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selection inference</title> <meta name="description" content="Search results for: model selection inference"> <meta name="keywords" content="model selection inference"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="model selection inference"> <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> 18668</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: model selection inference</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18668</span> Confidence Envelopes for Parametric Model Selection Inference and Post-Model Selection Inference</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20M.%20L.%20Nadeesha%20Jayaweera">I. M. L. Nadeesha Jayaweera</a>, <a href="https://publications.waset.org/abstracts/search?q=Adao%20Alex%20Trindade"> Adao Alex Trindade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20selection%20inference" title="model selection inference">model selection inference</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20information%20criteria" title=" generalized information criteria"> generalized information criteria</a>, <a href="https://publications.waset.org/abstracts/search?q=post%20model%20selection" title=" post model selection"> post model selection</a>, <a href="https://publications.waset.org/abstracts/search?q=Asymptotic%20Theory" title=" Asymptotic Theory"> Asymptotic Theory</a> </p> <a href="https://publications.waset.org/abstracts/157622/confidence-envelopes-for-parametric-model-selection-inference-and-post-model-selection-inference" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157622.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">88</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18667</span> Fuzzy Inference System for Risk Assessment Evaluation of Wheat Flour Product Manufacturing Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yas%20Barzegaar">Yas Barzegaar</a>, <a href="https://publications.waset.org/abstracts/search?q=Atrin%20Barzegar"> Atrin Barzegar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research is to develop an intelligent system to analyze the risk level of wheat flour product manufacturing system. The model consists of five Fuzzy Inference Systems in two different layers to analyse the risk of a wheat flour product manufacturing system. The first layer of the model consists of four Fuzzy Inference Systems with three criteria. The output of each one of the Physical, Chemical, Biological and Environmental Failures will be the input of the final manufacturing systems. The proposed model based on Mamdani Fuzzy Inference Systems gives a performance ranking of wheat flour products manufacturing systems. The first step is obtaining data to identify the failure modes from expert’s opinions. The second step is the fuzzification process to convert crisp input to a fuzzy set., then the IF-then fuzzy rule applied through inference engine, and in the final step, the defuzzification process is applied to convert the fuzzy output into real numbers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=failure%20modes" title="failure modes">failure modes</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20rules" title=" fuzzy rules"> fuzzy rules</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20assessment" title=" risk assessment"> risk assessment</a> </p> <a href="https://publications.waset.org/abstracts/169565/fuzzy-inference-system-for-risk-assessment-evaluation-of-wheat-flour-product-manufacturing-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169565.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">102</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">18666</span> Fuzzy Inference System for Risk Assessment Evaluation of Wheat Flour Product Manufacturing Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atrin%20Barzegar">Atrin Barzegar</a>, <a href="https://publications.waset.org/abstracts/search?q=Yas%20Barzegar"> Yas Barzegar</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefano%20Marrone"> Stefano Marrone</a>, <a href="https://publications.waset.org/abstracts/search?q=Francesco%20Bellini"> Francesco Bellini</a>, <a href="https://publications.waset.org/abstracts/search?q=Laura%20Verde"> Laura Verde</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research is to develop an intelligent system to analyze the risk level of wheat flour product manufacturing system. The model consists of five Fuzzy Inference Systems in two different layers to analyse the risk of a wheat flour product manufacturing system. The first layer of the model consists of four Fuzzy Inference Systems with three criteria. The output of each one of the Physical, Chemical, Biological and Environmental Failures will be the input of the final manufacturing systems. The proposed model based on Mamdani Fuzzy Inference Systems gives a performance ranking of wheat flour products manufacturing systems. The first step is obtaining data to identify the failure modes from expert’s opinions. The second step is the fuzzification process to convert crisp input to a fuzzy set., then the IF-then fuzzy rule applied through inference engine, and in the final step, the defuzzification process is applied to convert the fuzzy output into real numbers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=failure%20modes" title="failure modes">failure modes</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20rules" title=" fuzzy rules"> fuzzy rules</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20assessment" title=" risk assessment"> risk assessment</a> </p> <a href="https://publications.waset.org/abstracts/170997/fuzzy-inference-system-for-risk-assessment-evaluation-of-wheat-flour-product-manufacturing-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170997.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18665</span> Syllogistic Reasoning with 108 Inference Rules While Case Quantities Change</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mikhail%20Zarechnev">Mikhail Zarechnev</a>, <a href="https://publications.waset.org/abstracts/search?q=Bora%20I.%20Kumova"> Bora I. Kumova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A syllogism is a deductive inference scheme used to derive a conclusion from a set of premises. In a categorical syllogisms, there are only two premises and every premise and conclusion is given in form of a quantified relationship between two objects. The different order of objects in premises give classification known as figures. We have shown that the ordered combinations of 3 generalized quantifiers with certain figure provide in total of 108 syllogistic moods which can be considered as different inference rules. The classical syllogistic system allows to model human thought and reasoning with syllogistic structures always attracted the attention of cognitive scientists. Since automated reasoning is considered as part of learning subsystem of AI agents, syllogistic system can be applied for this approach. Another application of syllogistic system is related to inference mechanisms on the Semantic Web applications. In this paper we proposed the mathematical model and algorithm for syllogistic reasoning. Also the model of iterative syllogistic reasoning in case of continuous flows of incoming data based on case–based reasoning and possible applications of proposed system were discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=categorical%20syllogism" title="categorical syllogism">categorical syllogism</a>, <a href="https://publications.waset.org/abstracts/search?q=case-based%20reasoning" title=" case-based reasoning"> case-based reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20architecture" title=" cognitive architecture"> cognitive architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=inference%20on%20the%20semantic%20web" title=" inference on the semantic web"> inference on the semantic web</a>, <a href="https://publications.waset.org/abstracts/search?q=syllogistic%20reasoning" title=" syllogistic reasoning"> syllogistic reasoning</a> </p> <a href="https://publications.waset.org/abstracts/24127/syllogistic-reasoning-with-108-inference-rules-while-case-quantities-change" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24127.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">411</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">18664</span> A Model of Empowerment Evaluation of Knowledge Management in Private Banks Using Fuzzy Inference System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nazanin%20Pilevari">Nazanin Pilevari</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamyar%20Mahmoodi"> Kamyar Mahmoodi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this research is to provide a model based on fuzzy inference system for evaluating empowerment of Knowledge management. The first prototype of the research was developed based on the study of literature. In the next step, experts were provided with these models and after implementing consensus-based reform, the views of Fuzzy Delphi experts and techniques, components and Index research model were finalized. Culture, structure, IT and leadership were considered as dimensions of empowerment. Then, In order to collect and extract data for fuzzy inference system based on knowledge and Experience, the experts were interviewed. The values obtained from designed fuzzy inference system, made review and assessment of the organization's empowerment of Knowledge management possible. After the design and validation of systems to measure indexes ,empowerment of Knowledge management and inputs into fuzzy inference) in the AYANDEH Bank, a questionnaire was used. In the case of this bank, the system output indicates that the status of empowerment of Knowledge management, culture, organizational structure and leadership are at the moderate level and information technology empowerment are relatively high. Based on these results, the status of knowledge management empowerment in AYANDE Bank, was moderate. Eventually, some suggestions for improving the current situation of banks were provided. According to studies of research history, the use of powerful tools in Fuzzy Inference System for assessment of Knowledge management and knowledge management empowerment such an assessment in the field of banking, are the innovation of this Research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title="knowledge management">knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management%20empowerment" title=" knowledge management empowerment"> knowledge management empowerment</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20Delphi" title=" fuzzy Delphi"> fuzzy Delphi</a> </p> <a href="https://publications.waset.org/abstracts/72815/a-model-of-empowerment-evaluation-of-knowledge-management-in-private-banks-using-fuzzy-inference-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72815.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">358</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">18663</span> Optimal Selection of Replenishment Policies Using Distance Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20Gupta">Amit Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepak%20Juneja"> Deepak Juneja</a>, <a href="https://publications.waset.org/abstracts/search?q=Sorabh%20Gupta"> Sorabh Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a model based on distance based approach (DBA) method employed for evaluation, selection, and ranking of replenishment policies for a single location inventory, which hitherto not developed in the literature. This work recognizes the significance of the selection problem, identifies the selection criteria, the relative importance of selection criteria for this research problem. The developed model is capable of comparing any number of alternate inventory policies for various selection criteria where cardinal values are assigned as a rating to alternate inventory polices for selection criteria and weights of selection criteria. The illustrated example demonstrates the model and presents the result in terms of ranking of replenishment policies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DBA" title="DBA">DBA</a>, <a href="https://publications.waset.org/abstracts/search?q=ranking" title=" ranking"> ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=replenishment%20policies" title=" replenishment policies"> replenishment policies</a>, <a href="https://publications.waset.org/abstracts/search?q=selection%20criteria" title=" selection criteria"> selection criteria</a> </p> <a href="https://publications.waset.org/abstracts/116031/optimal-selection-of-replenishment-policies-using-distance-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116031.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">157</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">18662</span> A Bayesian Model with Improved Prior in Extreme Value Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eva%20L.%20Sanju%C3%A1n">Eva L. Sanjuán</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacinto%20Mart%C3%ADn"> Jacinto Martín</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Isabel%20Parra"> M. Isabel Parra</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20M.%20Pizarro"> Mario M. Pizarro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Extreme Value Theory, inference estimation for the parameters of the distribution is made employing a small part of the observation values. When block maxima values are taken, many data are discarded. We developed a new Bayesian inference model to seize all the information provided by the data, introducing informative priors and using the relations between baseline and limit parameters. Firstly, we studied the accuracy of the new model for three baseline distributions that lead to a Gumbel extreme distribution: Exponential, Normal and Gumbel. Secondly, we considered mixtures of Normal variables, to simulate practical situations when data do not adjust to pure distributions, because of perturbations (noise). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20inference" title="bayesian inference">bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=Gumbel%20distribution" title=" Gumbel distribution"> Gumbel distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=highly%20informative%20prior" title=" highly informative prior"> highly informative prior</a> </p> <a href="https://publications.waset.org/abstracts/141776/a-bayesian-model-with-improved-prior-in-extreme-value-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141776.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">198</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18661</span> The Effects of the Inference Process in Reading Texts in Arabic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=May%20George">May George</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Inference plays an important role in the learning process and it can lead to a rapid acquisition of a second language. When learning a non-native language, i.e., a critical language like Arabic, the students depend on the teacher’s support most of the time to learn new concepts. The students focus on memorizing the new vocabulary and stress on learning all the grammatical rules. Hence, the students became mechanical and cannot produce the language easily. As a result, they are unable to predict the meaning of words in the context by relying heavily on the teacher, in that they cannot link their prior knowledge or even identify the meaning of the words without the support of the teacher. This study explores how the teacher guides students learning during the inference process and what are the processes of learning that can direct student’s inference. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inference" title="inference">inference</a>, <a href="https://publications.waset.org/abstracts/search?q=reading" title=" reading"> reading</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20acquisition" title=" language acquisition "> language acquisition </a> </p> <a href="https://publications.waset.org/abstracts/35360/the-effects-of-the-inference-process-in-reading-texts-in-arabic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35360.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">531</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">18660</span> Integrated Nested Laplace Approximations For Quantile Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kajingulu%20Malandala">Kajingulu Malandala</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranganai%20Edmore"> Ranganai Edmore</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The asymmetric Laplace distribution (ADL) is commonly used as the likelihood function of the Bayesian quantile regression, and it offers different families of likelihood method for quantile regression. Notwithstanding their popularity and practicality, ADL is not smooth and thus making it difficult to maximize its likelihood. Furthermore, Bayesian inference is time consuming and the selection of likelihood may mislead the inference, as the Bayes theorem does not automatically establish the posterior inference. Furthermore, ADL does not account for greater skewness and Kurtosis. This paper develops a new aspect of quantile regression approach for count data based on inverse of the cumulative density function of the Poisson, binomial and Delaporte distributions using the integrated nested Laplace Approximations. Our result validates the benefit of using the integrated nested Laplace Approximations and support the approach for count data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title="quantile regression">quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Delaporte%20distribution" title=" Delaporte distribution"> Delaporte distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=count%20data" title=" count data"> count data</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20nested%20Laplace%20approximation" title=" integrated nested Laplace approximation"> integrated nested Laplace approximation</a> </p> <a href="https://publications.waset.org/abstracts/123306/integrated-nested-laplace-approximations-for-quantile-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123306.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">163</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">18659</span> LaPEA: Language for Preprocessing of Edge Applications in Smart Factory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masaki%20Sakai">Masaki Sakai</a>, <a href="https://publications.waset.org/abstracts/search?q=Tsuyoshi%20Nakajima"> Tsuyoshi Nakajima</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazuya%20Takahashi"> Kazuya Takahashi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to improve the productivity of a factory, it is often the case to create an inference model by collecting and analyzing operational data off-line and then to develop an edge application (EAP) that evaluates the quality of the products or diagnoses machine faults in real-time. To accelerate this development cycle, an edge application framework for the smart factory is proposed, which enables to create and modify EAPs based on prepared inference models. In the framework, the preprocessing component is the key part to make it work. This paper proposes a language for preprocessing of edge applications, called LaPEA, which can flexibly process several sensor data from machines into explanatory variables for an inference model, and proves that it meets the requirements for the preprocessing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20application%20framework" title="edge application framework">edge application framework</a>, <a href="https://publications.waset.org/abstracts/search?q=edgecross" title=" edgecross"> edgecross</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing%20language" title=" preprocessing language"> preprocessing language</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20factory" title=" smart factory"> smart factory</a> </p> <a href="https://publications.waset.org/abstracts/142882/lapea-language-for-preprocessing-of-edge-applications-in-smart-factory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142882.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">146</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">18658</span> Applying of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Estimation of Flood Hydrographs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Ahmad%20Dehghani">Amir Ahmad Dehghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Morteza%20Nabizadeh"> Morteza Nabizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to flood hydrograph modeling of Shahid Rajaee reservoir dam located in Iran. This was carried out using 11 flood hydrographs recorded in Tajan river gauging station. From this dataset, 9 flood hydrographs were chosen to train the model and 2 flood hydrographs to test the model. The different architectures of neuro-fuzzy model according to the membership function and learning algorithm were designed and trained with different epochs. The results were evaluated in comparison with the observed hydrographs and the best structure of model was chosen according the least RMSE in each performance. To evaluate the efficiency of neuro-fuzzy model, various statistical indices such as Nash-Sutcliff and flood peak discharge error criteria were calculated. In this simulation, the coordinates of a flood hydrograph including peak discharge were estimated using the discharge values occurred in the earlier time steps as input values to the neuro-fuzzy model. These results indicate the satisfactory efficiency of neuro-fuzzy model for flood simulating. This performance of the model demonstrates the suitability of the implemented approach to flood management projects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system" title="adaptive neuro-fuzzy inference system">adaptive neuro-fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=flood%20hydrograph" title=" flood hydrograph"> flood hydrograph</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20learning%20algorithm" title=" hybrid learning algorithm"> hybrid learning algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahid%20Rajaee%20reservoir%20dam" title=" Shahid Rajaee reservoir dam"> Shahid Rajaee reservoir dam</a> </p> <a href="https://publications.waset.org/abstracts/13913/applying-of-an-adaptive-neuro-fuzzy-inference-system-anfis-for-estimation-of-flood-hydrographs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13913.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">478</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">18657</span> Drinking Water Quality Assessment Using Fuzzy Inference System Method: A Case Study of Rome, Italy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yas%20Barzegar">Yas Barzegar</a>, <a href="https://publications.waset.org/abstracts/search?q=Atrin%20Barzegar"> Atrin Barzegar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drinking water quality assessment is a major issue today; technology and practices are continuously improving; Artificial Intelligence (AI) methods prove their efficiency in this domain. The current research seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system (FIS) is applied with different defuzzification methods. The Proposed Model includes three fuzzy intermediate models and one fuzzy final model. Each fuzzy model consists of three input parameters and 27 fuzzy rules. The model is developed for water quality assessment with a dataset considering nine parameters (Alkalinity, Hardness, pH, Ca, Mg, Fluoride, Sulphate, Nitrates, and Iron). Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; it is an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The FIS method can provide an effective solution to complex systems; this method can be modified easily to improve performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=water%20quality" title="water quality">water quality</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20cities" title=" smart cities"> smart cities</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20attribute" title=" water attribute"> water attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20function" title=" membership function"> membership function</a> </p> <a href="https://publications.waset.org/abstracts/170172/drinking-water-quality-assessment-using-fuzzy-inference-system-method-a-case-study-of-rome-italy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170172.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18656</span> Fuzzy Inference-Assisted Saliency-Aware Convolution Neural Networks for Multi-View Summarization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tanveer%20Hussain">Tanveer Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Khan%20Muhammad"> Khan Muhammad</a>, <a href="https://publications.waset.org/abstracts/search?q=Amin%20Ullah"> Amin Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mi%20Young%20Lee"> Mi Young Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung%20Wook%20Baik"> Sung Wook Baik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Big Data generated from distributed vision sensors installed on large scale in smart cities create hurdles in its efficient and beneficial exploration for browsing, retrieval, and indexing. This paper presents a three-folded framework for effective video summarization of such data and provide a compact and representative format of Big Video Data. In the first fold, the paper acquires input video data from the installed cameras and collect clues such as type and count of objects and clarity of the view from a chunk of pre-defined number of frames of each view. The decision of representative view selection for a particular interval is based on fuzzy inference system, acquiring a precise and human resembling decision, reinforced by the known clues as a part of the second fold. In the third fold, the paper forwards the selected view frames to the summary generation mechanism that is supported by a saliency-aware convolution neural network (CNN) model. The new trend of fuzzy rules for view selection followed by CNN architecture for saliency computation makes the multi-view video summarization (MVS) framework a suitable candidate for real-world practice in smart cities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20video%20data%20analysis" title="big video data analysis">big video data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-view%20video%20summarization" title=" multi-view video summarization"> multi-view video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=saliency%20detection" title=" saliency detection"> saliency detection</a> </p> <a href="https://publications.waset.org/abstracts/135176/fuzzy-inference-assisted-saliency-aware-convolution-neural-networks-for-multi-view-summarization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135176.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">188</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">18655</span> A Case-Based Reasoning-Decision Tree Hybrid System for Stock Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yaojun%20Wang">Yaojun Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yaoqing%20Wang"> Yaoqing Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=case-based%20reasoning" title="case-based reasoning">case-based reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20selection" title=" stock selection"> stock selection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/48974/a-case-based-reasoning-decision-tree-hybrid-system-for-stock-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48974.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">419</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">18654</span> Research on Knowledge Graph Inference Technology Based on Proximal Policy Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yihao%20Kuang">Yihao Kuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Bowen%20Ding"> Bowen Ding</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=PPO" title=" PPO"> PPO</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20inference" title=" knowledge inference"> knowledge inference</a> </p> <a href="https://publications.waset.org/abstracts/168844/research-on-knowledge-graph-inference-technology-based-on-proximal-policy-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168844.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">242</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">18653</span> Research on Knowledge Graph Inference Technology Based on Proximal Policy Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yihao%20Kuang">Yihao Kuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Bowen%20Ding"> Bowen Ding</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=PPO" title=" PPO"> PPO</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20inference" title=" knowledge inference"> knowledge inference</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a> </p> <a href="https://publications.waset.org/abstracts/173972/research-on-knowledge-graph-inference-technology-based-on-proximal-policy-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173972.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">67</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">18652</span> Recommendation Systems for Cereal Cultivation using Advanced Casual Inference Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md%20Yeasin">Md Yeasin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranjit%20Kumar%20Paul"> Ranjit Kumar Paul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, recommendation systems have become indispensable tools for agricultural system. The accurate and timely recommendations can significantly impact crop yield and overall productivity. Causal inference modeling aims to establish cause-and-effect relationships by identifying the impact of variables or factors on outcomes, enabling more accurate and reliable recommendations. New advancements in causal inference models have been found in the literature. With the advent of the modern era, deep learning and machine learning models have emerged as efficient tools for modeling. This study proposed an innovative approach to enhance recommendation systems-based machine learning based casual inference model. By considering the causal effect and opportunity cost of covariates, the proposed system can provide more reliable and actionable recommendations for cereal farmers. To validate the effectiveness of the proposed approach, experiments are conducted using cereal cultivation data of eastern India. Comparative evaluations are performed against existing correlation-based recommendation systems, demonstrating the superiority of the advanced causal inference modeling approach in terms of recommendation accuracy and impact on crop yield. Overall, it empowers farmers with personalized recommendations tailored to their specific circumstances, leading to optimized decision-making and increased crop productivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agriculture" title="agriculture">agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=casual%20inference" title=" casual inference"> casual inference</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=recommendation%20system" title=" recommendation system"> recommendation system</a> </p> <a href="https://publications.waset.org/abstracts/169691/recommendation-systems-for-cereal-cultivation-using-advanced-casual-inference-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169691.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">79</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">18651</span> Variational Explanation Generator: Generating Explanation for Natural Language Inference Using Variational Auto-Encoder</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhen%20Cheng">Zhen Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinyu%20Dai"> Xinyu Dai</a>, <a href="https://publications.waset.org/abstracts/search?q=Shujian%20Huang"> Shujian Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiajun%20Chen"> Jiajun Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, explanatory natural language inference has attracted much attention for the interpretability of logic relationship prediction, which is also known as explanation generation for Natural Language Inference (NLI). Existing explanation generators based on discriminative Encoder-Decoder architecture have achieved noticeable results. However, we find that these discriminative generators usually generate explanations with correct evidence but incorrect logic semantic. It is due to that logic information is implicitly encoded in the premise-hypothesis pairs and difficult to model. Actually, logic information identically exists between premise-hypothesis pair and explanation. And it is easy to extract logic information that is explicitly contained in the target explanation. Hence we assume that there exists a latent space of logic information while generating explanations. Specifically, we propose a generative model called Variational Explanation Generator (VariationalEG) with a latent variable to model this space. Training with the guide of explicit logic information in target explanations, latent variable in VariationalEG could capture the implicit logic information in premise-hypothesis pairs effectively. Additionally, to tackle the problem of posterior collapse while training VariaztionalEG, we propose a simple yet effective approach called Logic Supervision on the latent variable to force it to encode logic information. Experiments on explanation generation benchmark—explanation-Stanford Natural Language Inference (e-SNLI) demonstrate that the proposed VariationalEG achieves significant improvement compared to previous studies and yields a state-of-the-art result. Furthermore, we perform the analysis of generated explanations to demonstrate the effect of the latent variable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20inference" title="natural language inference">natural language inference</a>, <a href="https://publications.waset.org/abstracts/search?q=explanation%20generation" title=" explanation generation"> explanation generation</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20auto-encoder" title=" variational auto-encoder"> variational auto-encoder</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20model" title=" generative model"> generative model</a> </p> <a href="https://publications.waset.org/abstracts/126633/variational-explanation-generator-generating-explanation-for-natural-language-inference-using-variational-auto-encoder" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126633.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">151</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">18650</span> Application of Causal Inference and Discovery in Curriculum Evaluation and Continuous Improvement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lunliang%20Zhong">Lunliang Zhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Bin%20Duan"> Bin Duan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The undergraduate graduation project is a vital part of the higher education curriculum, crucial for engineering accreditation. Current evaluations often summarize data without identifying underlying issues. This study applies the Peter-Clark algorithm to analyze causal relationships within the graduation project data of an Electronics and Information Engineering program, creating a causal model. Structural equation modeling confirmed the model's validity. The analysis reveals key teaching stages affecting project success, uncovering problems in the process. Introducing causal discovery and inference into project evaluation helps identify issues and propose targeted improvement measures. The effectiveness of these measures is validated by comparing the learning outcomes of two student cohorts, stratified by confounding factors, leading to improved teaching quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=causal%20discovery" title="causal discovery">causal discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=causal%20inference" title=" causal inference"> causal inference</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20improvement" title=" continuous improvement"> continuous improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter-Clark%20algorithm" title=" Peter-Clark algorithm"> Peter-Clark algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20equation%20modeling" title=" structural equation modeling"> structural equation modeling</a> </p> <a href="https://publications.waset.org/abstracts/191014/application-of-causal-inference-and-discovery-in-curriculum-evaluation-and-continuous-improvement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191014.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">18</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">18649</span> Fuzzy Inference System for Diagnosis of Malaria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Purnima%20Pandit">Purnima Pandit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malaria remains one of the world’s most deadly infectious disease and arguably, the greatest menace to modern society in terms of morbidity and mortality. To choose the right treatment and to ensure a quality of life suitable for a specific patient condition, early and accurate diagnosis of malaria is essential. It reduces transmission of disease and prevents deaths. Our work focuses on designing an efficient, accurate fuzzy inference system for malaria diagnosis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title="fuzzy inference system">fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=malaria%20disease" title=" malaria disease"> malaria disease</a>, <a href="https://publications.waset.org/abstracts/search?q=triangular%20fuzzy%20number" title=" triangular fuzzy number"> triangular fuzzy number</a> </p> <a href="https://publications.waset.org/abstracts/55107/fuzzy-inference-system-for-diagnosis-of-malaria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55107.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">297</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">18648</span> Bayesian Parameter Inference for Continuous Time Markov Chains with Intractable Likelihood</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Randa%20Alharbi">Randa Alharbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladislav%20Vyshemirsky"> Vladislav Vyshemirsky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Approximate%20Bayesian%20computation%28ABC%29" title="Approximate Bayesian computation(ABC)">Approximate Bayesian computation(ABC)</a>, <a href="https://publications.waset.org/abstracts/search?q=Continuous-Time%20Markov%20Chains" title=" Continuous-Time Markov Chains"> Continuous-Time Markov Chains</a>, <a href="https://publications.waset.org/abstracts/search?q=Sequential%20Monte%20Carlo" title=" Sequential Monte Carlo"> Sequential Monte Carlo</a>, <a href="https://publications.waset.org/abstracts/search?q=Particle%20Markov%20chain%20Monte%20Carlo%20%28PMCMC%29" title=" Particle Markov chain Monte Carlo (PMCMC)"> Particle Markov chain Monte Carlo (PMCMC)</a> </p> <a href="https://publications.waset.org/abstracts/82129/bayesian-parameter-inference-for-continuous-time-markov-chains-with-intractable-likelihood" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82129.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">202</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">18647</span> An Adaptive Hybrid Surrogate-Assisted Particle Swarm Optimization Algorithm for Expensive Structural Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiongxiong%20You">Xiongxiong You</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhanwen%20Niu"> Zhanwen Niu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Choosing an appropriate surrogate model plays an important role in surrogates-assisted evolutionary algorithms (SAEAs) since there are many types and different kernel functions in the surrogate model. In this paper, an adaptive selection of the best suitable surrogate model method is proposed to solve different kinds of expensive optimization problems. Firstly, according to the prediction residual error sum of square (PRESS) and different model selection strategies, the excellent individual surrogate models are integrated into multiple ensemble models in each generation. Then, based on the minimum root of mean square error (RMSE), the best suitable surrogate model is selected dynamically. Secondly, two methods with dynamic number of models and selection strategies are designed, which are used to show the influence of the number of individual models and selection strategy. Finally, some compared studies are made to deal with several commonly used benchmark problems, as well as a rotor system optimization problem. The results demonstrate the accuracy and robustness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20selection" title="adaptive selection">adaptive selection</a>, <a href="https://publications.waset.org/abstracts/search?q=expensive%20optimization" title=" expensive optimization"> expensive optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=rotor%20system" title=" rotor system"> rotor system</a>, <a href="https://publications.waset.org/abstracts/search?q=surrogates%20assisted%20evolutionary%20algorithms" title=" surrogates assisted evolutionary algorithms"> surrogates assisted evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/137516/an-adaptive-hybrid-surrogate-assisted-particle-swarm-optimization-algorithm-for-expensive-structural-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137516.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">141</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">18646</span> Recruitment Model (FSRM) for Faculty Selection Based on Fuzzy Soft</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20S.%20Thakur">G. S. Thakur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a Fuzzy Soft Recruitment Model (FSRM) for faculty selection of MHRD technical institutions. The selection criteria are based on 4-tier flexible structure in the institutions. The Advisory Committee on Faculty Recruitment (ACoFAR) suggested nine criteria for faculty in the proposed FSRM. The model Fuzzy Soft is proposed with consultation of ACoFAR based on selection criteria. The Fuzzy Soft distance similarity measures are applied for finding best faculty from the applicant pool. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20soft%20set" title="fuzzy soft set">fuzzy soft set</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sets" title=" fuzzy sets"> fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20soft%20distance" title=" fuzzy soft distance"> fuzzy soft distance</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20soft%20similarity%20measures" title=" fuzzy soft similarity measures"> fuzzy soft similarity measures</a>, <a href="https://publications.waset.org/abstracts/search?q=ACoFAR" title=" ACoFAR"> ACoFAR</a> </p> <a href="https://publications.waset.org/abstracts/12838/recruitment-model-fsrm-for-faculty-selection-based-on-fuzzy-soft" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12838.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">347</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">18645</span> Risk Assessment of Natural Gas Pipelines in Coal Mined Gobs Based on Bow-Tie Model and Cloud Inference</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaobin%20Liang">Xiaobin Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Liang"> Wei Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Laibin%20Zhang"> Laibin Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoyan%20Guo"> Xiaoyan Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pipelines pass through coal mined gobs inevitably in the mining area, the stability of which has great influence on the safety of pipelines. After extensive literature study and field research, it was found that there are a few risk assessment methods for coal mined gob pipelines, and there is a lack of data on the gob sites. Therefore, the fuzzy comprehensive evaluation method is widely used based on expert opinions. However, the subjective opinions or lack of experience of individual experts may lead to inaccurate evaluation results. Hence the accuracy of the results needs to be further improved. This paper presents a comprehensive approach to achieve this purpose by combining bow-tie model and cloud inference. The specific evaluation process is as follows: First, a bow-tie model composed of a fault tree and an event tree is established to graphically illustrate the probability and consequence indicators of pipeline failure. Second, the interval estimation method can be scored in the form of intervals to improve the accuracy of the results, and the censored mean algorithm is used to remove the maximum and minimum values of the score to improve the stability of the results. The golden section method is used to determine the weight of the indicators and reduce the subjectivity of index weights. Third, the failure probability and failure consequence scores of the pipeline are converted into three numerical features by using cloud inference. The cloud inference can better describe the ambiguity and volatility of the results which can better describe the volatility of the risk level. Finally, the cloud drop graphs of failure probability and failure consequences can be expressed, which intuitively and accurately illustrate the ambiguity and randomness of the results. A case study of a coal mine gob pipeline carrying natural gas has been investigated to validate the utility of the proposed method. The evaluation results of this case show that the probability of failure of the pipeline is very low, the consequences of failure are more serious, which is consistent with the reality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bow-tie%20model" title="bow-tie model">bow-tie model</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20gas%20pipeline" title=" natural gas pipeline"> natural gas pipeline</a>, <a href="https://publications.waset.org/abstracts/search?q=coal%20mine%20gob" title=" coal mine gob"> coal mine gob</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20inference" title=" cloud inference"> cloud inference</a> </p> <a href="https://publications.waset.org/abstracts/98476/risk-assessment-of-natural-gas-pipelines-in-coal-mined-gobs-based-on-bow-tie-model-and-cloud-inference" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98476.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">250</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">18644</span> Application of Neuro-Fuzzy Technique for Optimizing the PVC Membrane Sensor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Rezayi">Majid Rezayi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sh.%20Shahaboddin"> Sh. Shahaboddin</a>, <a href="https://publications.waset.org/abstracts/search?q=HNM%20E.%20Mahmud"> HNM E. Mahmud</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Yadollah"> A. Yadollah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Saeid"> A. Saeid</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Yatimah"> A. Yatimah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the adaptive neuro-fuzzy inference system (ANFIS) was applied to obtain the membrane composition model affecting the potential response of our reported polymeric PVC sensor for determining the titanium (III) ions. The performance statistics of the artificial neural network (ANN) and linear regression models for potential slope prediction of membrane composition of titanium (III) ion selective electrode were compared with ANFIS technique. The results show that the ANFIS model can be used as a practical tool for obtaining the Nerntian slope of the proposed sensor in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro%20fuzzy%20inference" title="adaptive neuro fuzzy inference">adaptive neuro fuzzy inference</a>, <a href="https://publications.waset.org/abstracts/search?q=PVC%20sensor" title=" PVC sensor"> PVC sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=titanium%20%28III%29%20ions" title=" titanium (III) ions"> titanium (III) ions</a>, <a href="https://publications.waset.org/abstracts/search?q=Nerntian%20slope" title=" Nerntian slope"> Nerntian slope</a> </p> <a href="https://publications.waset.org/abstracts/57746/application-of-neuro-fuzzy-technique-for-optimizing-the-pvc-membrane-sensor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57746.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">287</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18643</span> An Adjusted Network Information Criterion for Model Selection in Statistical Neural Network Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christopher%20Godwin%20Udomboso">Christopher Godwin Udomboso</a>, <a href="https://publications.waset.org/abstracts/search?q=Angela%20Unna%20Chukwu"> Angela Unna Chukwu</a>, <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Kwame%20Dontwi"> Isaac Kwame Dontwi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In selecting a Statistical Neural Network model, the Network Information Criterion (NIC) has been observed to be sample biased, because it does not account for sample sizes. The selection of a model from a set of fitted candidate models requires objective data-driven criteria. In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC), based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The analyses show that on a general note, the ANIC improves model selection in more sample sizes than does the NIC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20neural%20network" title="statistical neural network">statistical neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20information%20criterion" title=" network information criterion"> network information criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=adjusted%20network" title=" adjusted network"> adjusted network</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20criterion" title=" information criterion"> information criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20function" title=" transfer function"> transfer function</a> </p> <a href="https://publications.waset.org/abstracts/28771/an-adjusted-network-information-criterion-for-model-selection-in-statistical-neural-network-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28771.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">566</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">18642</span> Prediction Compressive Strength of Self-Compacting Concrete Containing Fly Ash Using Fuzzy Logic Inference System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belalia%20Douma%20Omar">Belalia Douma Omar</a>, <a href="https://publications.waset.org/abstracts/search?q=Bakhta%20Boukhatem"> Bakhta Boukhatem</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ghrici"> Mohamed Ghrici</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Self-compacting concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work condition and also reduce the impact of environment by elimination of the need for compaction. Fuzzy logic (FL) approaches has recently been used to model some of the human activities in many areas of civil engineering applications. Especially from these systems in the model experimental studies, very good results have been obtained. In the present study, a model for predicting compressive strength of SCC containing various proportions of fly ash, as partial replacement of cement has been developed by using Adaptive Neuro-Fuzzy Inference System (ANFIS). For the purpose of building this model, a database of experimental data were gathered from the literature and used for training and testing the model. The used data as the inputs of fuzzy logic models are arranged in a format of five parameters that cover the total binder content, fly ash replacement percentage, water content, super plasticizer and age of specimens. The training and testing results in the fuzzy logic model have shown a strong potential for predicting the compressive strength of SCC containing fly ash in the considered range. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-compacting%20concrete" title="self-compacting concrete">self-compacting concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=fly%20ash" title=" fly ash"> fly ash</a>, <a href="https://publications.waset.org/abstracts/search?q=strength%20prediction" title=" strength prediction"> strength prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic "> fuzzy logic </a> </p> <a href="https://publications.waset.org/abstracts/17337/prediction-compressive-strength-of-self-compacting-concrete-containing-fly-ash-using-fuzzy-logic-inference-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17337.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">335</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18641</span> Determinants of Self-Reported Hunger: An Ordered Probit Model with Sample Selection Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brian%20W.%20Mandikiana">Brian W. Mandikiana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Homestead food production has the potential to alleviate hunger, improve health and nutrition for children and adults. This article examines the relationship between self-reported hunger and homestead food production using the ordered probit model. A sample of households participating in homestead food production was drawn from the first wave of the South African National Income Dynamics Survey, a nationally representative cross-section. The sample selection problem was corrected using an ordered probit model with sample selection approach. The findings show that homestead food production exerts a positive and significant impact on children and adults’ ability to cope with hunger and malnutrition. Yet, on the contrary, potential gains of homestead food production are threatened by shocks such as crop failure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agriculture" title="agriculture">agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=hunger" title=" hunger"> hunger</a>, <a href="https://publications.waset.org/abstracts/search?q=nutrition" title=" nutrition"> nutrition</a>, <a href="https://publications.waset.org/abstracts/search?q=sample%20selection" title=" sample selection"> sample selection</a> </p> <a href="https://publications.waset.org/abstracts/47090/determinants-of-self-reported-hunger-an-ordered-probit-model-with-sample-selection-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47090.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">18640</span> Failure Inference and Optimization for Step Stress Model Based on Bivariate Wiener Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soudabeh%20Shemehsavar">Soudabeh Shemehsavar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider the situation under a life test, in which the failure time of the test units are not related deterministically to an observable stochastic time varying covariate. In such a case, the joint distribution of failure time and a marker value would be useful for modeling the step stress life test. The problem of accelerating such an experiment is considered as the main aim of this paper. We present a step stress accelerated model based on a bivariate Wiener process with one component as the latent (unobservable) degradation process, which determines the failure times and the other as a marker process, the degradation values of which are recorded at times of failure. Parametric inference based on the proposed model is discussed and the optimization procedure for obtaining the optimal time for changing the stress level is presented. The optimization criterion is to minimize the approximate variance of the maximum likelihood estimator of a percentile of the products’ lifetime distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bivariate%20normal" title="bivariate normal">bivariate normal</a>, <a href="https://publications.waset.org/abstracts/search?q=Fisher%20information%20matrix" title=" Fisher information matrix"> Fisher information matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20Gaussian%20distribution" title=" inverse Gaussian distribution"> inverse Gaussian distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Wiener%20process" title=" Wiener process"> Wiener process</a> </p> <a href="https://publications.waset.org/abstracts/4424/failure-inference-and-optimization-for-step-stress-model-based-on-bivariate-wiener-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4424.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">317</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">18639</span> Application of Adaptive Neuro Fuzzy Inference Systems Technique for Modeling of Postweld Heat Treatment Process of Pressure Vessel Steel AASTM A516 Grade 70</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Omar%20Al%20Denali">Omar Al Denali</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelaziz%20Badi"> Abdelaziz Badi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ASTM A516 Grade 70 steel is a suitable material used for the fabrication of boiler pressure vessels working in moderate and lower temperature services, and it has good weldability and excellent notch toughness. The post-weld heat treatment (PWHT) or stress-relieving heat treatment has significant effects on avoiding the martensite transformation and resulting in high hardness, which can lead to cracking in the heat-affected zone (HAZ). An adaptive neuro-fuzzy inference system (ANFIS) was implemented to predict the material tensile strength of post-weld heat treatment (PWHT) experiments. The ANFIS models presented excellent predictions, and the comparison was carried out based on the mean absolute percentage error between the predicted values and the experimental values. The ANFIS model gave a Mean Absolute Percentage Error of 0.556 %, which confirms the high accuracy of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=post-weld%20heat%20treatment" title=" post-weld heat treatment"> post-weld heat treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system" title=" adaptive neuro-fuzzy inference system"> adaptive neuro-fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20percentage%20error" title=" mean absolute percentage error"> mean absolute percentage error</a> </p> <a href="https://publications.waset.org/abstracts/148849/application-of-adaptive-neuro-fuzzy-inference-systems-technique-for-modeling-of-postweld-heat-treatment-process-of-pressure-vessel-steel-aastm-a516-grade-70" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148849.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">153</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=model%20selection%20inference&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=model%20selection%20inference&page=3">3</a></li> <li class="page-item"><a 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