Concept Features Extraction and Text Clustering Analysis of Neural Networks Based on Cognitive Mechanism

2006 ◽  
pp. 235-246
Author(s):  
Lin Wang ◽  
Minghu Jiang ◽  
Shasha Liao ◽  
Beixing Deng ◽  
Chengqing Zong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Clustering Analysis ◽  
Text Clustering ◽  
Features Extraction ◽  
Cognitive Mechanism

scholarly journals FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security

2019 ◽  
Vol 9 (10) ◽  
pp. 2042 ◽  
Author(s):  
Rachida Tobji ◽  
Wu Di ◽  
Naeem Ayoub
Keyword(s):  
Neural Networks ◽  
Iris Recognition ◽  
State Of The Art ◽  
Features Extraction ◽  
Iris Segmentation ◽  
Recognition Method ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale ◽  
Biometric Security

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

Features Extraction for Live Streaming Video Classification with Deep and Convolutional Neural Networks

2021 ◽  
Author(s):  
Elans Grabs ◽  
Tianhua Chen ◽  
Ernests Petersons ◽  
Dmitry Efrosinin ◽  
Aleksandrs Ipatovs ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Features Extraction ◽  
Streaming Video ◽  
Live Streaming ◽  
Video Classification

scholarly journals Comparative Study of Document Clustering Algorithms

2018 ◽  
Vol 7 (4.11) ◽  
pp. 246
Author(s):  
N. M. Ariff ◽  
M. A. A. Bakar ◽  
M. I. Rahmad
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Text Clustering ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Meaningful Result ◽  
Different Types

Text clustering is a data mining technique that is becoming more important in present studies. Document clustering makes use of text clustering to divide documents according to the various topics. The choice of words in document clustering is important to ensure that the document can be classified correctly. Three different methods of clustering which are hierarchical clustering, k-means and k-medoids are used and compared in this study in order to identify the best method which produce the best result in document clustering. The three methods are applied on 60 sports articles involving four different types of sports. The k-medoids clustering produced the worst result while k-means clustering is found to be more sensitive towards general words. Therefore, the method of hierarchical clustering is deemed more stable to produce a meaningful result in document clustering analysis. 

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