scholarly journals An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students

2016 ◽  
Vol 40 (4) ◽  
Author(s):  
José Luis Fernández-Alemán ◽  
Laura López-González ◽  
Ofelia González-Sequeros ◽  
Chrisina Jayne ◽  
Juan José López-Jiménez ◽  
...  
Keyword(s):  
Neural Network ◽  
Empirical Study ◽  
Pharmacy Students ◽  
Human Anatomy ◽  
Audience Response ◽  
Audience Response Technology

An Empirical Study of Supply Chain Risk Warning in China’s Automobile Manufacturing Based on BP Neural Network

2012 ◽  
Vol 630 ◽  
pp. 496-501 ◽  
Author(s):  
Ming Hong Zhang ◽  
Liang Lu ◽  
Yu Liu
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Empirical Study ◽  
Risk Model ◽  
Warning System ◽  
Risk Indicator ◽  
Supply Chain Risk ◽  
Automobile Manufacturing ◽  
Academic Level ◽  
Risk Warning

From the perspective of risk indicator of supply chain, this paper makes an empirical study of risk warning system in Jinlong Automobile Group in Fujian province. It discusses several indicators that cause risks to supply chain in company and categorize them. Then risk model is tested with artificial neural network to testify its applicability and accuracy. It’s argued that this is a rewarding attempt to go from academic level towards practical use and explores ways of thinking for risk warning system designing.

Audience response technology: under-appreciated value of post hoc analysis

2011 ◽  
Vol 45 (11) ◽  
pp. 1157-1158 ◽  
Author(s):  
Paul Schick ◽  
Sandra Abramson ◽  
James Burke
Keyword(s):  
Audience Response ◽  
Post Hoc Analysis ◽  
Post Hoc ◽  
Audience Response Technology

An Efficient Neural Network Model with Taylor Series-Based Data Pre-Processing for Stock Price Forecast

2013 ◽  
Vol 284-287 ◽  
pp. 3020-3024
Author(s):  
Jung Bin Li ◽  
Chien Ho Wu
Keyword(s):  
Neural Network ◽  
Empirical Study ◽  
Neural Network Model ◽  
Taylor Series ◽  
Stock Price ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
High Accuracy ◽  
Price Forecast

This study adopts popular back-propagation neural network to make one-period-ahead prediction of the stock price. A model based on Taylor series by using both fundamental and technical indicators EPS and MACD as input data is built for an empirical study. Leading Taiwanese companies in non-hi-tech industry such as Formosa Plastics, Yieh Phui Steel, Evergreen Marine, and Chang Hwa Bank are picked as targets to analyze their reasonable prices and moving trends. The performance of this model shows remarkable return and high accuracy in making long/short strategies.

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