scholarly journals Predictive Modeling of Copper in Electro-deposition of Bronze Using Regression and Neural Networks

2009 ◽  
Vol 27 (1) ◽  
pp. 47-55 ◽  
Author(s):  
K Subramanian ◽  
V M Periasamy ◽  
M Pushpavanam ◽  
K Ramasamy
Keyword(s):  
Neural Networks ◽  
Predictive Modeling ◽  
Electro Deposition

Predictive Modeling Versus Regression

2010 ◽  
pp. 110-152
Author(s):  
Patricia Cerrito
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Probability Distribution ◽  
Linear Model ◽  
Predictive Modeling ◽  
Patient Population ◽  
Nearest Neighbor ◽  
General Term ◽  
Outcome Variable

Predictive modeling includes regression, both logistic and linear, depending upon the type of outcome variable. It can also include the generalized linear model. However, there are other types of models also available, including decision trees and artificial neural networks under the general term of predictive modeling. Predictive modeling includes nearest neighbor discriminant analysis, also known as memory based reasoning. These other models are nonparametric and do not require that you know the probability distribution of the underlying patient population. Therefore, they are much more flexible when used to examine patient outcomes. Because predictive modeling uses regression in addition to these other models, the end results will improve upon those found using just regression by itself.

An Analysis of the Use of Predictive Modeling with Business Intelligence Systems for Exploration of Precious Metals Using Biogeochemical Data

2013 ◽  
Vol 4 (2) ◽  
pp. 39-53 ◽  
Author(s):  
Thomas A. Woolman ◽  
John C. Yi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Anomaly Detection ◽  
Predictive Modeling ◽  
Business Intelligence ◽  
Precious Metals ◽  
Geological Exploration ◽  
Feed Forward ◽  
Business Intelligence Systems ◽  
Modeling Techniques

This study addresses the use of predictive modeling techniques; primarily feed-forward artificial neural networks as a tool for forecasting geological exploration targets for gold prospecting. It also provides evidence of effectiveness of using Business Intelligence systems to model pathfinder variables, anomaly detection, and forecasting to locate potential exploration sites for precious metals. The results indicate that the use of advanced Business Intelligence systems can be of extremely high value to the extractive minerals exploration industry.

Predictive Models in Urology

2013 ◽  
Vol 80 (1) ◽  
pp. 42-45 ◽  
Author(s):  
Andrea Cestari
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Predictive Modeling ◽  
Predictive Models ◽  
Machine Intelligence ◽  
Health Records ◽  
Knowledge Based ◽  
Current State ◽  
Computer Based ◽  
Future Direction

Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called ‘machine intelligence’ will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

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