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belief network</title> <meta name="description" content="Search results for: Bayesian belief network"> <meta name="keywords" content="Bayesian belief network"> <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 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Bayesian belief network"> <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> 5411</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Bayesian belief network</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5411</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">5410</span> Development of Terrorist Threat Prediction Model in Indonesia by Using Bayesian Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hilya%20Mudrika%20Arini">Hilya Mudrika Arini</a>, <a href="https://publications.waset.org/abstracts/search?q=Nur%20Aini%20Masruroh"> Nur Aini Masruroh</a>, <a href="https://publications.waset.org/abstracts/search?q=Budi%20Hartono"> Budi Hartono</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are more than 20 terrorist threats from 2002 to 2012 in Indonesia. Despite of this fact, preventive solution through studies in the field of national security in Indonesia has not been conducted comprehensively. This study aims to provide a preventive solution by developing prediction model of the terrorist threat in Indonesia by using Bayesian network. There are eight stages to build the model, started from literature review, build and verify Bayesian belief network to what-if scenario. In order to build the model, four experts from different perspectives are utilized. This study finds several significant findings. First, news and the readiness of terrorist group are the most influent factor. Second, according to several scenarios of the news portion, it can be concluded that the higher positive news proportion, the higher probability of terrorist threat will occur. Therefore, the preventive solution to reduce the terrorist threat in Indonesia based on the model is by keeping the positive news portion to a maximum of 38%. <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=decision%20analysis" title=" decision analysis"> decision analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=national%20security%20system" title=" national security system"> national security system</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/1453/development-of-terrorist-threat-prediction-model-in-indonesia-by-using-bayesian-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1453.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">392</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">5409</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">5408</span> Dynamic Risk Model for Offshore Decommissioning Using Bayesian Belief Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20O.%20Babaleye">Ahmed O. Babaleye</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafet%20E.%20Kurt"> Rafet E. Kurt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The global oil and gas industry is beginning to witness an increase in the number of installations moving towards decommissioning. Decommissioning of offshore installations is a complex, costly and hazardous activity, making safety one of the major concerns. Among existing removal options, complete and partial removal options pose the highest risks. Therefore, a dynamic risk model of the accidents from the two options is important to assess the risks on an overall basis. In this study, a risk-based safety model is developed to conduct quantitative risk analysis (QRA) for jacket structure systems failure. Firstly, bow-tie (BT) technique is utilised to model the causal relationship between the system failure and potential accident scenarios. Subsequently, to relax the shortcomings of BT, Bayesian Belief Networks (BBNs) were established to dynamically assess associated uncertainties and conditional dependencies. The BBN is developed through a similitude mapping of the developed bow-tie. The BBN is used to update the failure probabilities of the contributing elements through diagnostic analysis, thus, providing a case-specific and realistic safety analysis method when compared to a bow-tie. This paper presents the application of dynamic safety analysis to guide the allocation of risk control measures and consequently, drive down the avoidable cost of remediation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20belief%20network" title="Bayesian belief network">Bayesian belief network</a>, <a href="https://publications.waset.org/abstracts/search?q=offshore%20decommissioning" title=" offshore decommissioning"> offshore decommissioning</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20safety%20model" title=" dynamic safety model"> dynamic safety model</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20risk%20analysis" title=" quantitative risk analysis"> quantitative risk analysis</a> </p> <a href="https://publications.waset.org/abstracts/90041/dynamic-risk-model-for-offshore-decommissioning-using-bayesian-belief-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90041.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">280</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">5407</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">5406</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">5405</span> Probabilistic Approach of Dealing with Uncertainties in Distributed Constraint Optimization Problems and Situation Awareness for Multi-agent Systems</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 Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DCOP" title="DCOP">DCOP</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20reasoning" title=" multi-agent reasoning"> multi-agent reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20reasoning" title=" Bayesian reasoning"> Bayesian reasoning</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/116869/probabilistic-approach-of-dealing-with-uncertainties-in-distributed-constraint-optimization-problems-and-situation-awareness-for-multi-agent-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116869.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">119</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">5404</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">5403</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">5402</span> Analyzing Energy Consumption Behavior of Migrated Population in Turkey Using Bayesian Belief Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebru%20Acuner">Ebru Acuner</a>, <a href="https://publications.waset.org/abstracts/search?q=Gulgun%20Kayakutlu"> Gulgun Kayakutlu</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Ozgur%20Kayalica"> M. Ozgur Kayalica</a>, <a href="https://publications.waset.org/abstracts/search?q=Sermin%20Onaygil"> Sermin Onaygil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Turkey, emigration, especially from Syria, has been continuously increasing together with rapid urbanization. In parallel to this, total energy consumption has been growing, rapidly. Unfortunately, domestic energy sources could not meet this energy demand. Hence, there is a need for reliable predictions. For this reason, before making a survey study for the migrated people, an informative questionnaire was prepared to take the opinions of the experts on the main drivers that shape the energy consumption behavior of the migrated people. Totally, 17 experts were answered, and they were analyzed by means of Netica program considering Bayesian belief analysis method. In the analysis, factors affecting energy consumption behaviors as well as strategies, institutions, tools and financing methods to change these behaviors towards efficient consumption were investigated. On the basis of the results, it can be concluded that changing the energy consumption behavior of the migrated people is crucial. In order to be successful, electricity and natural gas prices and tariffs in the market should be arranged considering energy efficiency. In addition, support mechanisms by not only the government but also municipalities should be taken into account while preparing related policies. Also, electric appliance producers should develop and implement strategies and action in favor of the usage of more efficient appliances. Last but not least, non-governmental organizations should support the migrated people to improve their awareness on the efficient consumption for the sustainable future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20belief" title="Bayesian belief">Bayesian belief</a>, <a href="https://publications.waset.org/abstracts/search?q=behavior" title=" behavior"> behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption" title=" energy consumption"> energy consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title=" energy efficiency"> energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=migrated%20people" title=" migrated people"> migrated people</a> </p> <a href="https://publications.waset.org/abstracts/99605/analyzing-energy-consumption-behavior-of-migrated-population-in-turkey-using-bayesian-belief-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99605.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">111</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">5401</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">5400</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">5399</span> Hyperspectral Data Classification Algorithm Based on the Deep Belief and Self-Organizing Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Qingjian">Li Qingjian</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Ke"> Li Ke</a>, <a href="https://publications.waset.org/abstracts/search?q=He%20Chun"> He Chun</a>, <a href="https://publications.waset.org/abstracts/search?q=Huang%20Yong"> Huang Yong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DBN" title="DBN">DBN</a>, <a href="https://publications.waset.org/abstracts/search?q=SOM" title=" SOM"> SOM</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20classification" title=" pattern classification"> pattern classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title=" hyperspectral"> hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20compression" title=" data compression"> data compression</a> </p> <a href="https://publications.waset.org/abstracts/89759/hyperspectral-data-classification-algorithm-based-on-the-deep-belief-and-self-organizing-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89759.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">341</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">5398</span> Improved Dynamic Bayesian Networks Applied to Arabic On Line 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=Abdelkader%20Benyettou"> Abdelkader Benyettou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. 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). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%. <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=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/7319/improved-dynamic-bayesian-networks-applied-to-arabic-on-line-characters-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7319.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">428</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">5397</span> Human Performance Evaluating of Advanced Cardiac Life Support Procedure Using Fault Tree and Bayesian Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shokoufeh%20Abrisham">Shokoufeh Abrisham</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mahmoud%20Hossieni"> Seyed Mahmoud Hossieni</a>, <a href="https://publications.waset.org/abstracts/search?q=Elham%20Pishbin"> Elham Pishbin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a hybrid method based on the fault tree analysis (FTA) and Bayesian networks (BNs) are employed to evaluate the team performance quality of advanced cardiac life support (ACLS) procedures in emergency department. According to American Heart Association (AHA) guidelines, a category relying on staff action leading to clinical incidents and also some discussions with emergency medicine experts, a fault tree model for ACLS procedure is obtained based on the human performance. The obtained FTA model is converted into BNs, and some different scenarios are defined to demonstrate the efficiency and flexibility of the presented model of BNs. Also, a sensitivity analysis is conducted to indicate the effects of team leader presence and uncertainty knowledge of experts on the quality of ACLS. The proposed model based on BNs shows that how the results of risk analysis can be closed to reality comparing to the obtained results based on only FTA in medical procedures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20cardiac%20life%20support" title="advanced cardiac life support">advanced cardiac life support</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20tree%20analysis" title=" fault tree analysis"> fault tree analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20belief%20networks" title=" Bayesian belief networks"> Bayesian belief networks</a>, <a href="https://publications.waset.org/abstracts/search?q=numan%20performance" title=" numan performance"> numan performance</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20systems" title=" healthcare systems"> healthcare systems</a> </p> <a href="https://publications.waset.org/abstracts/100435/human-performance-evaluating-of-advanced-cardiac-life-support-procedure-using-fault-tree-and-bayesian-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100435.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">5396</span> Bayesian Network and Feature Selection for Rank Deficient Inverse Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyugneun%20Lee">Kyugneun Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ikjin%20Lee"> Ikjin Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Parameter estimation with inverse problem often suffers from unfavorable conditions in the real world. Useless data and many input parameters make the problem complicated or insoluble. Data refinement and reformulation of the problem can solve that kind of difficulties. In this research, a method to solve the rank deficient inverse problem is suggested. A multi-physics system which has rank deficiency caused by response correlation is treated. Impeditive information is removed and the problem is reformulated to sequential estimations using Bayesian network (BN) and subset groups. At first, subset grouping of the responses is performed. Feature selection with singular value decomposition (SVD) is used for the grouping. Next, BN inference is used for sequential conditional estimation according to the group hierarchy. Directed acyclic graph (DAG) structure is organized to maximize the estimation ability. Variance ratio of response to noise is used to pairing the estimable parameters by each response. <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=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=rank%20deficiency" title=" rank deficiency"> rank deficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20inverse%20analysis" title=" statistical inverse analysis"> statistical inverse analysis</a> </p> <a href="https://publications.waset.org/abstracts/75870/bayesian-network-and-feature-selection-for-rank-deficient-inverse-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75870.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">314</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">5395</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">5394</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">5393</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">5392</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">5391</span> Enhanced Image Representation for Deep Belief Network Classification of Hyperspectral Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khitem%20Amiri">Khitem Amiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Farah"> Mohamed Farah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20images" title="hyperspectral images">hyperspectral images</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20belief%20network" title=" deep belief network"> deep belief network</a>, <a href="https://publications.waset.org/abstracts/search?q=radiometric%20indices" title=" radiometric indices"> radiometric indices</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a> </p> <a href="https://publications.waset.org/abstracts/93458/enhanced-image-representation-for-deep-belief-network-classification-of-hyperspectral-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93458.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">280</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">5390</span> The Bayesian Premium Under Entropy Loss</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farouk%20Metiri">Farouk Metiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Halim%20Zeghdoudi"> Halim Zeghdoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Riad%20Remita"> Mohamed Riad Remita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Credibility theory is an experience rating technique in actuarial science which can be seen as one of quantitative tools that allows the insurers to perform experience rating, that is, to adjust future premiums based on past experiences. It is used usually in automobile insurance, worker's compensation premium, and IBNR (incurred but not reported claims to the insurer) where credibility theory can be used to estimate the claim size amount. In this study, we focused on a popular tool in credibility theory which is the Bayesian premium estimator, considering Lindley distribution as a claim distribution. We derive this estimator under entropy loss which is asymmetric and squared error loss which is a symmetric loss function with informative and non-informative priors. In a purely Bayesian setting, the prior distribution represents the insurer’s prior belief about the insured’s risk level after collection of the insured’s data at the end of the period. However, the explicit form of the Bayesian premium in the case when the prior is not a member of the exponential family could be quite difficult to obtain as it involves a number of integrations which are not analytically solvable. The paper finds a solution to this problem by deriving this estimator using numerical approximation (Lindley approximation) which is one of the suitable approximation methods for solving such problems, it approaches the ratio of the integrals as a whole and produces a single numerical result. Simulation study using Monte Carlo method is then performed to evaluate this estimator and mean squared error technique is made to compare the Bayesian premium estimator under the above loss functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20estimator" title="bayesian estimator">bayesian estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=credibility%20theory" title=" credibility theory"> credibility theory</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy%20loss" title=" entropy loss"> entropy loss</a>, <a href="https://publications.waset.org/abstracts/search?q=monte%20carlo%20simulation" title=" monte carlo simulation"> monte carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/47704/the-bayesian-premium-under-entropy-loss" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47704.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5389</span> Applied Bayesian Regularized Artificial Neural Network for Up-Scaling Wind Speed Profile and Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aghbalou%20Nihad">Aghbalou Nihad</a>, <a href="https://publications.waset.org/abstracts/search?q=Charki%20Abderafi"> Charki Abderafi</a>, <a href="https://publications.waset.org/abstracts/search?q=Saida%20Rahali"> Saida Rahali</a>, <a href="https://publications.waset.org/abstracts/search?q=Reklaoui%20Kamal"> Reklaoui Kamal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Maximize the benefit from the wind energy potential is the most interest of the wind power stakeholders. As a result, the wind tower size is radically increasing. Nevertheless, choosing an appropriate wind turbine for a selected site require an accurate estimate of vertical wind profile. It is also imperative from cost and maintenance strategy point of view. Then, installing tall towers or even more expensive devices such as LIDAR or SODAR raises the costs of a wind power project. Various models were developed coming within this framework. However, they suffer from complexity, generalization and lacks accuracy. In this work, we aim to investigate the ability of neural network trained using the Bayesian Regularization technique to estimate wind speed profile up to height of 100 m based on knowledge of wind speed lower heights. Results show that the proposed approach can achieve satisfactory predictions and proof the suitability of the proposed method for generating wind speed profile and probability distributions based on knowledge of wind speed at lower heights. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20regularization" title="bayesian regularization">bayesian regularization</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20shear" title=" wind shear"> wind shear</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a> </p> <a href="https://publications.waset.org/abstracts/24218/applied-bayesian-regularized-artificial-neural-network-for-up-scaling-wind-speed-profile-and-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24218.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">501</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">5388</span> A Safety Analysis Method for Multi-Agent Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ching%20Louis%20Liu">Ching Louis Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Edmund%20Kazmierczak"> Edmund Kazmierczak</a>, <a href="https://publications.waset.org/abstracts/search?q=Tim%20Miller"> Tim Miller</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Safety analysis for multi-agent systems is complicated by the, potentially nonlinear, interactions between agents. This paper proposes a method for analyzing the safety of multi-agent systems by explicitly focusing on interactions and the accident data of systems that are similar in structure and function to the system being analyzed. The method creates a Bayesian network using the accident data from similar systems. A feature of our method is that the events in accident data are labeled with HAZOP guide words. Our method uses an Ontology to abstract away from the details of a multi-agent implementation. Using the ontology, our methods then constructs an “Interaction Map,” a graphical representation of the patterns of interactions between agents and other artifacts. Interaction maps combined with statistical data from accidents and the HAZOP classifications of events can be converted into a Bayesian Network. Bayesian networks allow designers to explore “<em>what it</em>” scenarios and make design trade-offs that maintain safety. We show how to use the Bayesian networks, and the interaction maps to improve multi-agent system designs. <p class="card-text"><strong>Keywords:</strong> <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=safety%20analysis" title=" safety analysis"> safety analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=safety%20model" title=" safety model"> safety model</a>, <a href="https://publications.waset.org/abstracts/search?q=integration%20map" title=" integration map"> integration map</a> </p> <a href="https://publications.waset.org/abstracts/29024/a-safety-analysis-method-for-multi-agent-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29024.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">417</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5387</span> Modeling the Impact of Aquaculture in Wetland Ecosystems Using an Integrated Ecosystem Approach: Case Study of Setiu Wetlands, Malaysia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Roseliza%20Mat%20Alipiah">Roseliza Mat Alipiah</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Raffaelli"> David Raffaelli</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20C.%20R.%20Smart"> J. C. R. Smart</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research is a new approach as it integrates information from both environmental and social sciences to inform effective management of the wetlands. A three-stage research framework was developed for modelling the drivers and pressures imposed on the wetlands and their impacts to the ecosystem and the local communities. Firstly, a Bayesian Belief Network (BBN) was used to predict the probability of anthropogenic activities affecting the delivery of different key wetland ecosystem services under different management scenarios. Secondly, Choice Experiments (CEs) were used to quantify the relative preferences which key wetland stakeholder group (aquaculturists) held for delivery of different levels of these key ecosystem services. Thirdly, a Multi-Criteria Decision Analysis (MCDA) was applied to produce an ordinal ranking of the alternative management scenarios accounting for their impacts upon ecosystem service delivery as perceived through the preferences of the aquaculturists. This integrated ecosystem management approach was applied to a wetland ecosystem in Setiu, Terengganu, Malaysia which currently supports a significant level of aquaculture activities. This research has produced clear guidelines to inform policy makers considering alternative wetland management scenarios: Intensive Aquaculture, Conservation or Ecotourism, in addition to the Status Quo. The findings of this research are as follows: The BBN revealed that current aquaculture activity is likely to have significant impacts on water column nutrient enrichment, but trivial impacts on caged fish biomass, especially under the Intensive Aquaculture scenario. Secondly, the best fitting CE models identified several stakeholder sub-groups for aquaculturists, each with distinct sets of preferences for the delivery of key ecosystem services. Thirdly, the MCDA identified Conservation as the most desirable scenario overall based on ordinal ranking in the eyes of most of the stakeholder sub-groups. Ecotourism and Status Quo scenarios were the next most preferred and Intensive Aquaculture was the least desirable scenario. The methodologies developed through this research provide an opportunity for improving planning and decision making processes that aim to deliver sustainable management of wetland ecosystems in Malaysia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20belief%20network%20%28BBN%29" title="Bayesian belief network (BBN)">Bayesian belief network (BBN)</a>, <a href="https://publications.waset.org/abstracts/search?q=choice%20experiments%20%28CE%29" title=" choice experiments (CE)"> choice experiments (CE)</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-criteria%20decision%20analysis%20%28MCDA%29" title=" multi-criteria decision analysis (MCDA)"> multi-criteria decision analysis (MCDA)</a>, <a href="https://publications.waset.org/abstracts/search?q=aquaculture" title=" aquaculture"> aquaculture</a> </p> <a href="https://publications.waset.org/abstracts/6690/modeling-the-impact-of-aquaculture-in-wetland-ecosystems-using-an-integrated-ecosystem-approach-case-study-of-setiu-wetlands-malaysia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6690.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">294</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5386</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">5385</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">5384</span> Upgrades for Hydric Supply in Water System Distribution: Use of the Bayesian Network and Technical Expedients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elena%20Carcano">Elena Carcano</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Ball"> James Ball</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work details the strategies adopted by the Italian Water Utilities during the distribution of water in emergency conditions which glide from earthquakes and droughts to floods and fires. Several water bureaus located over the national territory have been interviewed, and the collected information has been used in a database of potential interventions to be taken. The work discusses the actions adopted by water utilities. These are generally prioritized in order to minimize the social, temporal, and economic burden that the damaged and nearby areas need to support. Actions are defined relying on the Bayesian Network Approach, which constitutes the hard core of any decision support system. The Bayesian Networks give answers to interventions to real and most likely risky cases. The added value of this research consists in supplying the National Bureau, namely Protezione Civile, in charge of managing havoc and catastrophic situations with a univocal plot outline so as to be able to handle actions uniformly at the expense of different local laws or contradictory customs which squander any recovery conditions, proper technical service, and economic aids. The paper is organized as follows: in section 1, the introduction is stated; section 2 provides a brief discussion of BNNs (Bayesian Networks), section 3 introduces the adopted methodology; and in the last sections, results are presented, and conclusions are drawn. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20process" title="hierarchical process">hierarchical process</a>, <a href="https://publications.waset.org/abstracts/search?q=strategic%20plan" title=" strategic plan"> strategic plan</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20emergency%20conditions" title=" water emergency conditions"> water emergency conditions</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20supply" title=" water supply"> water supply</a> </p> <a href="https://publications.waset.org/abstracts/150764/upgrades-for-hydric-supply-in-water-system-distribution-use-of-the-bayesian-network-and-technical-expedients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150764.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">160</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">5383</span> e-Learning Security: A Distributed Incident Response Generator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bel%20G%20Raggad">Bel G Raggad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An e-Learning setting is a distributed computing environment where information resources can be connected to any public network. Public networks are very unsecure which can compromise the reliability of an e-Learning environment. This study is only concerned with the intrusion detection aspect of e-Learning security and how incident responses are planned. The literature reported great advances in intrusion detection system (ids) but neglected to study an important ids weakness: suspected events are detected but an intrusion is not determined because it is not defined in ids databases. We propose an incident response generator (DIRG) that produces incident responses when the working ids system suspects an event that does not correspond to a known intrusion. Data involved in intrusion detection when ample uncertainty is present is often not suitable to formal statistical models including Bayesian. We instead adopt Dempster and Shafer theory to process intrusion data for the unknown event. The DIRG engine transforms data into a belief structure using incident scenarios deduced by the security administrator. Belief values associated with various incident scenarios are then derived and evaluated to choose the most appropriate scenario for which an automatic incident response is generated. This article provides a numerical example demonstrating the working of the DIRG system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20support%20system" title="decision support system">decision support system</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20computing" title=" distributed computing"> distributed computing</a>, <a href="https://publications.waset.org/abstracts/search?q=e-Learning%20security" title=" e-Learning security"> e-Learning security</a>, <a href="https://publications.waset.org/abstracts/search?q=incident%20response" title=" incident response"> incident response</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=security%20risk" title=" security risk"> security risk</a>, <a href="https://publications.waset.org/abstracts/search?q=statefull%20inspection" title=" statefull inspection"> statefull inspection</a> </p> <a href="https://publications.waset.org/abstracts/23292/e-learning-security-a-distributed-incident-response-generator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23292.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">437</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">5382</span> Probabilistic Approach to Contrast Theoretical Predictions from a Public Corruption Game Using Bayesian Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaime%20E.%20Fernandez">Jaime E. Fernandez</a>, <a href="https://publications.waset.org/abstracts/search?q=Pablo%20J.%20Valverde"> Pablo J. Valverde</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a methodological approach that aims to contrast/validate theoretical results from a corruption network game through probabilistic analysis of simulated microdata using Bayesian Networks (BNs). The research develops a public corruption model in a game theory framework. Theoretical results suggest a series of 'optimal settings' of model's exogenous parameters that boost the emergence of corruption. The paper contrasts these outcomes with probabilistic inference results based on BNs adjusted over simulated microdata. Principal findings indicate that probabilistic reasoning based on BNs significantly improves parameter specification and causal analysis in a public corruption game. <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=probabilistic%20reasoning" title=" probabilistic reasoning"> probabilistic reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20corruption" title=" public corruption"> public corruption</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20games" title=" theoretical games"> theoretical games</a> </p> <a href="https://publications.waset.org/abstracts/100412/probabilistic-approach-to-contrast-theoretical-predictions-from-a-public-corruption-game-using-bayesian-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100412.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">210</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%20belief%20network&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20belief%20network&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Bayesian%20belief%20network&page=4">4</a></li> <li class="page-item"><a class="page-link" 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