latex effluent applied to homogeneous and heterogeneous membranes with uniform and non-uniform pore sizes, respectively. The models were also developed for an accurate prediction of power consumption that can handle the large-scale purposes. The model incorporated the fouling attachments as well as chemical and physical factors in membrane fouling for accurate prediction and scale-up application. Both Polycarbonate and Polysulfone flat membranes, with pore sizes of 0.05 µm and a molecular weight cut-off of 60,000, respectively, were used under a constant feed flow rate and a cross-flow mode in ultrafiltration of the simulated paint effluent. Furthermore, hydrophilic ultrafilic and hydrophobic PVDF membranes with MWCO of 100,000 were used to test the reliability of the models. Monodisperse particles of 50 nm and 100 nm in diameter, and a latex effluent with a wide range of particle size distributions were utilized to validate the models. The aggregation and the sphericity of the particles indicated a significant effect on membrane fouling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=membrane%20fouling" title="membrane fouling">membrane fouling</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20modeling" title=" mathematical modeling"> mathematical modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20consumption" title=" power consumption"> power consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=attachments" title=" attachments"> attachments</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrafiltration" title=" ultrafiltration"> ultrafiltration</a> </p> <a href="https://publications.waset.org/abstracts/16539/mathematical-modeling-of-the-fouling-phenomenon-in-ultrafiltration-of-latex-effluent" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16539.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">470</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5807</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">5806</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">46</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">5805</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">5804</span> Fast Authentication Using User Path Prediction in Wireless Broadband Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gunasekaran%20Raja">Gunasekaran Raja</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajakumar%20Arul"> Rajakumar Arul</a>, <a href="https://publications.waset.org/abstracts/search?q=Kottilingam%20Kottursamy"> Kottilingam Kottursamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramkumar%20Jayaraman"> Ramkumar Jayaraman</a>, <a href="https://publications.waset.org/abstracts/search?q=Sathya%20Pavithra"> Sathya Pavithra</a>, <a href="https://publications.waset.org/abstracts/search?q=Swaminathan%20Venkatraman"> Swaminathan Venkatraman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless Interoperability for Microwave Access (WiMAX) utilizes the IEEE 802.1X mechanism for authentication. However, this mechanism incurs considerable delay during handoffs. This delay during handoffs results in service disruption which becomes a severe bottleneck. To overcome this delay, our article proposes a key caching mechanism based on user path prediction. If the user mobility follows that path, the user bypasses the normal IEEE 802.1X mechanism and establishes the necessary authentication keys directly. Through analytical and simulation modeling, we have proved that our mechanism effectively decreases the handoff delay thereby achieving fast authentication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authentication" title="authentication">authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=authorization" title=" authorization"> authorization</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20accounting%20%28AAA%29" title=" and accounting (AAA)"> and accounting (AAA)</a>, <a href="https://publications.waset.org/abstracts/search?q=handoff" title=" handoff"> handoff</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile" title=" mobile"> mobile</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20path%20prediction%20%28UPP%29%20and%20user%20pattern" title=" user path prediction (UPP) and user pattern"> user path prediction (UPP) and user pattern</a> </p> <a href="https://publications.waset.org/abstracts/48859/fast-authentication-using-user-path-prediction-in-wireless-broadband-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48859.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">405</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5803</span> Research and Application of the Three-Dimensional Visualization Geological Modeling of Mine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Wang">Bin Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong%20Xu"> Yong Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Honggang%20Qu"> Honggang Qu</a>, <a href="https://publications.waset.org/abstracts/search?q=Rongmei%20Liu"> Rongmei Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhenji%20Gao"> Zhenji Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today's mining industry is advancing gradually toward digital and visual direction. The three dimensional visualization geological modeling of mine is the digital characterization of mineral deposit, and is one of the key technology of digital mine. The three-dimensional geological modeling is a technology that combines the geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in three-dimensional environment with computer technology, and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provided scientific bases for mine resource assessment, reserve calculation, mining design and so on. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=three-dimensional%20geological%20modeling" title="three-dimensional geological modeling">three-dimensional geological modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=geological%20database" title=" geological database"> geological database</a>, <a href="https://publications.waset.org/abstracts/search?q=geostatistics" title=" geostatistics"> geostatistics</a>, <a href="https://publications.waset.org/abstracts/search?q=block%20model" title=" block model"> block model</a> </p> <a href="https://publications.waset.org/abstracts/167346/research-and-application-of-the-three-dimensional-visualization-geological-modeling-of-mine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167346.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">70</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">5802</span> Quantitative Structure-Property Relationship Study of Base Dissociation Constants of Some Benzimidazoles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanja%20O.%20Podunavac-Kuzmanovi%C4%87">Sanja O. Podunavac-Kuzmanović</a>, <a href="https://publications.waset.org/abstracts/search?q=Lidija%20R.%20Jevri%C4%87"> Lidija R. Jevrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Strahinja%20Z.%20Kova%C4%8Devi%C4%87"> Strahinja Z. Kovačević</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Benzimidazoles are a group of compounds with significant antibacterial, antifungal and anticancer activity. The studied compounds consist of the main benzimidazole structure with different combinations of substituens. This study is based on the two-dimensional and three-dimensional molecular modeling and calculation of molecular descriptors (physicochemical and lipophilicity descriptors) of structurally diverse benzimidazoles. Molecular modeling was carried out by using ChemBio3D Ultra version 14.0 software. The obtained 3D models were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. The obtained set of molecular descriptors was used in principal component analysis (PCA) of possible similarities and dissimilarities among the studied derivatives. After the molecular modeling, the quantitative structure-property relationship (QSPR) analysis was applied in order to get the mathematical models which can be used in prediction of pKb values of structurally similar benzimidazoles. The obtained models are based on statistically valid multiple linear regression (MLR) equations. The calculated cross-validation parameters indicate the high prediction ability of the established QSPR models. This study is financially supported by COST action CM1306 and the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=benzimidazoles" title="benzimidazoles">benzimidazoles</a>, <a href="https://publications.waset.org/abstracts/search?q=chemometrics" title=" chemometrics"> chemometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20modeling" title=" molecular modeling"> molecular modeling</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=QSPR" title=" QSPR"> QSPR</a> </p> <a href="https://publications.waset.org/abstracts/45055/quantitative-structure-property-relationship-study-of-base-dissociation-constants-of-some-benzimidazoles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45055.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">287</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">5801</span> Protein Tertiary Structure Prediction by a Multiobjective Optimization and Neural Network Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexandre%20Barbosa%20de%20Almeida">Alexandre Barbosa de Almeida</a>, <a href="https://publications.waset.org/abstracts/search?q=Telma%20Woerle%20de%20Lima%20Soares"> Telma Woerle de Lima Soares</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ab%20initio%20heuristic%20modeling" title="Ab initio heuristic modeling">Ab initio heuristic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction" title=" protein structure prediction"> protein structure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20network" title=" recurrent neural network"> recurrent neural network</a> </p> <a href="https://publications.waset.org/abstracts/141565/protein-tertiary-structure-prediction-by-a-multiobjective-optimization-and-neural-network-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141565.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">205</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">5800</span> Using Simulation Modeling Approach to Predict USMLE Steps 1 and 2 Performances</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chau-Kuang%20Chen">Chau-Kuang Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Hughes"> John Hughes</a>, <a href="https://publications.waset.org/abstracts/search?q=Jr."> Jr.</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Dexter%20Samuels"> A. Dexter Samuels</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prediction models for the United States Medical Licensure Examination (USMLE) Steps 1 and 2 performances were constructed by the Monte Carlo simulation modeling approach via linear regression. The purpose of this study was to build robust simulation models to accurately identify the most important predictors and yield the valid range estimations of the Steps 1 and 2 scores. The application of simulation modeling approach was deemed an effective way in predicting student performances on licensure examinations. Also, sensitivity analysis (a/k/a what-if analysis) in the simulation models was used to predict the magnitudes of Steps 1 and 2 affected by changes in the National Board of Medical Examiners (NBME) Basic Science Subject Board scores. In addition, the study results indicated that the Medical College Admission Test (MCAT) Verbal Reasoning score and Step 1 score were significant predictors of the Step 2 performance. Hence, institutions could screen qualified student applicants for interviews and document the effectiveness of basic science education program based on the simulation results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction%20model" title="prediction model">prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation%20method" title=" simulation method"> simulation method</a>, <a href="https://publications.waset.org/abstracts/search?q=USMLE" title=" USMLE"> USMLE</a> </p> <a href="https://publications.waset.org/abstracts/54294/using-simulation-modeling-approach-to-predict-usmle-steps-1-and-2-performances" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54294.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">339</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5799</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">5798</span> Research of the Three-Dimensional Visualization Geological Modeling of Mine Based on Surpac</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Honggang%20Qu">Honggang Qu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong%20Xu"> Yong Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Rongmei%20Liu"> Rongmei Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhenji%20Gao"> Zhenji Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Wang"> Bin Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today's mining industry is advancing gradually toward digital and visual direction. The three-dimensional visualization geological modeling of mine is the digital characterization of mineral deposits and is one of the key technology of digital mining. Three-dimensional geological modeling is a technology that combines geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in a three-dimensional environment with computer technology and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between the distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provides scientific bases for mine resource assessment, reserve calculation, mining design and so on. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=three-dimensional%20geological%20modeling" title="three-dimensional geological modeling">three-dimensional geological modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=geological%20database" title=" geological database"> geological database</a>, <a href="https://publications.waset.org/abstracts/search?q=geostatistics" title=" geostatistics"> geostatistics</a>, <a href="https://publications.waset.org/abstracts/search?q=block%20model" title=" block model"> block model</a> </p> <a href="https://publications.waset.org/abstracts/167349/research-of-the-three-dimensional-visualization-geological-modeling-of-mine-based-on-surpac" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167349.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">77</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">5797</span> Traffic Congestions Modeling and Predictions by Social Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bojan%20Najdenov">Bojan Najdenov</a>, <a href="https://publications.waset.org/abstracts/search?q=Danco%20Davcev"> Danco Davcev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reduction of traffic congestions and the effects of pollution and waste of resources that come with them has been a big challenge in the past decades. Having reliable systems to facilitate the process of modeling and prediction of traffic conditions would not only reduce the environmental pollution, but will also save people time and money. Social networks play big role of people’s lives nowadays providing them means of communicating and sharing thoughts and ideas, that way generating huge knowledge bases by crowdsourcing. In addition to that, crowdsourcing as a concept provides mechanisms for fast and relatively reliable data generation and also many services are being used on regular basis because they are mainly powered by the public as main content providers. In this paper we present the Social-NETS-Traffic-Control System (SNTCS) that should serve as a facilitator in the process of modeling and prediction of traffic congestions. The main contribution of our system is to integrate data from social networks as Twitter and also implements a custom created crowdsourcing subsystem with which users report traffic conditions using an android application. Our first experience of the usage of the system confirms that the integrated approach allows easy extension of the system with other social networks and represents a very useful tool for traffic control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=traffic" title="traffic">traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=congestion%20reduction" title=" congestion reduction"> congestion reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=crowdsource" title=" crowdsource"> crowdsource</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networks" title=" social networks"> social networks</a>, <a href="https://publications.waset.org/abstracts/search?q=twitter" title=" twitter"> twitter</a>, <a href="https://publications.waset.org/abstracts/search?q=android" title=" android"> android</a> </p> <a href="https://publications.waset.org/abstracts/25742/traffic-congestions-modeling-and-predictions-by-social-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25742.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">482</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">5796</span> Nonlinear Estimation Model for Rail Track Deterioration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Karimpour">M. Karimpour</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Hitihamillage"> L. Hitihamillage</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Elkhoury"> N. Elkhoury</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Moridpour"> S. Moridpour</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Hesami"> R. Hesami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=MGT" title=" MGT"> MGT</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20modeling" title=" prediction modeling"> prediction modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=rail%20track%20degradation" title=" rail track degradation"> rail track degradation</a> </p> <a href="https://publications.waset.org/abstracts/77359/nonlinear-estimation-model-for-rail-track-deterioration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77359.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">335</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=prediction%20modeling&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=prediction%20modeling&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=prediction%20modeling&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=prediction%20modeling&page=5">5</a></li> <li class="page-item"><a class="page-link" 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