CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: Bayesian learning</title> <meta name="description" content="Search results for: Bayesian learning"> <meta name="keywords" content="Bayesian learning"> <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 navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="Bayesian learning" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Bayesian learning"> <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> 7431</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Bayesian learning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7431</span> Modern Machine Learning Conniptions for Automatic Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Jagadeesh%20Kumar">S. Jagadeesh Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20speech%20recognition" title="automatic speech recognition">automatic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20methods" title=" deep learning methods"> deep learning methods</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20archetypes" title=" machine learning archetypes"> machine learning archetypes</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning" title=" Bayesian learning"> Bayesian learning</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20and%20unsupervised%20learning" title=" supervised and unsupervised learning"> supervised and unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/71467/modern-machine-learning-conniptions-for-automatic-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71467.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">447</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">7430</span> Factorization of Computations in Bayesian Networks: Interpretation of Factors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linda%20Smail">Linda Smail</a>, <a href="https://publications.waset.org/abstracts/search?q=Zineb%20Azouz"> Zineb Azouz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P(S) where S is a subset of I. The general idea is to write the expression of P(S) in the form of a product of factors where each factor is easy to compute. More importantly, it will be very useful to give an interpretation of each of the factors in terms of conditional probabilities. This paper considers a semantic interpretation of the factors involved in computing marginal probabilities in Bayesian networks. Establishing such a semantic interpretations is indeed interesting and relevant in the case of large Bayesian networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20networks" title="Bayesian networks">Bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=D-Separation" title=" D-Separation"> D-Separation</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20two%20Bayesian%20networks" title=" level two Bayesian networks"> level two Bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=factorization%20of%20computation" title=" factorization of computation"> factorization of computation</a> </p> <a href="https://publications.waset.org/abstracts/18829/factorization-of-computations-in-bayesian-networks-interpretation-of-factors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18829.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">529</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7429</span> Networked Implementation of Milling Stability Optimization with Bayesian Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christoph%20Ramsauer">Christoph Ramsauer</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaydeep%20Karandikar"> Jaydeep Karandikar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tony%20Schmitz"> Tony Schmitz</a>, <a href="https://publications.waset.org/abstracts/search?q=Friedrich%20Bleicher"> Friedrich Bleicher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machining stability is an important limitation to discrete part machining. In this work, a networked implementation of milling stability optimization with Bayesian learning is presented. The milling process was monitored with a wireless sensory tool holder instrumented with an accelerometer at the Vienna University of Technology, Vienna, Austria. The recorded data from a milling test cut is used to classify the cut as stable or unstable based on the frequency analysis. The test cut result is fed to a Bayesian stability learning algorithm at the University of Tennessee, Knoxville, Tennessee, USA. The algorithm calculates the probability of stability as a function of axial depth of cut and spindle speed and recommends the parameters for the next test cut. The iterative process between two transatlantic locations repeats until convergence to a stable optimal process parameter set is achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machining%20stability" title="machining stability">machining stability</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=sensor" title=" sensor"> sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/135659/networked-implementation-of-milling-stability-optimization-with-bayesian-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135659.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">206</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">7428</span> An Exploratory Sequential Design: A Mixed Methods Model for the Statistics Learning Assessment with a Bayesian Network Representation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhidong%20Zhang">Zhidong Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study established a mixed method model in assessing statistics learning with Bayesian network models. There are three variants in exploratory sequential designs. There are three linked steps in one of the designs: qualitative data collection and analysis, quantitative measure, instrument, intervention, and quantitative data collection analysis. The study used a scoring model of analysis of variance (ANOVA) as a content domain. The research study is to examine students’ learning in both semantic and performance aspects at fine grain level. The ANOVA score model, y = α+ βx1 + γx1+ ε, as a cognitive task to collect data during the student learning process. When the learning processes were decomposed into multiple steps in both semantic and performance aspects, a hierarchical Bayesian network was established. This is a theory-driven process. The hierarchical structure was gained based on qualitative cognitive analysis. The data from students’ ANOVA score model learning was used to give evidence to the hierarchical Bayesian network model from the evidential variables. Finally, the assessment results of students’ ANOVA score model learning were reported. Briefly, this was a mixed method research design applied to statistics learning assessment. The mixed methods designs expanded more possibilities for researchers to establish advanced quantitative models initially with a theory-driven qualitative mode. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exploratory%20sequential%20design" title="exploratory sequential design">exploratory sequential design</a>, <a href="https://publications.waset.org/abstracts/search?q=ANOVA%20score%20model" title=" ANOVA score model"> ANOVA score model</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network%20model" title=" Bayesian network model"> Bayesian network model</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20methods%20research%20design" title=" mixed methods research design"> mixed methods research design</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20analysis" title=" cognitive analysis"> cognitive analysis</a> </p> <a href="https://publications.waset.org/abstracts/102367/an-exploratory-sequential-design-a-mixed-methods-model-for-the-statistics-learning-assessment-with-a-bayesian-network-representation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102367.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">178</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">7427</span> Identification of Bayesian Network with Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Raouf%20Benmakrelouf">Mohamed Raouf Benmakrelouf</a>, <a href="https://publications.waset.org/abstracts/search?q=Wafa%20Karouche"> Wafa Karouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Rynkiewicz"> Joseph Rynkiewicz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an alternative method to construct a Bayesian Network (BN). This method relies on a convolutional neural network (CNN classifier), which determinates the edges of the network skeleton. We train a CNN on a normalized empirical probability density distribution (NEPDF) for predicting causal interactions and relationships. We have to find the optimal Bayesian network structure for causal inference. Indeed, we are undertaking a search for pair-wise causality, depending on considered causal assumptions. In order to avoid unreasonable causal structure, we consider a blacklist and a whitelist of causality senses. We tested the method on real data to assess the influence of education on the voting intention for the extreme right-wing party. We show that, with this method, we get a safer causal structure of variables (Bayesian Network) and make to identify a variable that satisfies the backdoor criterion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title="Bayesian network">Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20learning" title=" structure learning"> structure learning</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20search" title=" optimal search"> optimal search</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=causal%20inference" title=" causal inference"> causal inference</a> </p> <a href="https://publications.waset.org/abstracts/151560/identification-of-bayesian-network-with-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151560.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">176</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">7426</span> Diagnostic Assessment for Mastery Learning of Engineering Students with a Bayesian Network Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhidong%20Zhang">Zhidong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yingchen%20Yang"> Yingchen Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a diagnostic assessment model for Mastery Engineering Learning was established based on a group of undergraduate students who studied in an engineering course. A diagnostic assessment model can examine both students' learning process and report achievement results. One very unique characteristic is that the diagnostic assessment model can recognize the errors and anything blocking students in their learning processes. The feedback is provided to help students to know how to solve the learning problems with alternative strategies and help the instructor to find alternative pedagogical strategies in the instructional designs. Dynamics is a core course in which is a common course being shared by several engineering programs. This course is a very challenging for engineering students to solve the problems. Thus knowledge acquisition and problem-solving skills are crucial for student success. Therefore, developing an effective and valid assessment model for student learning are of great importance. Diagnostic assessment is such a model which can provide effective feedback for both students and instructor in the mastery of engineering learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20assessment" title="diagnostic assessment">diagnostic assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=mastery%20learning" title=" mastery learning"> mastery learning</a>, <a href="https://publications.waset.org/abstracts/search?q=engineering" title=" engineering"> engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20network%20model" title=" bayesian network model"> bayesian network model</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20processes" title=" learning processes"> learning processes</a> </p> <a href="https://publications.waset.org/abstracts/95964/diagnostic-assessment-for-mastery-learning-of-engineering-students-with-a-bayesian-network-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95964.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">152</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">7425</span> Employing Bayesian Artificial Neural Network for Evaluation of Cold Rolling Force</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Kooche%20Baghy">P. Kooche Baghy</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Eskandari"> S. Eskandari</a>, <a href="https://publications.waset.org/abstracts/search?q=E.javanmard"> E.javanmard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neural network has been used as a predictive means of cold rolling force in this dissertation. Thus, imposed average force on rollers as a mere input and five pertaining parameters to its as a outputs are regarded. According to our study, feed-forward multilayer perceptron network has been selected. Besides, Bayesian algorithm based on the feed-forward back propagation method has been selected due to noisy data. Further, 470 out of 585 all tests were used for network learning and others (115 tests) were considered as assessment criteria. Eventually, by 30 times running the MATLAB software, mean error was obtained 3.84 percent as a criteria of network learning. As a consequence, this the mentioned error on par with other approaches such as numerical and empirical methods is acceptable admittedly. <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=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=cold%20rolling" title=" cold rolling"> cold rolling</a>, <a href="https://publications.waset.org/abstracts/search?q=force%20evaluation" title=" force evaluation"> force evaluation</a> </p> <a href="https://publications.waset.org/abstracts/47601/employing-bayesian-artificial-neural-network-for-evaluation-of-cold-rolling-force" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47601.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">443</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">7424</span> Learning a Bayesian Network for Situation-Aware Smart Home Service: A Case Study with a Robot Vacuum Cleaner</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eu%20Tteum%20Ha">Eu Tteum Ha</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyoung%20Kim"> Seyoung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeongmin%20Kim"> Jeongmin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The smart home environment backed up by IoT (internet of things) technologies enables intelligent services based on the awareness of the situation a user is currently in. One of the convenient sensors for recognizing the situations within a home is the smart meter that can monitor the status of each electrical appliance in real time. This paper aims at learning a Bayesian network that models the causal relationship between the user situations and the status of the electrical appliances. Using such a network, we can infer the current situation based on the observed status of the appliances. However, learning the conditional probability tables (CPTs) of the network requires many training examples that cannot be obtained unless the user situations are closely monitored by any means. This paper proposes a method for learning the CPT entries of the network relying only on the user feedbacks generated occasionally. In our case study with a robot vacuum cleaner, the feedback comes in whenever the user gives an order to the robot adversely from its preprogrammed setting. Given a network with randomly initialized CPT entries, our proposed method uses this feedback information to adjust relevant CPT entries in the direction of increasing the probability of recognizing the desired situations. Simulation experiments show that our method can rapidly improve the recognition performance of the Bayesian network using a relatively small number of feedbacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title="Bayesian network">Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=IoT" title=" IoT"> IoT</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=situation%20-awareness" title=" situation -awareness"> situation -awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20home" title=" smart home"> smart home</a> </p> <a href="https://publications.waset.org/abstracts/23000/learning-a-bayesian-network-for-situation-aware-smart-home-service-a-case-study-with-a-robot-vacuum-cleaner" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23000.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">523</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">7423</span> A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pan%20Long">Pan Long</a>, <a href="https://publications.waset.org/abstracts/search?q=Bi%20Dongjie"> Bi Dongjie</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Xifeng"> Li Xifeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Xie%20Yongle"> Xie Yongle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complex-valued%20signal%20processing" title="complex-valued signal processing">complex-valued signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20aperture%20radar" title=" synthetic aperture radar"> synthetic aperture radar</a>, <a href="https://publications.waset.org/abstracts/search?q=2-D%20radar%20imaging" title=" 2-D radar imaging"> 2-D radar imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=compressive%20sensing" title=" compressive sensing"> compressive sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20Bayesian%20learning" title=" sparse Bayesian learning"> sparse Bayesian learning</a> </p> <a href="https://publications.waset.org/abstracts/108404/a-generalized-sparse-bayesian-learning-algorithm-for-near-field-synthetic-aperture-radar-imaging-by-exploiting-impropriety-and-noncircularity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108404.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">131</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">7422</span> Financial Assets Return, Economic Factors and Investor's Behavioral Indicators Relationships Modeling: A Bayesian Networks Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Souissi">Nada Souissi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mourad%20Mroua"> Mourad Mroua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this study is to examine the interaction between financial asset volatility, economic factors and investor's behavioral indicators related to both the company's and the markets stocks for the period from January 2000 to January2020. Using multiple linear regression and Bayesian Networks modeling, results show a positive and negative relationship between investor's psychology index, economic factors and predicted stock market return. We reveal that the application of the Bayesian Discrete Network contributes to identify the different cause and effect relationships between all economic, financial variables and psychology index. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Financial%20asset%20return%20predictability" title="Financial asset return predictability">Financial asset return predictability</a>, <a href="https://publications.waset.org/abstracts/search?q=Economic%20factors" title=" Economic factors"> Economic factors</a>, <a href="https://publications.waset.org/abstracts/search?q=Investor%27s%20psychology%20index" title=" Investor's psychology index"> Investor's psychology index</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20approach" title=" Bayesian approach"> Bayesian approach</a>, <a href="https://publications.waset.org/abstracts/search?q=Probabilistic%20networks" title=" Probabilistic networks"> Probabilistic networks</a>, <a href="https://publications.waset.org/abstracts/search?q=Parametric%20learning" title=" Parametric learning"> Parametric learning</a> </p> <a href="https://publications.waset.org/abstracts/123056/financial-assets-return-economic-factors-and-investors-behavioral-indicators-relationships-modeling-a-bayesian-networks-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123056.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">149</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">7421</span> Optimal Bayesian Control of the Proportion of Defectives in a Manufacturing Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viliam%20Makis">Viliam Makis</a>, <a href="https://publications.waset.org/abstracts/search?q=Farnoosh%20Naderkhani"> Farnoosh Naderkhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Leila%20Jafari"> Leila Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20control%20chart" title="Bayesian control chart">Bayesian control chart</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-Markov%20decision%20process" title=" semi-Markov decision process"> semi-Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20observable%20process" title=" partially observable process"> partially observable process</a> </p> <a href="https://publications.waset.org/abstracts/49751/optimal-bayesian-control-of-the-proportion-of-defectives-in-a-manufacturing-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49751.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">318</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">7420</span> The Effect of Institutions on Economic Growth: An Analysis Based on Bayesian Panel Data Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Anwar">Mohammad Anwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Shah%20Waliullah"> Shah Waliullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigated panel data regression models. This paper used Bayesian and classical methods to study the impact of institutions on economic growth from data (1990-2014), especially in developing countries. Under the classical and Bayesian methodology, the two-panel data models were estimated, which are common effects and fixed effects. For the Bayesian approach, the prior information is used in this paper, and normal gamma prior is used for the panel data models. The analysis was done through WinBUGS14 software. The estimated results of the study showed that panel data models are valid models in Bayesian methodology. In the Bayesian approach, the effects of all independent variables were positively and significantly affected by the dependent variables. Based on the standard errors of all models, we must say that the fixed effect model is the best model in the Bayesian estimation of panel data models. Also, it was proved that the fixed effect model has the lowest value of standard error, as compared to other models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20approach" title="Bayesian approach">Bayesian approach</a>, <a href="https://publications.waset.org/abstracts/search?q=common%20effect" title=" common effect"> common effect</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20effect" title=" fixed effect"> fixed effect</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20effect" title=" random effect"> random effect</a>, <a href="https://publications.waset.org/abstracts/search?q=Dynamic%20Random%20Effect%20Model" title=" Dynamic Random Effect Model"> Dynamic Random Effect Model</a> </p> <a href="https://publications.waset.org/abstracts/161692/the-effect-of-institutions-on-economic-growth-an-analysis-based-on-bayesian-panel-data-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161692.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">68</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">7419</span> Bayesian Approach for Moving Extremes Ranked Set Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Said%20Ali%20Al-Hadhrami">Said Ali Al-Hadhrami</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Ibrahim%20Al-Omari"> Amer Ibrahim Al-Omari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, Bayesian estimation for the mean of exponential distribution is considered using Moving Extremes Ranked Set Sampling (MERSS). Three priors are used; Jeffery, conjugate and constant using MERSS and Simple Random Sampling (SRS). Some properties of the proposed estimators are investigated. It is found that the suggested estimators using MERSS are more efficient than its counterparts based on SRS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title="Bayesian">Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency" title=" efficiency"> efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20extreme%20ranked%20set%20sampling" title=" moving extreme ranked set sampling"> moving extreme ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=ranked%20set%20sampling" title=" ranked set sampling"> ranked set sampling</a> </p> <a href="https://publications.waset.org/abstracts/30733/bayesian-approach-for-moving-extremes-ranked-set-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30733.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">513</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">7418</span> Bayesian Reliability of Weibull Regression with Type-I Censored Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Al%20Omari%20Moahmmed%20Ahmed">Al Omari Moahmmed Ahmed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the Bayesian, we developed an approach by using non-informative prior with covariate and obtained by using Gauss quadrature method to estimate the parameters of the covariate and reliability function of the Weibull regression distribution with Type-I censored data. The maximum likelihood seen that the estimators obtained are not available in closed forms, although they can be solved it by using Newton-Raphson methods. The comparison criteria are the MSE and the performance of these estimates are assessed using simulation considering various sample size, several specific values of shape parameter. The results show that Bayesian with non-informative prior is better than Maximum Likelihood Estimator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-informative%20prior" title="non-informative prior">non-informative prior</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20method" title=" Bayesian method"> Bayesian method</a>, <a href="https://publications.waset.org/abstracts/search?q=type-I%20censoring" title=" type-I censoring"> type-I censoring</a>, <a href="https://publications.waset.org/abstracts/search?q=Gauss%20quardature" title=" Gauss quardature"> Gauss quardature</a> </p> <a href="https://publications.waset.org/abstracts/18728/bayesian-reliability-of-weibull-regression-with-type-i-censored-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18728.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">503</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">7417</span> Hybrid Structure Learning Approach for Assessing the Phosphate Laundries Impact </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emna%20Benmohamed">Emna Benmohamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Hela%20Ltifi"> Hela Ltifi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Ben%20Ayed"> Mounir Ben Ayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title="Bayesian network">Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20knowledge" title=" expert knowledge"> expert knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20learning" title=" structure learning"> structure learning</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20water%20analysis" title=" surface water analysis"> surface water analysis</a> </p> <a href="https://publications.waset.org/abstracts/119016/hybrid-structure-learning-approach-for-assessing-the-phosphate-laundries-impact" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119016.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">128</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">7416</span> BART Matching Method: Using Bayesian Additive Regression Tree for Data Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gianna%20Zou">Gianna Zou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BART" title="BART">BART</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/149989/bart-matching-method-using-bayesian-additive-regression-tree-for-data-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149989.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">147</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">7415</span> Breast Cancer Detection Using Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiwan%20Kumar">Jiwan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Pooja"> Pooja</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandeep%20Negi"> Sandeep Negi</a>, <a href="https://publications.waset.org/abstracts/search?q=Anjum%20Rouf"> Anjum Rouf</a>, <a href="https://publications.waset.org/abstracts/search?q=Amit%20Kumar"> Amit Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveen%20Lakra"> Naveen Lakra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title="Bayesian network">Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20learning" title=" ensemble learning"> ensemble learning</a>, <a href="https://publications.waset.org/abstracts/search?q=understandable" title=" understandable"> understandable</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20making%20better" title=" data making better"> data making better</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a> </p> <a href="https://publications.waset.org/abstracts/185470/breast-cancer-detection-using-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185470.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">52</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">7414</span> Optimized Dynamic Bayesian Networks and Neural Verifier Test Applied to On-Line Isolated Characters Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Redouane%20Tlemsani">Redouane Tlemsani</a>, <a href="https://publications.waset.org/abstracts/search?q=Redouane"> Redouane</a>, <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Kouninef"> Belkacem Kouninef</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkader%20Benyettou"> Abdelkader Benyettou </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20on%20line%20character%20recognition" title="Arabic on line character recognition">Arabic on line character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20Bayesian%20network" title=" dynamic Bayesian network"> dynamic Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=networks" title=" networks "> networks </a> </p> <a href="https://publications.waset.org/abstracts/34593/optimized-dynamic-bayesian-networks-and-neural-verifier-test-applied-to-on-line-isolated-characters-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34593.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">617</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">7413</span> Constructing a Bayesian Network for Solar Energy in Egypt Using Life Cycle Analysis and Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rawaa%20H.%20El-Bidweihy">Rawaa H. El-Bidweihy</a>, <a href="https://publications.waset.org/abstracts/search?q=Hisham%20M.%20Abdelsalam"> Hisham M. Abdelsalam</a>, <a href="https://publications.waset.org/abstracts/search?q=Ihab%20A.%20El-Khodary"> Ihab A. El-Khodary</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=auto%20correlation" title=" auto correlation"> auto correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title=" Bayesian network"> Bayesian network</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=life%20cycle" title=" life cycle"> life cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20correlation" title=" partial correlation"> partial correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energy" title=" renewable energy"> renewable energy</a>, <a href="https://publications.waset.org/abstracts/search?q=SARIMA" title=" SARIMA"> SARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20energy" title=" solar energy"> solar energy</a> </p> <a href="https://publications.waset.org/abstracts/127931/constructing-a-bayesian-network-for-solar-energy-in-egypt-using-life-cycle-analysis-and-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127931.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">155</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">7412</span> Fault Tree Analysis and Bayesian Network for Fire and Explosion of Crude Oil Tanks: Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Zerouali">B. Zerouali</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Kara"> M. Kara</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Hamaidi"> B. Hamaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mahdjoub"> H. Mahdjoub</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Rouabhia"> S. Rouabhia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a safety analysis for crude oil tanks to prevent undesirable events that may cause catastrophic accidents. The estimation of the probability of damage to industrial systems is carried out through a series of steps, and in accordance with a specific methodology. In this context, this work involves developing an assessment tool and risk analysis at the level of crude oil tanks system, based primarily on identification of various potential causes of crude oil tanks fire and explosion by the use of Fault Tree Analysis (FTA), then improved risk modelling by Bayesian Networks (BNs). Bayesian approach in the evaluation of failure and quantification of risks is a dynamic analysis approach. For this reason, have been selected as an analytical tool in this study. Research concludes that the Bayesian networks have a distinct and effective method in the safety analysis because of the flexibility of its structure; it is suitable for a wide variety of accident scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20networks" title="bayesian networks">bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=crude%20oil%20tank" title=" crude oil tank"> crude oil tank</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20tree" title=" fault tree"> fault tree</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=safety" title=" safety"> safety</a> </p> <a href="https://publications.waset.org/abstracts/30636/fault-tree-analysis-and-bayesian-network-for-fire-and-explosion-of-crude-oil-tanks-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30636.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">660</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">7411</span> Using Dynamic Bayesian Networks to Characterize and Predict Job Placement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xupin%20Zhang">Xupin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Caterina%20Bramati"> Maria Caterina Bramati</a>, <a href="https://publications.waset.org/abstracts/search?q=Enrest%20Fokoue"> Enrest Fokoue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding the career placement of graduates from the university is crucial for both the qualities of education and ultimate satisfaction of students. In this research, we adapt the capabilities of dynamic Bayesian networks to characterize and predict students’ job placement using data from various universities. We also provide elements of the estimation of the indicator (score) of the strength of the network. The research focuses on overall findings as well as specific student groups including international and STEM students and their insight on the career path and what changes need to be made. The derived Bayesian network has the potential to be used as a tool for simulating the career path for students and ultimately helps universities in both academic advising and career counseling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20bayesian%20networks" title="dynamic bayesian networks">dynamic bayesian networks</a>, <a href="https://publications.waset.org/abstracts/search?q=indicator%20estimation" title=" indicator estimation"> indicator estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20placement" title=" job placement"> job placement</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networks" title=" social networks"> social networks</a> </p> <a href="https://publications.waset.org/abstracts/61886/using-dynamic-bayesian-networks-to-characterize-and-predict-job-placement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61886.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">379</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">7410</span> Forecasting Stock Indexes Using Bayesian Additive Regression Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Darren%20Zou">Darren Zou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BART" title="BART">BART</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=predict" title=" predict"> predict</a>, <a href="https://publications.waset.org/abstracts/search?q=stock" title=" stock"> stock</a> </p> <a href="https://publications.waset.org/abstracts/124504/forecasting-stock-indexes-using-bayesian-additive-regression-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124504.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">130</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">7409</span> Multi-Agent Searching Adaptation Using Levy Flight and Inferential Reasoning </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagir%20M.%20Yusuf">Sagir M. Yusuf</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Baber"> Chris Baber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Levy%20flight" title="Levy flight">Levy flight</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20constraint%20optimization%20problem" title=" distributed constraint optimization problem"> distributed constraint optimization problem</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20system" title=" multi-agent system"> multi-agent system</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-robot%20coordination" title=" multi-robot coordination"> multi-robot coordination</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20system" title=" autonomous system"> autonomous system</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a> </p> <a href="https://publications.waset.org/abstracts/116932/multi-agent-searching-adaptation-using-levy-flight-and-inferential-reasoning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116932.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">144</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">7408</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">7407</span> Inferential Reasoning for Heterogeneous Multi-Agent Mission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagir%20M.%20Yusuf">Sagir M. Yusuf</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Baber"> Chris Baber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20constraint%20optimization%20problem" title="distributed constraint optimization problem">distributed constraint optimization problem</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20system" title=" multi-agent system"> multi-agent system</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-robot%20coordination" title=" multi-robot coordination"> multi-robot coordination</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20system" title=" autonomous system"> autonomous system</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a> </p> <a href="https://publications.waset.org/abstracts/116896/inferential-reasoning-for-heterogeneous-multi-agent-mission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116896.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">154</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">7406</span> Design of Bayesian MDS Sampling Plan Based on the Process Capability Index</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Davood%20Shishebori">Davood Shishebori</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Saber%20Fallah%20Nezhad"> Mohammad Saber Fallah Nezhad</a>, <a href="https://publications.waset.org/abstracts/search?q=Sina%20Seifi"> Sina Seifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a variable multiple dependent state (MDS) sampling plan is developed based on the process capability index using Bayesian approach. The optimal parameters of the developed sampling plan with respect to constraints related to the risk of consumer and producer are presented. Two comparison studies have been done. First, the methods of double sampling model, sampling plan for resubmitted lots and repetitive group sampling (RGS) plan are elaborated and average sample numbers of the developed MDS plan and other classical methods are compared. A comparison study between the developed MDS plan based on Bayesian approach and the exact probability distribution is carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MDS%20sampling%20plan" title="MDS sampling plan">MDS sampling plan</a>, <a href="https://publications.waset.org/abstracts/search?q=RGS%20plan" title=" RGS plan"> RGS plan</a>, <a href="https://publications.waset.org/abstracts/search?q=sampling%20plan%20for%20resubmitted%20lots" title=" sampling plan for resubmitted lots"> sampling plan for resubmitted lots</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20capability%20index%20%28PCI%29" title=" process capability index (PCI)"> process capability index (PCI)</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20sample%20number%20%28ASN%29" title=" average sample number (ASN)"> average sample number (ASN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20approach" title=" Bayesian approach"> Bayesian approach</a> </p> <a href="https://publications.waset.org/abstracts/74571/design-of-bayesian-mds-sampling-plan-based-on-the-process-capability-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74571.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">301</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">7405</span> Estimating Occupancy in Residential Context Using Bayesian Networks for Energy Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manar%20Amayri">Manar Amayri</a>, <a href="https://publications.waset.org/abstracts/search?q=Hussain%20Kazimi"> Hussain Kazimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Quoc-Dung%20Ngo"> Quoc-Dung Ngo</a>, <a href="https://publications.waset.org/abstracts/search?q=Stephane%20Ploix"> Stephane Ploix</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A general approach is proposed to determine occupant behavior (occupancy and activity) in residential buildings and to use these estimates for improved energy management. Occupant behaviour is modelled with a Bayesian Network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO₂ concentration. Two case studies are presented which show the real world applicability of estimating occupant behaviour in this way. Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort standards. The efficiency gains are strongly correlated with occupant behaviour and accuracy of the occupancy estimates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy" title="energy">energy</a>, <a href="https://publications.waset.org/abstracts/search?q=management" title=" management"> management</a>, <a href="https://publications.waset.org/abstracts/search?q=control" title=" control"> control</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20methods" title=" Bayesian methods"> Bayesian methods</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20theory" title=" learning theory"> learning theory</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20networks" title=" sensor networks"> sensor networks</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20modelling%20and%20knowledge%20based%20systems" title=" knowledge modelling and knowledge based systems"> knowledge modelling and knowledge based systems</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=buildings" title=" buildings"> buildings</a> </p> <a href="https://publications.waset.org/abstracts/84739/estimating-occupancy-in-residential-context-using-bayesian-networks-for-energy-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84739.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">370</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">7404</span> A Two-Stage Bayesian Variable Selection Method with the Extension of Lasso for Geo-Referenced Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Georgiana%20Onicescu">Georgiana Onicescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuqian%20Shen"> Yuqian Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the complex nature of geo-referenced data, multicollinearity of the risk factors in public health spatial studies is a commonly encountered issue, which leads to low parameter estimation accuracy because it inflates the variance in the regression analysis. To address this issue, we proposed a two-stage variable selection method by extending the least absolute shrinkage and selection operator (Lasso) to the Bayesian spatial setting, investigating the impact of risk factors to health outcomes. Specifically, in stage I, we performed the variable selection using Bayesian Lasso and several other variable selection approaches. Then, in stage II, we performed the model selection with only the selected variables from stage I and compared again the methods. To evaluate the performance of the two-stage variable selection methods, we conducted a simulation study with different distributions for the risk factors, using geo-referenced count data as the outcome and Michigan as the research region. We considered the cases when all candidate risk factors are independently normally distributed, or follow a multivariate normal distribution with different correlation levels. Two other Bayesian variable selection methods, Binary indicator, and the combination of Binary indicator and Lasso were considered and compared as alternative methods. The simulation results indicated that the proposed two-stage Bayesian Lasso variable selection method has the best performance for both independent and dependent cases considered. When compared with the one-stage approach, and the other two alternative methods, the two-stage Bayesian Lasso approach provides the highest estimation accuracy in all scenarios considered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lasso" title="Lasso">Lasso</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20analysis" title=" Bayesian analysis"> Bayesian analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title=" spatial analysis"> spatial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a> </p> <a href="https://publications.waset.org/abstracts/105063/a-two-stage-bayesian-variable-selection-method-with-the-extension-of-lasso-for-geo-referenced-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105063.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">143</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">7403</span> The Application of Bayesian Heuristic for Scheduling in Real-Time Private Clouds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sahar%20Sohrabi">Sahar Sohrabi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The emergence of Cloud data centers has revolutionized the IT industry. Private Clouds in specific provide Cloud services for certain group of customers/businesses. In a real-time private Cloud each task that is given to the system has a deadline that desirably should not be violated. Scheduling tasks in a real-time private CLoud determine the way available resources in the system are shared among incoming tasks. The aim of the scheduling policy is to optimize the system outcome which for a real-time private Cloud can include: energy consumption, deadline violation, execution time and the number of host switches. Different scheduling policies can be used for scheduling. Each lead to a sub-optimal outcome in a certain settings of the system. A Bayesian Scheduling strategy is proposed for scheduling to further improve the system outcome. The Bayesian strategy showed to outperform all selected policies. It also has the flexibility in dealing with complex pattern of incoming task and has the ability to adapt. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title="cloud computing">cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20private%20cloud" title=" real-time private cloud"> real-time private cloud</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a> </p> <a href="https://publications.waset.org/abstracts/38592/the-application-of-bayesian-heuristic-for-scheduling-in-real-time-private-clouds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38592.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">359</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">7402</span> Brainbow Image Segmentation Using Bayesian Sequential Partitioning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yayun%20Hsu">Yayun Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Henry%20Horng-Shing%20Lu"> Henry Horng-Shing Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate cross talk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds since biological information is inherently included in the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brainbow" title="brainbow">brainbow</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20imaging" title=" 3D imaging"> 3D imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=neuron%20morphology" title=" neuron morphology"> neuron morphology</a>, <a href="https://publications.waset.org/abstracts/search?q=biological%20data%20mining" title=" biological data mining"> biological data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=non-parametric%20learning" title=" non-parametric learning"> non-parametric learning</a> </p> <a href="https://publications.waset.org/abstracts/2189/brainbow-image-segmentation-using-bayesian-sequential-partitioning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2189.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">487</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=Bayesian%20learning&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=247">247</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=248">248</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>