Understanding Data Modeling Methods: Graphical Syntax

2006 ◽  
pp. 89-106
Keyword(s):  
Data Modeling ◽  
Modeling Methods

Frequency of Change Orders in Highway Construction Using Alternate Count-Data Modeling Methods

2010 ◽  
Vol 136 (8) ◽  
pp. 886-893 ◽  
Author(s):  
Panagiotis Ch. Anastasopoulos ◽  
Samuel Labi ◽  
Abhishek Bhargava ◽  
Claire Bordat ◽  
Fred L. Mannering
Keyword(s):  
Count Data ◽  
Highway Construction ◽  
Data Modeling ◽  
Change Orders ◽  
Modeling Methods

Hydrographic data modeling methods for determining precise seafloor topography

2013 ◽  
Vol 17 (4) ◽  
pp. 661-669 ◽  
Author(s):  
Nedim Onur Aykut ◽  
Burak Akpınar ◽  
Ömer Aydın
Keyword(s):  
Data Modeling ◽  
Seafloor Topography ◽  
Hydrographic Data ◽  
Modeling Methods

scholarly journals Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0240948
Author(s):  
Zhanyou Xu ◽  
Andreomar Kurek ◽  
Steven B. Cannon ◽  
William D. Beavis
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Mixed Models ◽  
Genetic Improvement ◽  
Data Modeling ◽  
Support Vector ◽  
Iron Deficiency Chlorosis ◽  
Modeling Methods ◽  
Ordinal Response

In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the ‘wrong’ genotypes retained, and how often are the ‘correct’ genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC.

Speaker Recognition in Uncontrolled Environment: A Review

2013 ◽  
Vol 22 (1) ◽  
pp. 49-65 ◽  
Author(s):  
Narendra Karamangala ◽  
Ramaswamy Kumaraswamy
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Background Noise ◽  
Data Modeling ◽  
Research Area ◽  
Signal Quality ◽  
Modeling Methods ◽  
Modeling Techniques ◽  
Active Research ◽  
Active Research Area

Abstract.Speaker recognition has been an active research area for many years. Methods to represent and quantify information embedded in speech signal are termed as features of the signal. The features are obtained, modeled and stored for further reference when the system is to be tested. Decision whether to accept or reject speakers are taken based on parameters of the data modeling techniques. Real world offers various degradations to the signal that hamper the signal quality. The degradations may be due to ambient background noise, reverberation or multispeaker scenario. This paper presents a survey of various feature extraction, data modeling methods, metrics that are used to take the decisions and methods that can be used to preprocess the degraded data that have been used to perform the task of speaker recognition.

Image Analysis of Intramembrane Particles in Freeze-Fractured Biomembranes Using Computer Simulation Techniques

1975 ◽  
Vol 33 ◽  
pp. 214-215
Author(s):  
D.J. Benefiel ◽  
R.S. Weinstein
Keyword(s):  
Membrane Proteins ◽  
Freeze Fracture ◽  
Integral Membrane Proteins ◽  
Systematic Evaluation ◽  
Intramembrane Particles ◽  
Modeling Methods ◽  
Simulation Techniques ◽  
Freeze Fracture Electron Microscopy ◽  
Fracture Electron ◽  
Computer Simulation Techniques

Intramembrane particles (IMP or MAP) are components of most biomembranes. They are visualized by freeze-fracture electron microscopy, and they probably represent replicas of integral membrane proteins. The presence of MAP in biomembranes has been extensively investigated but their detailed ultrastructure has been largely ignored. In this study, we have attempted to lay groundwork for a systematic evaluation of MAP ultrastructure. Using mathematical modeling methods, we have simulated the electron optical appearances of idealized globular proteins as they might be expected to appear in replicas under defined conditions. By comparing these images with the apearances of MAPs in replicas, we have attempted to evaluate dimensional and shape distortions that may be introduced by the freeze-fracture technique and further to deduce the actual shapes of integral membrane proteins from their freezefracture images.

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