Probing Norwalk-like virus presence in shellfish, using artificial neural networks

2004 ◽  
Vol 50 (1) ◽  
pp. 125-129 ◽  
Author(s):  
G. Brion ◽  
S. Lingeriddy ◽  
T.R. Neelakantan ◽  
M. Wang ◽  
R. Girones ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Classification Performance ◽  
Relative Importance ◽  
Logistic Regression Models ◽  
Ann Models ◽  
Artificial Neural ◽  
Presence And Absence ◽  
Input Variables ◽  
Artificial Neural Network Ann

A database was examined using artificial neural network (ANN) models to investigate the efficacy of predicting PCR-identified Norwalk-like virus presence and absence in shellfish. The relative importance of variables in the model and the predictive power obtained by application of ANN modelling methods were compared with previously developed logistic regression models. In addition, two country-specific datasets were analysed separately with ANN models to determine if the relative importance of the input variables was similar for geographically diverse regions. The results of this analysis found that ANN models predicted Norwalk-like virus presence and absence in shellfish with equivalent, and better, precision than logistic regression models. For overall classification performance, ANN modelling had a rate of 93%, vs 75% for the logistic regression. ANN models were able to illuminate the site-specific relationships between indicators and pathogens.

scholarly journals The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Lee Saro ◽  
Jeon Seong Woo ◽  
Oh Kwan-Young ◽  
Lee Moung-Jin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Regression Models ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Landslide Occurrence ◽  
Logistic Regression Models ◽  
Artificial Neural

AbstractThe aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330), and ‘aspect’ yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

2009 ◽  
Vol 34 (3) ◽  
pp. 319-347 ◽  
Author(s):  
Razia Azen ◽  
Nicole Traxel
Keyword(s):  
Logistic Regression ◽  
Random Sampling ◽  
Regression Models ◽  
Applied Research ◽  
Simple Random Sampling ◽  
Relative Importance ◽  
Bootstrap Sampling ◽  
Dominance Analysis ◽  
Logistic Regression Models ◽  
Predictor Importance

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R2 analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A simulation study, using both simple random sampling from a known population and bootstrap sampling from a single (parent) random sample, was performed to evaluate the bias, sampling distribution, and confidence intervals of quantitative dominance measures as well as the reproducibility of qualitative dominance measures. Results indicated that the bootstrap procedure is feasible and can be used in applied research to generalize logistic regression dominance analysis results to the population of interest. The procedures for determining and interpreting the general dominance of predictors in a logistic regression context are illustrated with an empirical example.

Monitoring-System Development of Gillnet Using Artificial Neural Network

2020 ◽  
Author(s):  
Chungkuk Jin ◽  
HanSung Kim ◽  
JeongYong Park ◽  
MooHyun Kim ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Learning Algorithm ◽  
System Development ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

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