An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data

2004 ◽  
Vol 178 (3-4) ◽  
pp. 389-397 ◽  
Author(s):  
Julian D Olden ◽  
Michael K Joy ◽  
Russell G Death
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Data ◽  
Variable Importance ◽  
Comparison Of Methods ◽  
Accurate Comparison ◽  
Artificial Neural

scholarly journals Tutorial: Artificial Neural Networks and Deep Learning to Analyze Single-Case Designs

2020 ◽  
Author(s):  
Marc J Lanovaz ◽  
Jordan D Bailey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Visual Analysis ◽  
Clinical Medicine ◽  
Single Case ◽  
Simulated Data ◽  
Fundamental Physics ◽  
Promising Area ◽  
Artificial Neural ◽  
Single Case Designs

Since the start of the 21st century, few advances have had as far reaching consequences in science as the widespread adoption of artificial neural networks in fields as diverse as fundamental physics, clinical medicine, and social networking. In behavior analysis, one promising area for the adoption of neural networks involves the analysis of single-case graphs. However, few behavior analysts have any training on the use of these methods, which may limit progress in this area. The purpose of our tutorial is to address this issue by providing a step-by-step description on using artificial neural networks to improve the analysis of single-case graphs. To this end, we trained a new model using simulated data to analyze multiple baseline graphs and compared its outcomes to those of visual analysis on a previously published dataset. In addition to showing that artificial neural networks may outperform visual analysis, the tutorial provides information to facilitate the replication and extension of this line of work to other datasets and designs.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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