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Search results for: logistic model

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text-center" style="font-size:1.6rem;">Search results for: logistic model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17348</span> Logistic Regression Model versus Additive Model for Recurrent Event Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Entisar%20A.%20Elgmati">Entisar A. Elgmati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recurrent infant diarrhea is studied using daily data collected in Salvador, Brazil over one year and three months. A logistic regression model is fitted instead of Aalen's additive model using the same covariates that were used in the analysis with the additive model. The model gives reasonably similar results to that using additive regression model. In addition, the problem with the estimated conditional probabilities not being constrained between zero and one in additive model is solved here. Also martingale residuals that have been used to judge the goodness of fit for the additive model are shown to be useful for judging the goodness of fit of the logistic model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=additive%20model" title="additive model">additive model</a>, <a href="https://publications.waset.org/abstracts/search?q=cumulative%20probabilities" title=" cumulative probabilities"> cumulative probabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=infant%20diarrhoea" title=" infant diarrhoea"> infant diarrhoea</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20event" title=" recurrent event"> recurrent event</a> </p> <a href="https://publications.waset.org/abstracts/27829/logistic-regression-model-versus-additive-model-for-recurrent-event-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27829.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">635</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">17347</span> Application Difference between Cox and Logistic Regression Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Idrissa%20Kayijuka">Idrissa Kayijuka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression%20model" title="logistic regression model">logistic regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=Cox%20regression%20model" title=" Cox regression model"> Cox regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20analysis" title=" survival analysis"> survival analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard%20ratio" title=" hazard ratio"> hazard ratio</a> </p> <a href="https://publications.waset.org/abstracts/66111/application-difference-between-cox-and-logistic-regression-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66111.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">455</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">17346</span> Developing a Cybernetic Model of Interdepartmental Logistic Interactions in SME</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jonas%20Mayer">Jonas Mayer</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai-Frederic%20Seitz"> Kai-Frederic Seitz</a>, <a href="https://publications.waset.org/abstracts/search?q=Thorben%20Kuprat"> Thorben Kuprat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today’s competitive environment production’s logistic objectives such as ‘delivery reliability’ and ‘delivery time’ and distribution’s logistic objectives such as ‘service level’ and ‘delivery delay’ are attributed great importance. Especially for small and mid-sized enterprises (SME) attaining these objectives pose a key challenge. Within this context, one of the difficulties is that interactions between departments within the enterprise and their specific objectives are insufficiently taken into account and aligned. Interdepartmental independencies along with contradicting targets set within the different departments result in enterprises having sub-optimal logistic performance capability. This paper presents a research project which will systematically describe the interactions between departments and convert them into a quantifiable form. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=department-specific%20actuating%20and%20control%20variables" title="department-specific actuating and control variables">department-specific actuating and control variables</a>, <a href="https://publications.waset.org/abstracts/search?q=interdepartmental%20interactions" title=" interdepartmental interactions"> interdepartmental interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=cybernetic%20model" title=" cybernetic model"> cybernetic model</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20objectives" title=" logistic objectives"> logistic objectives</a> </p> <a href="https://publications.waset.org/abstracts/10592/developing-a-cybernetic-model-of-interdepartmental-logistic-interactions-in-sme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10592.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">372</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">17345</span> A Study of Population Growth Models and Future Population of India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sheena%20K.%20J.">Sheena K. J.</a>, <a href="https://publications.waset.org/abstracts/search?q=Jyoti%20Badge"> Jyoti Badge</a>, <a href="https://publications.waset.org/abstracts/search?q=Sayed%20Mohammed%20Zeeshan"> Sayed Mohammed Zeeshan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A Comparative Study of Exponential and Logistic Population Growth Models in India India is the second most populous city in the world, just behind China, and is going to be in the first place by next year. The Indian population has remarkably at higher rate than the other countries from the past 20 years. There were many scientists and demographers who has formulated various models of population growth in order to study and predict the future population. Some of the models are Fibonacci population growth model, Exponential growth model, Logistic growth model, Lotka-Volterra model, etc. These models have been effective in the past to an extent in predicting the population. However, it is essential to have a detailed comparative study between the population models to come out with a more accurate one. Having said that, this research study helps to analyze and compare the two population models under consideration - exponential and logistic growth models, thereby identifying the most effective one. Using the census data of 2011, the approximate population for 2016 to 2031 are calculated for 20 Indian states using both the models, compared and recorded the data with the actual population. On comparing the results of both models, it is found that logistic population model is more accurate than the exponential model, and using this model, we can predict the future population in a more effective way. This will give an insight to the researchers about the effective models of population and how effective these population models are in predicting the future population. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=population%20growth" title="population growth">population growth</a>, <a href="https://publications.waset.org/abstracts/search?q=population%20models" title=" population models"> population models</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20model" title=" exponential model"> exponential model</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20model" title=" logistic model"> logistic model</a>, <a href="https://publications.waset.org/abstracts/search?q=fibonacci%20model" title=" fibonacci model"> fibonacci model</a>, <a href="https://publications.waset.org/abstracts/search?q=lotka-volterra%20model" title=" lotka-volterra model"> lotka-volterra model</a>, <a href="https://publications.waset.org/abstracts/search?q=future%20population%20prediction" title=" future population prediction"> future population prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=demographers" title=" demographers"> demographers</a> </p> <a href="https://publications.waset.org/abstracts/158205/a-study-of-population-growth-models-and-future-population-of-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158205.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">124</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">17344</span> Generalized Extreme Value Regression with Binary Dependent Variable: An Application for Predicting Meteorological Drought Probabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retius%20Chifurira">Retius Chifurira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20value%20distribution" title="generalized extreme value distribution">generalized extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20linear%20model" title=" general linear model"> general linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20annual%20rainfall" title=" mean annual rainfall"> mean annual rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20drought%20probabilities" title=" meteorological drought probabilities"> meteorological drought probabilities</a> </p> <a href="https://publications.waset.org/abstracts/99321/generalized-extreme-value-regression-with-binary-dependent-variable-an-application-for-predicting-meteorological-drought-probabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99321.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">200</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">17343</span> A Kolmogorov-Smirnov Type Goodness-Of-Fit Test of Multinomial Logistic Regression Model in Case-Control Studies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chen%20Li-Ching">Chen Li-Ching </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The multinomial logistic regression model is used popularly for inferring the relationship of risk factors and disease with multiple categories. This study based on the discrepancy between the nonparametric maximum likelihood estimator and semiparametric maximum likelihood estimator of the cumulative distribution function to propose a Kolmogorov-Smirnov type test statistic to assess adequacy of the multinomial logistic regression model for case-control data. A bootstrap procedure is presented to calculate the critical value of the proposed test statistic. Empirical type I error rates and powers of the test are performed by simulation studies. Some examples will be illustrated the implementation of the test. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=case-control%20studies" title="case-control studies">case-control studies</a>, <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit%20test" title=" goodness-of-fit test"> goodness-of-fit test</a>, <a href="https://publications.waset.org/abstracts/search?q=Kolmogorov-Smirnov%20test" title=" Kolmogorov-Smirnov test"> Kolmogorov-Smirnov test</a>, <a href="https://publications.waset.org/abstracts/search?q=multinomial%20logistic%20regression" title=" multinomial logistic regression"> multinomial logistic regression</a> </p> <a href="https://publications.waset.org/abstracts/44967/a-kolmogorov-smirnov-type-goodness-of-fit-test-of-multinomial-logistic-regression-model-in-case-control-studies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44967.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">456</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">17342</span> The Theory behind Logistic Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jan%20Henrik%20Wosnitza">Jan Henrik Wosnitza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation" title="correlation">correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20risk%20estimation" title=" credit risk estimation"> credit risk estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=default%20correlation" title=" default correlation"> default correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=homoscedasticity" title=" homoscedasticity"> homoscedasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20logistic%20regression" title=" nonlinear logistic regression"> nonlinear logistic regression</a> </p> <a href="https://publications.waset.org/abstracts/14339/the-theory-behind-logistic-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14339.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">426</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">17341</span> Generalized Additive Model for Estimating Propensity Score</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tahmidul%20Islam">Tahmidul Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=covariate%20balances" title=" covariate balances"> covariate balances</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20additive%20model" title=" generalized additive model"> generalized additive model</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linearity" title=" non-linearity"> non-linearity</a>, <a href="https://publications.waset.org/abstracts/search?q=propensity%20score%20matching" title=" propensity score matching"> propensity score matching</a> </p> <a href="https://publications.waset.org/abstracts/40433/generalized-additive-model-for-estimating-propensity-score" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40433.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">367</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">17340</span> Credit Risk Prediction Based on Bayesian Estimation of Logistic Regression Model with Random Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sami%20Mestiri">Sami Mestiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdeljelil%20Farhat"> Abdeljelil Farhat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of the logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using the information on a sample of 528 Tunisian firms and 26 financial ratios, we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting" title="forecasting">forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20risk" title=" credit risk"> credit risk</a>, <a href="https://publications.waset.org/abstracts/search?q=Penalized%20Quasi%20Likelihood" title=" Penalized Quasi Likelihood"> Penalized Quasi Likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=Gibbs%20Sampler" title=" Gibbs Sampler"> Gibbs Sampler</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression%20with%20random%20effects" title=" logistic regression with random effects"> logistic regression with random effects</a>, <a href="https://publications.waset.org/abstracts/search?q=curve%20ROC" title=" curve ROC"> curve ROC</a> </p> <a href="https://publications.waset.org/abstracts/28981/credit-risk-prediction-based-on-bayesian-estimation-of-logistic-regression-model-with-random-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28981.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">542</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">17339</span> Robustified Asymmetric Logistic Regression Model for Global Fish Stock Assessment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Osamu%20Komori">Osamu Komori</a>, <a href="https://publications.waset.org/abstracts/search?q=Shinto%20Eguchi"> Shinto Eguchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroshi%20Okamura"> Hiroshi Okamura</a>, <a href="https://publications.waset.org/abstracts/search?q=Momoko%20Ichinokawa"> Momoko Ichinokawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The long time-series data on population assessments are essential for global ecosystem assessment because the temporal change of biomass in such a database reflects the status of global ecosystem properly. However, the available assessment data usually have limited sample sizes and the ratio of populations with low abundance of biomass (collapsed) to those with high abundance (non-collapsed) is highly imbalanced. To allow for the imbalance and uncertainty involved in the ecological data, we propose a binary regression model with mixed effects for inferring ecosystem status through an asymmetric logistic model. In the estimation equation, we observe that the weights for the non-collapsed populations are relatively reduced, which in turn puts more importance on the small number of observations of collapsed populations. Moreover, we extend the asymmetric logistic regression model using propensity score to allow for the sample biases observed in the labeled and unlabeled datasets. It robustified the estimation procedure and improved the model fitting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=double%20robust%20estimation" title="double robust estimation">double robust estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=ecological%20binary%20data" title=" ecological binary data"> ecological binary data</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20effect%20logistic%20regression%20model" title=" mixed effect logistic regression model"> mixed effect logistic regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=propensity%20score" title=" propensity score"> propensity score</a> </p> <a href="https://publications.waset.org/abstracts/65277/robustified-asymmetric-logistic-regression-model-for-global-fish-stock-assessment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65277.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">266</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">17338</span> Evaluating the Logistic Performance Capability of Regeneration Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thorben%20Kuprat">Thorben Kuprat</a>, <a href="https://publications.waset.org/abstracts/search?q=Julian%20Becker"> Julian Becker</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonas%20Mayer"> Jonas Mayer</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Nyhuis"> Peter Nyhuis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For years now, it has been recognized that logistic performance capability contributes enormously to a production enterprise’s competitiveness and as such is a critical control lever. In doing so, the orientation on customer wishes (e.g. delivery dates) represents a key parameter not only in the value-adding production but also in product regeneration. Since production and regeneration processes have different characteristics, production planning and control measures cannot be directly transferred to regeneration processes. As part of a special research project, the Institute of Production Systems and Logistics Hannover is focused on increasing the logistic performance capability of regeneration processes for complex capital goods. The aim is to ensure logistic targets are met by implementing a model specifically designed to align the capacities and load in regeneration processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=capacity%20planning" title="capacity planning">capacity planning</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20capital%20goods" title=" complex capital goods"> complex capital goods</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20performance" title=" logistic performance"> logistic performance</a>, <a href="https://publications.waset.org/abstracts/search?q=regeneration%20process" title=" regeneration process"> regeneration process</a> </p> <a href="https://publications.waset.org/abstracts/10591/evaluating-the-logistic-performance-capability-of-regeneration-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10591.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">489</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">17337</span> A Hybrid Model Tree and Logistic Regression Model for Prediction of Soil Shear Strength in Clay</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Mehryaar">Ehsan Mehryaar</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Armin%20Motahari%20Tabari"> Seyed Armin Motahari Tabari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Without a doubt, soil shear strength is the most important property of the soil. The majority of fatal and catastrophic geological accidents are related to shear strength failure of the soil. Therefore, its prediction is a matter of high importance. However, acquiring the shear strength is usually a cumbersome task that might need complicated laboratory testing. Therefore, prediction of it based on common and easy to get soil properties can simplify the projects substantially. In this paper, A hybrid model based on the classification and regression tree algorithm and logistic regression is proposed where each leaf of the tree is an independent regression model. A database of 189 points for clay soil, including Moisture content, liquid limit, plastic limit, clay content, and shear strength, is collected. The performance of the developed model compared to the existing models and equations using root mean squared error and coefficient of correlation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20tree" title="model tree">model tree</a>, <a href="https://publications.waset.org/abstracts/search?q=CART" title=" CART"> CART</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20shear%20strength" title=" soil shear strength"> soil shear strength</a> </p> <a href="https://publications.waset.org/abstracts/141471/a-hybrid-model-tree-and-logistic-regression-model-for-prediction-of-soil-shear-strength-in-clay" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141471.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">197</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">17336</span> Hybrid Model for Measuring the Hedge Strategy in Exchange Risk in Information Technology Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yi-Hsien%20Wang">Yi-Hsien Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Fu-Ju%20Yang"> Fu-Ju Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hwa-Rong%20Shen"> Hwa-Rong Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Rui-Lin%20Tseng"> Rui-Lin Tseng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The business is notably related to the market risk according to the increase of liberalization of financial markets. Hence, the company usually utilized high financial leverage of derivatives to hedge the risk. When the company choose different hedging instruments to face a variety of exchange rate risk, we employ the Multinomial Logistic-AHP to analyze the impact of various derivatives. Hence, the research summarized the literature on relevant factors affecting managers selected exchange rate hedging instruments, using Multinomial Logistic Model and and further integrate AHP. Using Experts’ Questionnaires can test multi-level selection and hedging effect of different hedging instruments in order to calculate the hedging instruments and the multi-level factors of weights to understand the gap between the empirical results and practical operation. Finally, the Multinomial Logistic-AHP Model will sort the weights to analyze. The research findings can be a basis reference for investors in decision-making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exchange%20rate%20risk" title="exchange rate risk">exchange rate risk</a>, <a href="https://publications.waset.org/abstracts/search?q=derivatives" title=" derivatives"> derivatives</a>, <a href="https://publications.waset.org/abstracts/search?q=hedge" title=" hedge"> hedge</a>, <a href="https://publications.waset.org/abstracts/search?q=multinomial%20logistic-AHP" title=" multinomial logistic-AHP"> multinomial logistic-AHP</a> </p> <a href="https://publications.waset.org/abstracts/7564/hybrid-model-for-measuring-the-hedge-strategy-in-exchange-risk-in-information-technology-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7564.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">442</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">17335</span> A Monte Carlo Fuzzy Logistic Regression Framework against Imbalance and Separation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Georgios%20Charizanos">Georgios Charizanos</a>, <a href="https://publications.waset.org/abstracts/search?q=Haydar%20Demirhan"> Haydar Demirhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Duygu%20Icen"> Duygu Icen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logistic%20regression" title="fuzzy logistic regression">fuzzy logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy" title=" fuzzy"> fuzzy</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic" title=" logistic"> logistic</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/175302/a-monte-carlo-fuzzy-logistic-regression-framework-against-imbalance-and-separation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175302.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">74</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">17334</span> An Information Matrix Goodness-of-Fit Test of the Conditional Logistic Model for Matched Case-Control Studies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li-Ching%20Chen">Li-Ching Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The case-control design has been widely applied in clinical and epidemiological studies to investigate the association between risk factors and a given disease. The retrospective design can be easily implemented and is more economical over prospective studies. To adjust effects for confounding factors, methods such as stratification at the design stage and may be adopted. When some major confounding factors are difficult to be quantified, a matching design provides an opportunity for researchers to control the confounding effects. The matching effects can be parameterized by the intercepts of logistic models and the conditional logistic regression analysis is then adopted. This study demonstrates an information-matrix-based goodness-of-fit statistic to test the validity of the logistic regression model for matched case-control data. The asymptotic null distribution of this proposed test statistic is inferred. It needs neither to employ a simulation to evaluate its critical values nor to partition covariate space. The asymptotic power of this test statistic is also derived. The performance of the proposed method is assessed through simulation studies. An example of the real data set is applied to illustrate the implementation of the proposed method as well. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20logistic%20model" title="conditional logistic model">conditional logistic model</a>, <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit" title=" goodness-of-fit"> goodness-of-fit</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20matrix" title=" information matrix"> information matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=matched%20case-control%20studies" title=" matched case-control studies"> matched case-control studies</a> </p> <a href="https://publications.waset.org/abstracts/67430/an-information-matrix-goodness-of-fit-test-of-the-conditional-logistic-model-for-matched-case-control-studies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67430.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">292</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">17333</span> Instability Index Method and Logistic Regression to Assess Landslide Susceptibility in County Route 89, Taiwan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20H.%20Wu">Y. H. Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ji-Yuan%20Lin"> Ji-Yuan Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Ming%20Liou"> Yu-Ming Liou </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to set up the landslide susceptibility map of County Route 89 at Ren-Ai Township in Nantou County using the Instability Index Method and Logistic regression. Seven susceptibility factors including Slope Angle, Aspect, Elevation, Distance to fold, Distance to River, Distance to Road and Accumulated Rainfall were obtained by GIS based on the Typhoon Toraji landslide area identified by Industrial Technology Research Institute in 2001. To calculate the landslide percentage of each factor and acquire the weight and grade the grid by means of Instability Index Method. In this study, landslide susceptibility can be classified into four grades: high, medium high, medium low and low, in order to determine the advantages and disadvantages of the two models. The precision of this model is verified by classification error matrix and SRC curve. These results suggest that the logistic regression model is a preferred method than instability index in the assessment of landslide susceptibility. It is suitable for the landslide prediction and precaution in this area in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=instability%20index%20method" title="instability index method">instability index method</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=landslide%20susceptibility" title=" landslide susceptibility"> landslide susceptibility</a>, <a href="https://publications.waset.org/abstracts/search?q=SRC%20curve" title=" SRC curve"> SRC curve</a> </p> <a href="https://publications.waset.org/abstracts/46070/instability-index-method-and-logistic-regression-to-assess-landslide-susceptibility-in-county-route-89-taiwan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46070.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">292</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">17332</span> Use of Multistage Transition Regression Models for Credit Card Income Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Denys%20Osipenko">Denys Osipenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Crook"> Jonathan Crook</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Because of the variety of the card holders’ behaviour types and income sources each consumer account can be transferred to a variety of states. Each consumer account can be inactive, transactor, revolver, delinquent, defaulted and requires an individual model for the income prediction. The estimation of transition probabilities between statuses at the account level helps to avoid the memorylessness of the Markov Chains approach. This paper investigates the transition probabilities estimation approaches to credit cards income prediction at the account level. The key question of empirical research is which approach gives more accurate results: multinomial logistic regression or multistage conditional logistic regression with binary target. Both models have shown moderate predictive power. Prediction accuracy for conditional logistic regression depends on the order of stages for the conditional binary logistic regression. On the other hand, multinomial logistic regression is easier for usage and gives integrate estimations for all states without priorities. Thus further investigations can be concentrated on alternative modeling approaches such as discrete choice models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multinomial%20regression" title="multinomial regression">multinomial regression</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20logistic%20regression" title=" conditional logistic regression"> conditional logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=credit%20account%20state" title=" credit account state"> credit account state</a>, <a href="https://publications.waset.org/abstracts/search?q=transition%20probability" title=" transition probability"> transition probability</a> </p> <a href="https://publications.waset.org/abstracts/19488/use-of-multistage-transition-regression-models-for-credit-card-income-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19488.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17331</span> A Solution for Production Facility Assignment: An Automotive Subcontract Case</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cihan%20%C3%87etinkaya">Cihan Çetinkaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Eren%20%C3%96zceylan"> Eren Özceylan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kerem%20Elibal"> Kerem Elibal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a solution method for selection of production facility. The motivation has been taken from a real life case, an automotive subcontractor which has two production facilities at different cities and parts. The problem is to decide which part(s) should be produced at which facility. To the best of our knowledge, until this study, there was no scientific approach about this problem at the firm and decisions were being given intuitively. In this study, some logistic cost parameters have been defined and with these parameters a mathematical model has been constructed. Defined and collected cost parameters are handling cost of parts, shipment cost of parts and shipment cost of welding fixtures. Constructed multi-objective mathematical model aims to minimize these costs while aims to balance the workload between two locations. Results showed that defined model can give optimum solutions in reasonable computing times. Also, this result gave encouragement to develop the model with addition of new logistic cost parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automotive%20subcontract" title="automotive subcontract">automotive subcontract</a>, <a href="https://publications.waset.org/abstracts/search?q=facility%20assignment" title=" facility assignment"> facility assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20costs" title=" logistic costs"> logistic costs</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20models" title=" multi-objective models"> multi-objective models</a> </p> <a href="https://publications.waset.org/abstracts/68574/a-solution-for-production-facility-assignment-an-automotive-subcontract-case" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68574.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">366</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">17330</span> A Novel Approach of NPSO on Flexible Logistic (S-Shaped) Model for Software Reliability Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pooja%20Rani">Pooja Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20S.%20Mahapatra"> G. S. Mahapatra</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Pandey"> S. K. Pandey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20reliability" title="software reliability">software reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20logistic%20growth%20curve%20model" title=" flexible logistic growth curve model"> flexible logistic growth curve model</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20cumulative%20failure%20prediction" title=" software cumulative failure prediction"> software cumulative failure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/36601/a-novel-approach-of-npso-on-flexible-logistic-s-shaped-model-for-software-reliability-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36601.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">344</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">17329</span> Classical and Bayesian Inference of the Generalized Log-Logistic Distribution with Applications to Survival Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdisalam%20Hassan%20Muse">Abdisalam Hassan Muse</a>, <a href="https://publications.waset.org/abstracts/search?q=Samuel%20Mwalili"> Samuel Mwalili</a>, <a href="https://publications.waset.org/abstracts/search?q=Oscar%20Ngesa"> Oscar Ngesa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A generalized log-logistic distribution with variable shapes of the hazard rate was introduced and studied, extending the log-logistic distribution by adding an extra parameter to the classical distribution, leading to greater flexibility in analysing and modeling various data types. The proposed distribution has a large number of well-known lifetime special sub-models such as; Weibull, log-logistic, exponential, and Burr XII distributions. Its basic mathematical and statistical properties were derived. The method of maximum likelihood was adopted for estimating the unknown parameters of the proposed distribution, and a Monte Carlo simulation study is carried out to assess the behavior of the estimators. The importance of this distribution is that its tendency to model both monotone (increasing and decreasing) and non-monotone (unimodal and bathtub shape) or reversed “bathtub” shape hazard rate functions which are quite common in survival and reliability data analysis. Furthermore, the flexibility and usefulness of the proposed distribution are illustrated in a real-life data set and compared to its sub-models; Weibull, log-logistic, and BurrXII distributions and other parametric survival distributions with 3-parmaeters; like the exponentiated Weibull distribution, the 3-parameter lognormal distribution, the 3- parameter gamma distribution, the 3-parameter Weibull distribution, and the 3-parameter log-logistic (also known as shifted log-logistic) distribution. The proposed distribution provided a better fit than all of the competitive distributions based on the goodness-of-fit tests, the log-likelihood, and information criterion values. Finally, Bayesian analysis and performance of Gibbs sampling for the data set are also carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hazard%20rate%20function" title="hazard rate function">hazard rate function</a>, <a href="https://publications.waset.org/abstracts/search?q=log-logistic%20distribution" title=" log-logistic distribution"> log-logistic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20log-logistic%20distribution" title=" generalized log-logistic distribution"> generalized log-logistic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20data" title=" survival data"> survival data</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/139326/classical-and-bayesian-inference-of-the-generalized-log-logistic-distribution-with-applications-to-survival-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139326.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">17328</span> Comparison of the Logistic and the Gompertz Growth Functions Considering a Periodic Perturbation in the Model Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Avan%20Al-Saffar">Avan Al-Saffar</a>, <a href="https://publications.waset.org/abstracts/search?q=Eun-Jin%20Kim"> Eun-Jin Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Both the logistic growth model and the gompertz growth model are used to describe growth processes. Both models driven by perturbations in different cases are investigated using information theory as a useful measure of sustainability and the variability. Specifically, we study the effect of different oscillatory modulations in the system's parameters on the evolution of the system and Probability Density Function (PDF). We show the maintenance of the initial conditions for a long time. We offer Fisher information analysis in positive and/or negative feedback and explain its implications for the sustainability of population dynamics. We also display a finite amplitude solution due to the purely fluctuating growth rate whereas the periodic fluctuations in negative feedback can lead to break down the system's self-regulation with an exponentially growing solution. In the cases tested, the gompertz and logistic systems show similar behaviour in terms of information and sustainability although they develop differently in time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamical%20systems" title="dynamical systems">dynamical systems</a>, <a href="https://publications.waset.org/abstracts/search?q=fisher%20information" title=" fisher information"> fisher information</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20density%20function%20%28pdf%29" title=" probability density function (pdf)"> probability density function (pdf)</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability" title=" sustainability"> sustainability</a> </p> <a href="https://publications.waset.org/abstracts/74758/comparison-of-the-logistic-and-the-gompertz-growth-functions-considering-a-periodic-perturbation-in-the-model-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74758.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">431</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">17327</span> Breast Cancer Mortality and Comorbidities in Portugal: A Predictive Model Built with Real World Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cec%C3%ADlia%20M.%20Ant%C3%A3o">Cecília M. Antão</a>, <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Jorge%20Nogueira"> Paulo Jorge Nogueira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer (BC) is the first cause of cancer mortality among Portuguese women. This retrospective observational study aimed at identifying comorbidities associated with BC female patients admitted to Portuguese public hospitals (2010-2018), investigating the effect of comorbidities on BC mortality rate, and building a predictive model using logistic regression. Results showed that the BC mortality in Portugal decreased in this period and reached 4.37% in 2018. Adjusted odds ratio indicated that secondary malignant neoplasms of liver, of bone and bone marrow, congestive heart failure, and diabetes were associated with an increased chance of dying from breast cancer. Although the Lisbon district (the most populated area) accounted for the largest percentage of BC patients, the logistic regression model showed that, besides patient’s age, being resident in Bragança, Castelo Branco, or Porto districts was directly associated with an increase of the mortality rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=comorbidities" title=" comorbidities"> comorbidities</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=adjusted%20odds%20ratio" title=" adjusted odds ratio"> adjusted odds ratio</a> </p> <a href="https://publications.waset.org/abstracts/143667/breast-cancer-mortality-and-comorbidities-in-portugal-a-predictive-model-built-with-real-world-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143667.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">87</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">17326</span> Efficient Management of Construction Logistics: A Challenge to Both Conventional and Technological Systems in the Developing Nations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nuruddeen%20Usman">Nuruddeen Usman</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Muhammad%20Ibrahim"> Ahmad Muhammad Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Management of construction logistics at construction sites becomes increasingly complex with rising construction volume, which made it relatively inefficient in the developing nations even with the technological advancement. The objective of this research is to conceptually synthesise the approaches and challenges befall in the course of construction logistic management, with the aim to proffer possible solution to it. Therefore, this study appraised the glitches associated with both conventional and technological methods of construction logistic management that result in its inefficiency. Thus, this investigation found that, both conventional and the technological issues were due to certain obstacles that affect the construction logistic management which resulted into delays, accidents, fraudulent activities, time and cost overrun. Therefore, this study has developed a framework that might bring a lasting solution to the challenges of construction logistic management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=construction" title="construction">construction</a>, <a href="https://publications.waset.org/abstracts/search?q=conventional" title=" conventional"> conventional</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic" title=" logistic"> logistic</a>, <a href="https://publications.waset.org/abstracts/search?q=technological" title=" technological"> technological</a> </p> <a href="https://publications.waset.org/abstracts/14211/efficient-management-of-construction-logistics-a-challenge-to-both-conventional-and-technological-systems-in-the-developing-nations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14211.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">554</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">17325</span> A Hybrid Adomian Decomposition Method in the Solution of Logistic Abelian Ordinary Differential and Its Comparism with Some Standard Numerical Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20J.%20Adeyeye">F. J. Adeyeye</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Eni"> D. Eni</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20M.%20Okedoye"> K. M. Okedoye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a Hybrid of Adomian decomposition method (ADM). This is the substitution of a One-step method of Taylor’s series approximation of orders I and II, into the nonlinear part of Adomian decomposition method resulting in a convergent series scheme. This scheme is applied to solve some Logistic problems represented as Abelian differential equation and the results are compared with the actual solution and Runge-kutta of order IV in order to ascertain the accuracy and efficiency of the scheme. The findings shows that the scheme is efficient enough to solve logistic problems considered in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adomian%20decomposition%20method" title="Adomian decomposition method">Adomian decomposition method</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20part" title=" nonlinear part"> nonlinear part</a>, <a href="https://publications.waset.org/abstracts/search?q=one-step%20method" title=" one-step method"> one-step method</a>, <a href="https://publications.waset.org/abstracts/search?q=Taylor%20series%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20approximation" title=" Taylor series approximation"> Taylor series approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20of%20Adomian%20polynomial" title=" hybrid of Adomian polynomial"> hybrid of Adomian polynomial</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20problem" title=" logistic problem"> logistic problem</a>, <a href="https://publications.waset.org/abstracts/search?q=Malthusian%20parameter" title=" Malthusian parameter"> Malthusian parameter</a>, <a href="https://publications.waset.org/abstracts/search?q=Verhulst%20Model" title=" Verhulst Model"> Verhulst Model</a> </p> <a href="https://publications.waset.org/abstracts/36872/a-hybrid-adomian-decomposition-method-in-the-solution-of-logistic-abelian-ordinary-differential-and-its-comparism-with-some-standard-numerical-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36872.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">400</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">17324</span> A Performance Model for Designing Network in Reverse Logistic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Dhib">S. Dhib</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Addouche"> S. A. Addouche</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Loukil"> T. Loukil</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Elmhamedi"> A. Elmhamedi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a reverse supply chain network is investigated for a decision making. This decision is surrounded by complex flows of returned products, due to the increasing quantity, the type of returned products and the variety of recovery option products (reuse, recycling, and refurbishment). The most important problem in the reverse logistic network (RLN) is to orient returned products to the suitable type of recovery option. However, returned products orientations from collect sources to the recovery disposition have not well considered in performance model. In this study, we propose a performance model for designing a network configuration on reverse logistics. Conceptual and analytical models are developed with taking into account operational, economic and environmental factors on designing network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reverse%20logistics" title="reverse logistics">reverse logistics</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20design" title=" network design"> network design</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20model" title=" performance model"> performance model</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20loop%20configuration" title=" open loop configuration"> open loop configuration</a> </p> <a href="https://publications.waset.org/abstracts/40989/a-performance-model-for-designing-network-in-reverse-logistic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40989.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">435</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17323</span> Comparative Analysis of Predictive Models for Customer Churn Prediction in the Telecommunication Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deepika%20Christopher">Deepika Christopher</a>, <a href="https://publications.waset.org/abstracts/search?q=Garima%20Anand"> Garima Anand</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attrition" title="attrition">attrition</a>, <a href="https://publications.waset.org/abstracts/search?q=retention" title=" retention"> retention</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20segmentation" title=" customer segmentation"> customer segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=telecommunications" title=" telecommunications"> telecommunications</a> </p> <a href="https://publications.waset.org/abstracts/184400/comparative-analysis-of-predictive-models-for-customer-churn-prediction-in-the-telecommunication-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184400.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">57</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">17322</span> Logistic Regression Based Model for Predicting Students’ Academic Performance in Higher Institutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Osaze%20Oshoiribhor">Emmanuel Osaze Oshoiribhor</a>, <a href="https://publications.waset.org/abstracts/search?q=Adetokunbo%20MacGregor%20John-Otumu"> Adetokunbo MacGregor John-Otumu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=ML" title=" ML"> ML</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/151047/logistic-regression-based-model-for-predicting-students-academic-performance-in-higher-institutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17321</span> Landslide Susceptibility Mapping: A Comparison between Logistic Regression and Multivariate Adaptive Regression Spline Models in the Municipality of Oudka, Northern of Morocco</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benchelha">S. Benchelha</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20C.%20Aoudjehane"> H. C. Aoudjehane</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Hakdaoui"> M. Hakdaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20El%20Hamdouni"> R. El Hamdouni</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Mansouri"> H. Mansouri</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Benchelha"> T. Benchelha</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Layelmam"> M. Layelmam</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Alaoui"> M. Alaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The logistic regression (LR) and multivariate adaptive regression spline (MarSpline) are applied and verified for analysis of landslide susceptibility map in Oudka, Morocco, using geographical information system. From spatial database containing data such as landslide mapping, topography, soil, hydrology and lithology, the eight factors related to landslides such as elevation, slope, aspect, distance to streams, distance to road, distance to faults, lithology map and Normalized Difference Vegetation Index (NDVI) were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by the two mentioned methods. Before the calculation, this database was divided into two parts, the first for the formation of the model and the second for the validation. The results of the landslide susceptibility analysis were verified using success and prediction rates to evaluate the quality of these probabilistic models. The result of this verification was that the MarSpline model is the best model with a success rate (AUC = 0.963) and a prediction rate (AUC = 0.951) higher than the LR model (success rate AUC = 0.918, rate prediction AUC = 0.901). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landslide%20susceptibility%20mapping" title="landslide susceptibility mapping">landslide susceptibility mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20logistic" title=" regression logistic"> regression logistic</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20adaptive%20regression%20spline" title=" multivariate adaptive regression spline"> multivariate adaptive regression spline</a>, <a href="https://publications.waset.org/abstracts/search?q=Oudka" title=" Oudka"> Oudka</a>, <a href="https://publications.waset.org/abstracts/search?q=Taounate" title=" Taounate"> Taounate</a> </p> <a href="https://publications.waset.org/abstracts/107250/landslide-susceptibility-mapping-a-comparison-between-logistic-regression-and-multivariate-adaptive-regression-spline-models-in-the-municipality-of-oudka-northern-of-morocco" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107250.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">17320</span> Logistic Model Tree and Expectation-Maximization for Pollen Recognition and Grouping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Endrick%20Barnacin">Endrick Barnacin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Luc%20Henry"> Jean-Luc Henry</a>, <a href="https://publications.waset.org/abstracts/search?q=Jack%20Molini%C3%A9"> Jack Molinié</a>, <a href="https://publications.waset.org/abstracts/search?q=Jimmy%20Nagau"> Jimmy Nagau</a>, <a href="https://publications.waset.org/abstracts/search?q=H%C3%A9l%C3%A8ne%20Delatte"> Hélène Delatte</a>, <a href="https://publications.waset.org/abstracts/search?q=G%C3%A9rard%20Lebreton"> Gérard Lebreton</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pollen%20recognition" title="pollen recognition">pollen recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20model%20tree" title=" logistic model tree"> logistic model tree</a>, <a href="https://publications.waset.org/abstracts/search?q=expectation-maximization" title=" expectation-maximization"> expectation-maximization</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a> </p> <a href="https://publications.waset.org/abstracts/111314/logistic-model-tree-and-expectation-maximization-for-pollen-recognition-and-grouping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111314.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">182</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17319</span> Generalized Additive Model Approach for the Chilean Hake Population in a Bio-Economic Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Selin%20Guney">Selin Guney</a>, <a href="https://publications.waset.org/abstracts/search?q=Andres%20Riquelme"> Andres Riquelme</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The traditional bio-economic method for fisheries modeling uses some estimate of the growth parameters and the system carrying capacity from a biological model for the population dynamics (usually a logistic population growth model) which is then analyzed as a traditional production function. The stock dynamic is transformed into a revenue function and then compared with the extraction costs to estimate the maximum economic yield. In this paper, the logistic population growth model for the population is combined with a forecast of the abundance and location of the stock by using a generalized additive model approach. The paper focuses on the Chilean hake population. This method allows for the incorporation of climatic variables and the interaction with other marine species, which in turn will increase the reliability of the estimates and generate better extraction paths for different conservation objectives, such as the maximum biological yield or the maximum economic yield. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-economic" title="bio-economic">bio-economic</a>, <a href="https://publications.waset.org/abstracts/search?q=fisheries" title=" fisheries"> fisheries</a>, <a href="https://publications.waset.org/abstracts/search?q=GAM" title=" GAM"> GAM</a>, <a href="https://publications.waset.org/abstracts/search?q=production" title=" production"> production</a> </p> <a href="https://publications.waset.org/abstracts/59045/generalized-additive-model-approach-for-the-chilean-hake-population-in-a-bio-economic-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59045.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">252</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=logistic%20model&amp;page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=logistic%20model&amp;page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=logistic%20model&amp;page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=logistic%20model&amp;page=5">5</a></li> <li class="page-item"><a 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