نتایج جستجو برای: prediction models
تعداد نتایج: 1108474 فیلتر نتایج به سال:
background: noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. one of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. in this study, advanced fuzzy approaches were employed to develop relatively accurate ...
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Background & Objective: Using parametric models is common approach in survival analysis. In the recent years, artificial neural network (ANN) models have increasingly used in survival prediction. The aim of this study was to predict of survival rate of patients with gastric cancer by using a parametric regression and ANN models and compare these methods. Methods: We used the data of 436 gast...
Prediction Markets for Economic Forecasting Prediction markets – markets used to forecast future events – have been used to accurately forecast the outcome of political contests, sporting events, and, occasionally, economic outcomes. This chapter summarizes the latest research on prediction markets in order to further their utilization by economic forecasters. We show that prediction markets ha...
in recent decades, seizure prediction caused a lot of research in both signal processing and neuroscience field. the researches tried to enhance the conventional seizure prediction algorithms such that rate of the false alarms be appropriately small so that seizures can be predicted according to clinical standards. up to now none of the proposed algorithms have been sufficiently adequate. in th...
Erosion, sediment transport and sediment estimate phenomenon with their damage in rivers is a one of the most importance point in river engineering. Correctly modeling and prediction of this parameter with involving the river flow discharge can be most useful in life of hydraulic structures and drainage networks. In fact, using the multivariate models and involving the effective other parameter...
Introduction: In Processes Modeling, when there is relatively a high correlation between covariates, multicollinearity is created, and it leads to reduction in model's efficiency. In this study, by using principle component analysis, modification of the effect of multicolinearity in Artificial Neural Network (ANN) and Logistic Regression (LR) has been studied. Also, the effect of multicolineari...
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