Evaluation of a statistical prediction model used in the design of neural network based ECG classifiers: a multiple linear regression approach

2003 ◽  
Author(s):  
D.D. Finlay ◽  
C.D. Nugent ◽  
P.J. McCullagh ◽  
N.D. Black ◽  
J.A. Lopez
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Prediction Model ◽  
Multiple Linear Regression ◽  
Statistical Prediction ◽  
Regression Approach ◽  
Statistical Prediction Model

An Epidemic Avian Influenza Prediction Model Based on Google Trends

2019 ◽  
Vol 16 (4) ◽  
pp. 303-310 ◽  
Author(s):  
Yi Lu ◽  
Shuo Wang ◽  
Jianying Wang ◽  
Guangya Zhou ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Avian Influenza ◽  
Prediction Model ◽  
Multiple Linear Regression ◽  
Google Trends ◽  
Health Providers ◽  
Human Beings ◽  
Success Rates ◽  
New Model ◽  
Regression Approach

The occurrence of epidemic avian influenza (EAI) not only hinders the development of a country's agricultural economy, but also seriously affects human beings’ life. Recently, the information collected from Google Trends has been increasingly used to predict various epidemics. In this study, using the relevant keywords in Google Trends as well as the multiple linear regression approach, a model was developed to predict the occurrence of epidemic avian influenza. It was demonstrated by rigorous cross-validations that the success rates achieved by the new model were quite high, indicating the predictor will become a very useful tool for hospitals and health providers.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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