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process regression</title> <meta name="description" content="Search results for: Gaussian process regression"> <meta name="keywords" content="Gaussian process regression"> <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 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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="Gaussian process regression"> <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> 18313</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Gaussian process regression</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18313</span> The Extended Skew Gaussian Process for Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20T.%20Alodat">M. T. Alodat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression(ESGPr) model. The ESGPR model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGPR model at a new input. Also we apply the ESGPR model to FOREX data and we find that it fits the Forex data better than the GPR model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extended%20skew%20normal%20distribution" title="extended skew normal distribution">extended skew normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20for%20regression" title=" Gaussian process for regression"> Gaussian process for regression</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20distribution" title=" predictive distribution"> predictive distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=ESGPr%20model" title=" ESGPr model"> ESGPr model</a> </p> <a href="https://publications.waset.org/abstracts/2233/the-extended-skew-gaussian-process-for-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2233.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">553</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">18312</span> Solving Single Machine Total Weighted Tardiness Problem Using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanatchapong%20Kongkaew">Wanatchapong Kongkaew</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title="Gaussian process regression">Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=iterated%20local%20search" title=" iterated local search"> iterated local search</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20machine%20total%20weighted%20tardiness" title=" single machine total weighted tardiness"> single machine total weighted tardiness</a> </p> <a href="https://publications.waset.org/abstracts/6433/solving-single-machine-total-weighted-tardiness-problem-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6433.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">18311</span> Prediction of the Thermodynamic Properties of Hydrocarbons Using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Alhazmi">N. Alhazmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowing the thermodynamics properties of hydrocarbons is vital when it comes to analyzing the related chemical reaction outcomes and understanding the reaction process, especially in terms of petrochemical industrial applications, combustions, and catalytic reactions. However, measuring the thermodynamics properties experimentally is time-consuming and costly. In this paper, Gaussian process regression (GPR) has been used to directly predict the main thermodynamic properties - standard enthalpy of formation, standard entropy, and heat capacity -for more than 360 cyclic and non-cyclic alkanes, alkenes, and alkynes. A simple workflow has been proposed that can be applied to directly predict the main properties of any hydrocarbon by knowing its descriptors and chemical structure and can be generalized to predict the main properties of any material. The model was evaluated by calculating the statistical error R², which was more than 0.9794 for all the predicted properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thermodynamic" title="thermodynamic">thermodynamic</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title=" Gaussian process regression"> Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrocarbons" title=" hydrocarbons"> hydrocarbons</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=enthalpy" title=" enthalpy"> enthalpy</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20capacity" title=" heat capacity"> heat capacity</a> </p> <a href="https://publications.waset.org/abstracts/145010/prediction-of-the-thermodynamic-properties-of-hydrocarbons-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145010.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">222</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">18310</span> Enhancing Predictive Accuracy in Pharmaceutical Sales through an Ensemble Kernel Gaussian Process Regression Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahin%20Mirshekari">Shahin Mirshekari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Moradi"> Mohammadreza Moradi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Jafari"> Hossein Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Jafari"> Mehdi Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ensaf"> Mohammad Ensaf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title="Gaussian process regression">Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20kernels" title=" ensemble kernels"> ensemble kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20optimization" title=" bayesian optimization"> bayesian optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pharmaceutical%20sales%20analysis" title=" pharmaceutical sales analysis"> pharmaceutical sales analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a> </p> <a href="https://publications.waset.org/abstracts/181581/enhancing-predictive-accuracy-in-pharmaceutical-sales-through-an-ensemble-kernel-gaussian-process-regression-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181581.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">71</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">18309</span> Enhancing Temporal Extrapolation of Wind Speed Using a Hybrid Technique: A Case Study in West Coast of Denmark</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Elshafei">B. Elshafei</a>, <a href="https://publications.waset.org/abstracts/search?q=X.%20Mao"> X. Mao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20fusion" title="data fusion">data fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title=" Gaussian process regression"> Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20denoise" title=" signal denoise"> signal denoise</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20extrapolation" title=" temporal extrapolation"> temporal extrapolation</a> </p> <a href="https://publications.waset.org/abstracts/127870/enhancing-temporal-extrapolation-of-wind-speed-using-a-hybrid-technique-a-case-study-in-west-coast-of-denmark" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127870.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">135</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">18308</span> A Comparative Analysis of Machine Learning Techniques for PM10 Forecasting in Vilnius</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Adel%20Shokry%20Fahim">Mina Adel Shokry Fahim</a>, <a href="https://publications.waset.org/abstracts/search?q=J%C5%ABrat%C4%97%20Su%C5%BEiedelyt%C4%97%20Visockien%C4%97"> Jūratė Sužiedelytė Visockienė</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20pollution" title="air pollution">air pollution</a>, <a href="https://publications.waset.org/abstracts/search?q=anthropogenic%20and%20natural%20sources" title=" anthropogenic and natural sources"> anthropogenic and natural sources</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=Gaussian%20process%20regression" title=" Gaussian process regression"> Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20ensemble" title=" tree ensemble"> tree ensemble</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20models" title=" forecasting models"> forecasting models</a>, <a href="https://publications.waset.org/abstracts/search?q=particulate%20matter" title=" particulate matter"> particulate matter</a> </p> <a href="https://publications.waset.org/abstracts/183947/a-comparative-analysis-of-machine-learning-techniques-for-pm10-forecasting-in-vilnius" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183947.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">53</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">18307</span> Using Gaussian Process in Wind Power Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hacene%20Benkhoula">Hacene Benkhoula</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Badreddine%20Benabdella"> Mohamed Badreddine Benabdella</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Bouzeboudja"> Hamid Bouzeboudja</a>, <a href="https://publications.waset.org/abstracts/search?q=Abderrahmane%20Asraoui"> Abderrahmane Asraoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20power" title="wind power">wind power</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussien%20process" title=" Gaussien process"> Gaussien process</a>, <a href="https://publications.waset.org/abstracts/search?q=modelling" title=" modelling"> modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/41876/using-gaussian-process-in-wind-power-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41876.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">417</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">18306</span> Image Compression Based on Regression SVM and Biorthogonal Wavelets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zikiou%20Nadia">Zikiou Nadia</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahdir%20Mourad"> Lahdir Mourad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ameur%20Soltane"> Ameur Soltane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an effective method for image compression based on SVM Regression (SVR), with three different kernels, and biorthogonal 2D Discrete Wavelet Transform. SVM regression could learn dependency from training data and compressed using fewer training points (support vectors) to represent the original data and eliminate the redundancy. Biorthogonal wavelet has been used to transform the image and the coefficients acquired are then trained with different kernels SVM (Gaussian, Polynomial, and Linear). Run-length and Arithmetic coders are used to encode the support vectors and its corresponding weights, obtained from the SVM regression. The peak signal noise ratio (PSNR) and their compression ratios of several test images, compressed with our algorithm, with different kernels are presented. Compared with other kernels, Gaussian kernel achieves better image quality. Experimental results show that the compression performance of our method gains much improvement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20discrete%20wavelet%20transform%20%28DWT-2D%29" title=" 2D discrete wavelet transform (DWT-2D)"> 2D discrete wavelet transform (DWT-2D)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression%20%28SVR%29" title=" support vector regression (SVR)"> support vector regression (SVR)</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20Kernels" title=" SVM Kernels"> SVM Kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=run-length" title=" run-length"> run-length</a>, <a href="https://publications.waset.org/abstracts/search?q=arithmetic%20coding" title=" arithmetic coding"> arithmetic coding</a> </p> <a href="https://publications.waset.org/abstracts/17954/image-compression-based-on-regression-svm-and-biorthogonal-wavelets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17954.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18305</span> Pattern Synthesis of Nonuniform Linear Arrays Including Mutual Coupling Effects Based on Gaussian Process Regression and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ming%20Su">Ming Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziqiang%20Mu"> Ziqiang Mu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a synthesis method for nonuniform linear antenna arrays that combine Gaussian process regression (GPR) and genetic algorithm (GA). In this method, the GPR model can be used to calculate the array radiation pattern in the presence of mutual coupling effects, and then the GA is used to optimize the excitations and locations of the elements so as to generate the desired radiation pattern. In this paper, taking a 9-element nonuniform linear array as an example and the desired radiation pattern corresponding to a Chebyshev distribution as the optimization objective, optimize the excitations and locations of the elements. Finally, the optimization results are verified by electromagnetic simulation software CST, which shows that the method is effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonuniform%20linear%20antenna%20arrays" title="nonuniform linear antenna arrays">nonuniform linear antenna arrays</a>, <a href="https://publications.waset.org/abstracts/search?q=GPR" title=" GPR"> GPR</a>, <a href="https://publications.waset.org/abstracts/search?q=GA" title=" GA"> GA</a>, <a href="https://publications.waset.org/abstracts/search?q=mutual%20coupling%20effects" title=" mutual coupling effects"> mutual coupling effects</a>, <a href="https://publications.waset.org/abstracts/search?q=active%20element%20pattern" title=" active element pattern"> active element pattern</a> </p> <a href="https://publications.waset.org/abstracts/151650/pattern-synthesis-of-nonuniform-linear-arrays-including-mutual-coupling-effects-based-on-gaussian-process-regression-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151650.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">109</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18304</span> Covariance of the Queue Process Fed by Isonormal Gaussian Input Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samaneh%20Rahimirshnani">Samaneh Rahimirshnani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Jafari"> Hossein Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider fluid queueing processes fed by an isonormal Gaussian process. We study the correlation structure of the queueing process and the rate of convergence of the running supremum in the queueing process. The Malliavin calculus techniques are applied to obtain relations that show the workload process inherits the dependence properties of the input process. As examples, we consider two isonormal Gaussian processes, the sub-fractional Brownian motion (SFBM) and the fractional Brownian motion (FBM). For these examples, we obtain upper bounds for the covariance function of the queueing process and its rate of convergence to zero. We also discover that the rate of convergence of the queueing process is related to the structure of the covariance function of the input process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=queue%20length%20process" title="queue length process">queue length process</a>, <a href="https://publications.waset.org/abstracts/search?q=Malliavin%20calculus" title=" Malliavin calculus"> Malliavin calculus</a>, <a href="https://publications.waset.org/abstracts/search?q=covariance%20function" title=" covariance function"> covariance function</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20Brownian%20motion" title=" fractional Brownian motion"> fractional Brownian motion</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-fractional%20Brownian%20motion" title=" sub-fractional Brownian motion"> sub-fractional Brownian motion</a> </p> <a href="https://publications.waset.org/abstracts/182769/covariance-of-the-queue-process-fed-by-isonormal-gaussian-input-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182769.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">63</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">18303</span> Multinomial Dirichlet Gaussian Process Model for Classification of Multidimensional Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanggoon%20Kim"> Sanggoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multinomial%20dirichlet%20classification%20model" title="multinomial dirichlet classification model">multinomial dirichlet classification model</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20priors" title=" Gaussian process priors"> Gaussian process priors</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20Bayesian%20approximation" title=" variational Bayesian approximation"> variational Bayesian approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=importance%20sampling" title=" importance sampling"> importance sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=approximate%20posterior%20distribution" title=" approximate posterior distribution"> approximate posterior distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=marginal%20likelihood%20evidence" title=" marginal likelihood evidence"> marginal likelihood evidence</a> </p> <a href="https://publications.waset.org/abstracts/33816/multinomial-dirichlet-gaussian-process-model-for-classification-of-multidimensional-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33816.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">444</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18302</span> A Learning-Based EM Mixture Regression Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Cheng%20Tian">Yi-Cheng Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Miin-Shen%20Yang"> Miin-Shen Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mixture likelihood approach to clustering is a popular clustering method where the expectation and maximization (EM) algorithm is the most used mixture likelihood method. In the literature, the EM algorithm had been used for mixture regression models. However, these EM mixture regression algorithms are sensitive to initial values with a priori number of clusters. In this paper, to resolve these drawbacks, we construct a learning-based schema for the EM mixture regression algorithm such that it is free of initializations and can automatically obtain an approximately optimal number of clusters. Some numerical examples and comparisons demonstrate the superiority and usefulness of the proposed learning-based EM mixture regression algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=EM%20algorithm" title=" EM algorithm"> EM algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20mixture%20model" title=" Gaussian mixture model"> Gaussian mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture%20regression%20model" title=" mixture regression model"> mixture regression model</a> </p> <a href="https://publications.waset.org/abstracts/25163/a-learning-based-em-mixture-regression-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25163.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">510</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">18301</span> Novel Inference Algorithm for Gaussian Process Classification Model with Multiclass and Its Application to Human Action Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a novel inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multi-class. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20rule" title="bayesian rule">bayesian rule</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20process%20classification%20model%20with%20multiclass" title=" gaussian process classification model with multiclass"> gaussian process classification model with multiclass</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20process%20prior" title=" gaussian process prior"> gaussian process prior</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20action%20classification" title=" human action classification"> human action classification</a>, <a href="https://publications.waset.org/abstracts/search?q=laplace%20approximation" title=" laplace approximation"> laplace approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20EM%20algorithm" title=" variational EM algorithm"> variational EM algorithm</a> </p> <a href="https://publications.waset.org/abstracts/34103/novel-inference-algorithm-for-gaussian-process-classification-model-with-multiclass-and-its-application-to-human-action-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34103.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">334</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">18300</span> Satellite LiDAR-Based Digital Terrain Model Correction using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keisuke%20Takahata">Keisuke Takahata</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroshi%20Suetsugu"> Hiroshi Suetsugu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20terrain%20model" title="digital terrain model">digital terrain model</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20LiDAR" title=" satellite LiDAR"> satellite LiDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20processes" title=" gaussian processes"> gaussian processes</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20quantification" title=" uncertainty quantification"> uncertainty quantification</a> </p> <a href="https://publications.waset.org/abstracts/148360/satellite-lidar-based-digital-terrain-model-correction-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148360.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">182</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">18299</span> Base Change for Fisher Metrics: Case of the q-Gaussian Inverse Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20I.%20Loaiza%20Ossa">Gabriel I. Loaiza Ossa</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20A.%20Cadavid%20Moreno"> Carlos A. Cadavid Moreno</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20C.%20%20Arango%20Parra"> Juan C. Arango Parra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is known that the Riemannian manifold determined by the family of inverse Gaussian distributions endowed with the Fisher metric has negative constant curvature κ= -1/2, as does the family of usual Gaussian distributions. In the present paper, firstly, we arrive at this result by following a different path, much simpler than the previous ones. We first put the family in exponential form, thus endowing the family with a new set of parameters, or coordinates, θ₁, θ₂; then we determine the matrix of the Fisher metric in terms of these parameters; and finally we compute this matrix in the original parameters. Secondly, we define the inverse q-Gaussian distribution family (q < 3) as the family obtained by replacing the usual exponential function with the Tsallis q-exponential function in the expression for the inverse Gaussian distribution and observe that it supports two possible geometries, the Fisher and the q-Fisher geometry. And finally, we apply our strategy to obtain results about the Fisher and q-Fisher geometry of the inverse q-Gaussian distribution family, similar to the ones obtained in the case of the inverse Gaussian distribution family. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=base%20of%20changes" title="base of changes">base of changes</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20geometry" title=" information geometry"> information geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20Gaussian%20distribution" title=" inverse Gaussian distribution"> inverse Gaussian distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20q-Gaussian%20distribution" title=" inverse q-Gaussian distribution"> inverse q-Gaussian distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20manifolds" title=" statistical manifolds"> statistical manifolds</a> </p> <a href="https://publications.waset.org/abstracts/138122/base-change-for-fisher-metrics-case-of-the-q-gaussian-inverse-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138122.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">244</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">18298</span> Fast Bayesian Inference of Multivariate Block-Nearest Neighbor Gaussian Process (NNGP) Models for Large Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Gonzales">Carlos Gonzales</a>, <a href="https://publications.waset.org/abstracts/search?q=Zaida%20Quiroz"> Zaida Quiroz</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcos%20Prates"> Marcos Prates</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Several spatial variables collected at the same location that share a common spatial distribution can be modeled simultaneously through a multivariate geostatistical model that takes into account the correlation between these variables and the spatial autocorrelation. The main goal of this model is to perform spatial prediction of these variables in the region of study. Here we focus on a geostatistical multivariate formulation that relies on sharing common spatial random effect terms. In particular, the first response variable can be modeled by a mean that incorporates a shared random spatial effect, while the other response variables depend on this shared spatial term, in addition to specific random spatial effects. Each spatial random effect is defined through a Gaussian process with a valid covariance function, but in order to improve the computational efficiency when the data are large, each Gaussian process is approximated to a Gaussian random Markov field (GRMF), specifically to the block nearest neighbor Gaussian process (Block-NNGP). This approach involves dividing the spatial domain into several dependent blocks under certain constraints, where the cross blocks allow capturing the spatial dependence on a large scale, while each individual block captures the spatial dependence on a smaller scale. The multivariate geostatistical model belongs to the class of Latent Gaussian Models; thus, to achieve fast Bayesian inference, it is used the integrated nested Laplace approximation (INLA) method. The good performance of the proposed model is shown through simulations and applications for massive data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Block-NNGP" title="Block-NNGP">Block-NNGP</a>, <a href="https://publications.waset.org/abstracts/search?q=geostatistics" title=" geostatistics"> geostatistics</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20process" title=" gaussian process"> gaussian process</a>, <a href="https://publications.waset.org/abstracts/search?q=GRMF" title=" GRMF"> GRMF</a>, <a href="https://publications.waset.org/abstracts/search?q=INLA" title=" INLA"> INLA</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20models." title=" multivariate models."> multivariate models.</a> </p> <a href="https://publications.waset.org/abstracts/170871/fast-bayesian-inference-of-multivariate-block-nearest-neighbor-gaussian-process-nngp-models-for-large-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170871.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">18297</span> A Time-Varying and Non-Stationary Convolution Spectral Mixture Kernel for Gaussian Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kai%20Chen">Kai Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuguang%20Cui"> Shuguang Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Yin"> Feng Yin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gaussian process (GP) with spectral mixture (SM) kernel demonstrates flexible non-parametric Bayesian learning ability in modeling unknown function. In this work a novel time-varying and non-stationary convolution spectral mixture (TN-CSM) kernel with a significant enhancing of interpretability by using process convolution is introduced. A way decomposing the SM component into an auto-convolution of base SM component and parameterizing it to be input dependent is outlined. Smoothly, performing a convolution between two base SM component yields a novel structure of non-stationary SM component with much better generalized expression and interpretation. The TN-CSM perfectly allows compatibility with the stationary SM kernel in terms of kernel form and spectral base ignored and confused by previous non-stationary kernels. On synthetic and real-world datatsets, experiments show the time-varying characteristics of hyper-parameters in TN-CSM and compare the learning performance of TN-CSM with popular and representative non-stationary GP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process" title="Gaussian process">Gaussian process</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20mixture" title=" spectral mixture"> spectral mixture</a>, <a href="https://publications.waset.org/abstracts/search?q=non-stationary" title=" non-stationary"> non-stationary</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution" title=" convolution"> convolution</a> </p> <a href="https://publications.waset.org/abstracts/131675/a-time-varying-and-non-stationary-convolution-spectral-mixture-kernel-for-gaussian-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131675.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">196</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">18296</span> Development of Computational Approach for Calculation of Hydrogen Solubility in Hydrocarbons for Treatment of Petroleum</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20Sumayli">Abdulrahman Sumayli</a>, <a href="https://publications.waset.org/abstracts/search?q=Saad%20M.%20AlShahrani"> Saad M. AlShahrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=temperature" title="temperature">temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=pressure%20variations" title=" pressure variations"> pressure variations</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=oil%20treatment" title=" oil treatment"> oil treatment</a> </p> <a href="https://publications.waset.org/abstracts/173918/development-of-computational-approach-for-calculation-of-hydrogen-solubility-in-hydrocarbons-for-treatment-of-petroleum" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173918.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">69</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">18295</span> Human Action Recognition Using Variational Bayesian HMM with Dirichlet Process Mixture of Gaussian Wishart Emission Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20action%20recognition" title="human action recognition">human action recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20HMM" title=" Bayesian HMM"> Bayesian HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixture%20model" title=" Dirichlet process mixture model"> Dirichlet process mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian-Wishart%20emission%20model" title=" Gaussian-Wishart emission model"> Gaussian-Wishart emission model</a>, <a href="https://publications.waset.org/abstracts/search?q=Variational%20Bayesian%20inference" title=" Variational Bayesian inference"> Variational Bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=prior%20distribution%20and%20approximate%20posterior%20distribution" title=" prior distribution and approximate posterior distribution"> prior distribution and approximate posterior distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=KTH%20dataset" title=" KTH dataset"> KTH dataset</a> </p> <a href="https://publications.waset.org/abstracts/49713/human-action-recognition-using-variational-bayesian-hmm-with-dirichlet-process-mixture-of-gaussian-wishart-emission-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49713.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">353</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">18294</span> Machine Learning Analysis of Student Success in Introductory Calculus Based Physics I Course</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chandra%20Prayaga">Chandra Prayaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Aaron%20Wade"> Aaron Wade</a>, <a href="https://publications.waset.org/abstracts/search?q=Lakshmi%20Prayaga"> Lakshmi Prayaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Gopi%20Shankar%20Mallu"> Gopi Shankar Mallu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-. <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=student%20success" title=" student success"> student success</a>, <a href="https://publications.waset.org/abstracts/search?q=physics%20course" title=" physics course"> physics course</a>, <a href="https://publications.waset.org/abstracts/search?q=grades" title=" grades"> grades</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data" title=" synthetic data"> synthetic data</a>, <a href="https://publications.waset.org/abstracts/search?q=CTGAN" title=" CTGAN"> CTGAN</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20copula%20CTGAN" title=" gaussian copula CTGAN"> gaussian copula CTGAN</a> </p> <a href="https://publications.waset.org/abstracts/183001/machine-learning-analysis-of-student-success-in-introductory-calculus-based-physics-i-course" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183001.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">44</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">18293</span> Propagation of Cos-Gaussian Beam in Photorefractive Crystal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Keshavarz">A. Keshavarz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A physical model for guiding the wave in photorefractive media is studied. Propagation of cos-Gaussian beam as the special cases of sinusoidal-Gaussian beams in photorefractive crystal is simulated numerically by the Crank-Nicolson method in one dimension. Results show that the beam profile deforms as the energy transfers from the center to the tails under propagation. This simulation approach is of significant interest for application in optical telecommunication. The results are presented graphically and discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beam%20propagation" title="beam propagation">beam propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=cos-Gaussian%20beam" title=" cos-Gaussian beam"> cos-Gaussian beam</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title=" numerical simulation"> numerical simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=photorefractive%20crystal" title=" photorefractive crystal"> photorefractive crystal</a> </p> <a href="https://publications.waset.org/abstracts/33883/propagation-of-cos-gaussian-beam-in-photorefractive-crystal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33883.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">499</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">18292</span> Frequency Offset Estimation Schemes Based on ML for OFDM Systems in Non-Gaussian Noise Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, frequency offset (FO) estimation schemes robust to the non-Gaussian noise environments are proposed for orthogonal frequency division multiplexing (OFDM) systems. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise environments is proposed, and then, the complexity of the ML estimation scheme is reduced by employing a reduced set of candidate values. In numerical results, it is demonstrated that the proposed schemes provide a significant performance improvement over the conventional estimation scheme in non-Gaussian noise environments while maintaining the performance similar to the estimation performance in Gaussian noise environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20offset%20estimation" title="frequency offset estimation">frequency offset estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood" title=" maximum-likelihood"> maximum-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%20noise%0D%0Aenvironment" title=" non-Gaussian noise environment"> non-Gaussian noise environment</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=training%20symbol" title=" training symbol"> training symbol</a> </p> <a href="https://publications.waset.org/abstracts/9430/frequency-offset-estimation-schemes-based-on-ml-for-ofdm-systems-in-non-gaussian-noise-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9430.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">353</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">18291</span> Gaussian Operations with a Single Trapped Ion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bruna%20G.%20M.%20Ara%C3%BAjo">Bruna G. M. Araújo</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20M.%20M.%20Q.%20Cruz"> Pedro M. M. Q. Cruz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this letter, we review the literature of the major concepts that govern Gaussian quantum information. As we work with quantum information and computation with continuous variables, Gaussian states are needed to better describe these systems. Analyzing a single ion locked in a Paul trap we use the interaction picture to obtain a toolbox of Gaussian operations with the ion-laser interaction Hamiltionian. This is achieved exciting the ion through the combination of two lasers of distinct frequencies corresponding to different sidebands of the external degrees of freedom. First we study the case of a trap with 1 mode and then the case with 2 modes. In this way, we achieve different continuous variables gates just by changing the external degrees of freedom of the trap and combining the Hamiltonians of blue and red sidebands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paul%20trap" title="Paul trap">Paul trap</a>, <a href="https://publications.waset.org/abstracts/search?q=ion-laser%20interaction" title=" ion-laser interaction"> ion-laser interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20operations" title=" Gaussian operations"> Gaussian operations</a> </p> <a href="https://publications.waset.org/abstracts/18445/gaussian-operations-with-a-single-trapped-ion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18445.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">685</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">18290</span> Unsupervised Reciter Recognition Using Gaussian Mixture Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Alwosheel">Ahmad Alwosheel</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Alqaraawi"> Ahmed Alqaraawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quran" title="Quran">Quran</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20recognition" title=" speaker recognition"> speaker recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=reciter%20recognition" title=" reciter recognition"> reciter recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20Mixture%20Model" title=" Gaussian Mixture Model"> Gaussian Mixture Model</a> </p> <a href="https://publications.waset.org/abstracts/46532/unsupervised-reciter-recognition-using-gaussian-mixture-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46532.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">380</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">18289</span> Simulation of Propagation of Cos-Gaussian Beam in Strongly Nonlocal Nonlinear Media Using Paraxial Group Transformation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Keshavarz">A. Keshavarz</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Roosta"> Z. Roosta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, propagation of cos-Gaussian beam in strongly nonlocal nonlinear media has been stimulated by using paraxial group transformation. At first, cos-Gaussian beam, nonlocal nonlinear media, critical power, transfer matrix, and paraxial group transformation are introduced. Then, the propagation of the cos-Gaussian beam in strongly nonlocal nonlinear media is simulated. Results show that beam propagation has periodic structure during self-focusing effect in this case. However, this simple method can be used for investigation of propagation of kinds of beams in ABCD optical media. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=paraxial%20group%20transformation" title="paraxial group transformation">paraxial group transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlocal%20nonlinear%20media" title=" nonlocal nonlinear media"> nonlocal nonlinear media</a>, <a href="https://publications.waset.org/abstracts/search?q=cos-Gaussian%20beam" title=" cos-Gaussian beam"> cos-Gaussian beam</a>, <a href="https://publications.waset.org/abstracts/search?q=ABCD%20law" title=" ABCD law"> ABCD law</a> </p> <a href="https://publications.waset.org/abstracts/52660/simulation-of-propagation-of-cos-gaussian-beam-in-strongly-nonlocal-nonlinear-media-using-paraxial-group-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52660.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">342</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">18288</span> Use of Gaussian-Euclidean Hybrid Function Based Artificial Immune System for Breast Cancer Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cuneyt%20Yucelbas">Cuneyt Yucelbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Seral%20Ozsen"> Seral Ozsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Sule%20Yucelbas"> Sule Yucelbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Gulay%20Tezel"> Gulay Tezel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20immune%20system" title="artificial immune system">artificial immune system</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20diagnosis" title=" breast cancer diagnosis"> breast cancer diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Euclidean%20function" title=" Euclidean function"> Euclidean function</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20function" title=" Gaussian function"> Gaussian function</a> </p> <a href="https://publications.waset.org/abstracts/5135/use-of-gaussian-euclidean-hybrid-function-based-artificial-immune-system-for-breast-cancer-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5135.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">435</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">18287</span> Closed-Form Sharma-Mittal Entropy Rate for Gaussian Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Septimia%20Sarbu">Septimia Sarbu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The entropy rate of a stochastic process is a fundamental concept in information theory. It provides a limit to the amount of information that can be transmitted reliably over a communication channel, as stated by Shannon's coding theorems. Recently, researchers have focused on developing new measures of information that generalize Shannon's classical theory. The aim is to design more efficient information encoding and transmission schemes. This paper continues the study of generalized entropy rates, by deriving a closed-form solution to the Sharma-Mittal entropy rate for Gaussian processes. Using the squeeze theorem, we solve the limit in the definition of the entropy rate, for different values of alpha and beta, which are the parameters of the Sharma-Mittal entropy. In the end, we compare it with Shannon and Rényi's entropy rates for Gaussian processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20entropies" title="generalized entropies">generalized entropies</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharma-Mittal%20entropy%20rate" title=" Sharma-Mittal entropy rate"> Sharma-Mittal entropy rate</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20processes" title=" Gaussian processes"> Gaussian processes</a>, <a href="https://publications.waset.org/abstracts/search?q=eigenvalues%20of%20the%20covariance%20matrix" title=" eigenvalues of the covariance matrix"> eigenvalues of the covariance matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=squeeze%20theorem" title=" squeeze theorem "> squeeze theorem </a> </p> <a href="https://publications.waset.org/abstracts/32177/closed-form-sharma-mittal-entropy-rate-for-gaussian-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32177.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">519</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">18286</span> Non-Parametric Regression over Its Parametric Couterparts with Large Sample Size</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jude%20Opara">Jude Opara</a>, <a href="https://publications.waset.org/abstracts/search?q=Esemokumo%20Perewarebo%20Akpos"> Esemokumo Perewarebo Akpos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is on non-parametric linear regression over its parametric counterparts with large sample size. Data set on anthropometric measurement of primary school pupils was taken for the analysis. The study used 50 randomly selected pupils for the study. The set of data was subjected to normality test, and it was discovered that the residuals are not normally distributed (i.e. they do not follow a Gaussian distribution) for the commonly used least squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling technique. The algorithms for the nonparametric Theil’s regression are stated in this paper as well as its parametric OLS counterpart. The use of a programming language software known as “R Development” was used in this paper. From the analysis, the result showed that there exists a significant relationship between the response and the explanatory variable for both the parametric and non-parametric regression. To know the efficiency of one method over the other, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) are used, and it is discovered that the nonparametric regression performs better than its parametric regression counterparts due to their lower values in both the AIC and BIC. The study however recommends that future researchers should study a similar work by examining the presence of outliers in the data set, and probably expunge it if detected and re-analyze to compare results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Theil%E2%80%99s%20regression" title="Theil’s regression">Theil’s regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20information%20criterion" title=" Bayesian information criterion"> Bayesian information criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=Akaike%20information%20criterion" title=" Akaike information criterion"> Akaike information criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=OLS" title=" OLS"> OLS</a> </p> <a href="https://publications.waset.org/abstracts/58536/non-parametric-regression-over-its-parametric-couterparts-with-large-sample-size" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58536.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">305</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">18285</span> System of Linear Equations, Gaussian Elimination</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Khan">Rabia Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nargis%20Munir"> Nargis Munir</a>, <a href="https://publications.waset.org/abstracts/search?q=Suriya%20Gharib"> Suriya Gharib</a>, <a href="https://publications.waset.org/abstracts/search?q=Syeda%20Roshana%20Ali"> Syeda Roshana Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper linear equations are discussed in detail along with elimination method. Gaussian elimination and Gauss Jordan schemes are carried out to solve the linear system of equation. This paper comprises of matrix introduction, and the direct methods for linear equations. The goal of this research was to analyze different elimination techniques of linear equations and measure the performance of Gaussian elimination and Gauss Jordan method, in order to find their relative importance and advantage in the field of symbolic and numeric computation. The purpose of this research is to revise an introductory concept of linear equations, matrix theory and forms of Gaussian elimination through which the performance of Gauss Jordan and Gaussian elimination can be measured. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=direct" title="direct">direct</a>, <a href="https://publications.waset.org/abstracts/search?q=indirect" title=" indirect"> indirect</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20stage" title=" backward stage"> backward stage</a>, <a href="https://publications.waset.org/abstracts/search?q=forward%20stage" title=" forward stage"> forward stage</a> </p> <a href="https://publications.waset.org/abstracts/33569/system-of-linear-equations-gaussian-elimination" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33569.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">595</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">18284</span> Learning the Dynamics of Articulated Tracked Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20Gianni">Mario Gianni</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20A.%20Ruiz%20Garcia"> Manuel A. Ruiz Garcia</a>, <a href="https://publications.waset.org/abstracts/search?q=Fiora%20Pirri"> Fiora Pirri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20processes" title="Dirichlet processes">Dirichlet processes</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20mixture%20models" title=" gaussian mixture models"> gaussian mixture models</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20motion%20patterns" title=" learning motion patterns"> learning motion patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=tracked%20robots%20for%20urban%20search%20and%20rescue" title=" tracked robots for urban search and rescue"> tracked robots for urban search and rescue</a> </p> <a href="https://publications.waset.org/abstracts/45613/learning-the-dynamics-of-articulated-tracked-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45613.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">449</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=Gaussian%20process%20regression&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression&page=4">4</a></li> <li class="page-item"><a class="page-link" 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