Filter Banks and Neural Network-based Feature Extraction and Automatic Classification of Electrogastrogram

10.1114/1.151 ◽  
1999 ◽  
Vol 27 (1) ◽  
pp. 88-95 ◽  
Author(s):  
Zhishun Wang ◽  
Zhenya He ◽  
J. D. Z. Chen
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Filter Banks ◽  
Automatic Classification

scholarly journals Classification of data stream based on dynamic feature extraction and neural network

2010 ◽  
Vol 30 (6) ◽  
pp. 1539-1542
Author(s):  
Cheng-liang WANG ◽  
Xu PANG ◽  
Zhi-jian LU ◽  
Chang-yin LUO
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Data Stream ◽  
Dynamic Feature

scholarly journals Smartphone enabled Counterfeit Note Detection using Siamese Network

2019 ◽  
Vol 9 (2S) ◽  
pp. 598-604
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Data Augmentation ◽  
Feasible Solution ◽  
Smart Phone ◽  
Research Articles ◽  
Distinctive Features ◽  
Siamese Network ◽  
Processing Data

Counterfeit note has a disastrous impact on a country’s economy. The circulation of such fake notes not only diminishes the value of genuine note but also results in inflation. The feasible solution to this burning issue is to create awareness about the counterfeit notes among public and to equip them with a technology to detect fake notes on their own. Though there exist numerous research articles on detection of fake notes, they are not handy. The reason for this could be the unavailability or unaffordability in acquiring the equipment for the same. This paper proposes an approach whose implementation can easily be deployed on a smart phone and hence anyone with access to them can use the application to detect the fake notes. The proposed approach consists of the processing phases including image procurement, pre-processing, data augmentation, feature extraction and classification. ₹500 notes are considered for experimentation analysis. Out of 17 distinctive features, 3 such from the obverse side are considered to evaluate the genuineness of the note. Siamese neural network is employed to build a model for effective classification of the notes. The performance of the proposed approach is evaluated at 85% with respect to accuracy.

scholarly journals Pattern Recognition by Hierarchical Feature Extraction

2003 ◽  
Vol 15 (3) ◽  
pp. 278-285
Author(s):  
Daigo Misaki ◽  
◽  
Shigeru Aomura ◽  
Noriyuki Aoyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Input Data ◽  
Local Features ◽  
Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

2015 ◽  
Vol 26 (1) ◽  
pp. 195-202 ◽  
Author(s):  
Francesco Ciompi ◽  
Bartjan de Hoop ◽  
Sarah J. van Riel ◽  
Kaman Chung ◽  
Ernst Th. Scholten ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Convolutional Neural Network ◽  
Automatic Classification
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