scholarly journals Heart Disease Detection using Iridology with Principal Component Analysis (PCA) and Backpropagation Neural Network

2018 ◽  
Author(s):  
Leonardus Sandy Ade Putra ◽  
R. Rizal Isnanto ◽  
Aris Triwiyatno ◽  
Vincentius Abdi Gunawan
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Heart Disease ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Detection ◽  
Backpropagation Neural Network

scholarly journals Graphene Foam Chemical Sensor System Based on Principal Component Analysis and Backpropagation Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hongling Hua ◽  
Xiaohui Xie ◽  
Jinjin Sun ◽  
Ge Qin ◽  
Caiyan Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Chemical Sensors ◽  
Chemical Sensor ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Backpropagation Neural Network ◽  
Graphene Foam ◽  
New Strategy

A kind of graphene foam chemical sensor (GFCS) system based on the principal component analysis (PCA) and backpropagation neural network (BPNN) was presented in this paper. Compared with conventional chemical sensors, the GFCS could discriminate various chemical molecules with selectivity without surface modification. The GFCS system consisted of an unmodified graphene foam chemical sensor, an electrical resistance time domain detection system (ERTDS), and a pattern recognition module. The GFCS has been validated via several chemical molecules discrimination including chloroform, acetone, and ether. The experimental results showed that the discrimination accuracy for each molecule exceeded 97% and a single measurement can be achieved in ten minutes. This work may have presented a new strategy for research and application for graphene chemical sensors.

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