scholarly journals Constructing early diagnosis model of colorectal cancer based on expression profile

2020 ◽  
Author(s):  
shaoqiang Wang ◽  
Yifan Wang ◽  
Shudong Wang
Keyword(s):  
Expression Profiles ◽  
Back Propagation ◽  
Occult Blood ◽  
Back Propagation Neural Network ◽  
Bowel Cancer ◽  
Fecal Occult Blood ◽  
Detection Model ◽  
Error Back Propagation ◽  
Model Based ◽  
Diagnosis Model

AbstractpurposeIn order to break through the restrictive factors such as the fecal occult blood test (FOBT) in the routine detection of bowel cancer, which is susceptible to diet and drugs, and the high cost and inconvenience of microscopy, Seeking a possible FOBT alternative.MethodsAn error back propagation neural network (BPNN) algorithm was used to construct a CRC diagnosis model based on expression profiles.ResultsThe accuracy of the model on the training and test sets is 0.943 and 0.935, respectively. AUC all reached above 0.95.ConclusionThe CRC molecular detection model based on expression profiles provides a possible alternative to FOBT. It provides a new approach and method for the clinical diagnosis of bowel cancer.

Free Air Ball Forming Process Parameter Analysis for Wire Bonder

Manufacturing ◽  
2002 ◽  
Author(s):  
Jau-Liang Chen ◽  
Hsu-Yang Chang
Keyword(s):  
Optimal Parameter ◽  
Back Propagation ◽  
Tail Length ◽  
Back Propagation Neural Network ◽  
Parameter Analysis ◽  
Forming Process ◽  
Current Time ◽  
Ball Size ◽  
Error Back Propagation ◽  
Free Air

In this paper, we are focusing on the FAB forming technology for fine pitch wire bonding. The parameters that affect the FAB formation include: 1) tail length; 2) spark gap; 3) Electric flame-off (EFO) voltage, current, time; 4) relative position between electrode plate and tail; 5) wire material; and 6) type of capillary. Except the last two items, all the other parameters can be quantified for analysis. By using Taguchi method it was found that EFO time and EFO current are the most important parameters that affect the formation free-air ball. The error-back-propagation neural network was then used to predict suitable EFO time and current setting. The main objective in this research is to find a suitable rule for parameters setting in order to control the FAB ball size as required. The result can be used in the future for optimal parameter setting and prediction of FAB formation.

scholarly journals Bending Angle Prediction Model Based on BPNN-Spline in Air Bending Springback Process

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Zhefeng Guo ◽  
Wencheng Tang
Keyword(s):  
Prediction Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Numerical Control ◽  
Experimental Sample ◽  
Bending Angle ◽  
Air Bending ◽  
Error Back Propagation ◽  
Element Simulation ◽  
Bending Process

In order to rapidly and accurately predict the springback bending angle in V-die air bending process, a springback bending angle prediction model on the combination of error back propagation neural network and spline function (BPNN-Spline) is presented in this study. An orthogonal experimental sample set for training BPNN-Spline is obtained by finite element simulation. Through the analysis of network structure, the BPNN-Spline black box function of bending angle prediction is established, and the advantage of BPNN-Spline is discussed in comparison with traditional BPNN. The results show a close agreement with simulated and experimental results by application examples, which means that the BPNN-Spline model in this study has higher prediction accuracy and better applicable ability. Therefore, it could be adopted in a numerical control bending machine system.

scholarly journals Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kuo-Nan Yu ◽  
Her-Terng Yau ◽  
Jian-Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Photovoltaic System ◽  
Photovoltaic Systems ◽  
Solar Photovoltaic ◽  
Error Back Propagation ◽  
Point Tracking ◽  
Diagnosis Method

At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT) for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

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