Image Feature Extraction Based on an Extended Non-negative Sparse Coding Neural Network Model

2005 ◽  
pp. 807-812
Author(s):  
Li Shang Li ◽  
Deshuang Huang ◽  
Chunhou Zheng ◽  
Zhanli Sun
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Network Model ◽  
Neural Network Model ◽  
Sparse Coding ◽  
Image Feature ◽  
Image Feature Extraction

scholarly journals Cell-Free DNA Pattern Identification and its Classification using Convolution Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5362-5364
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
User Interface ◽  
Graphical User Interface ◽  
Probabilistic Method ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Free Dna ◽  
Dna Pattern

Circulating cell DNA (cfDNA) design identification assumes a cardinal job in fetal drug, transplantation and oncology. Be that as it may, it has additionally demonstrated to be a biomarker for different maladies. There are numerous order strategies by which the acknowledgment and arrangement should be possible. So as to have a superior time unpredictability and improve the precision further, this strategy targets distinguishing and arranging the general DNA examples and ailments related with them utilizing cfDNA Images in a Convolution Neural Network. A probabilistic method is used for cfDNA image feature extraction, fragmentation and interpretation. Graphical User Interface is the platform where this method is employed since it uses visual indicators in place of text-based interface. The aftereffects of this test demonstrate that the Convolution Neural Network calculation can perceive cfDNA successions accurately and successfully with no dubiety. Prepared with examples, the CNN can effectively characterize the picture surrendered to coordinated and unparalleled examples with numerical highlights.

Image Feature Extraction and Denoising by Sparse Coding

1999 ◽  
Vol 2 (2) ◽  
pp. 104-110 ◽  
Author(s):  
E. Oja ◽  
A. Hyvärinen ◽  
P. Hoyer
Keyword(s):  
Feature Extraction ◽  
Sparse Coding ◽  
Image Feature ◽  
Image Feature Extraction

Research on Aviation Material with Unsafe Events Prediction Based on BP Neural Network with PCA Feature Extraction

2014 ◽  
Vol 540 ◽  
pp. 488-491 ◽  
Author(s):  
Xu Sheng Gan ◽  
Hua Ping Li ◽  
Jing Shun Duanmu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Aviation Material ◽  
Training Samples ◽  
Basic Features ◽  
Bp Neural Network Model ◽  
Better Than

In order to better predict the aviation material unsafe events, a BP neural network model based on PCA feature extraction is proposed. Firstly, the training samples of aviation material unsafe events are used to carry out the PCA feature extraction, and then using the extracted basic features, BP neural network model is established. The numerical example shows that, the hybrid model proposed is better than that of alone BP neural network model, and it is effective and feasible to establish the unsafe events model for aviation material.

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