scholarly journals PC-Filter: A Robust Filtering Technique for Duplicate Record Detection in Large Databases

2004 ◽  
pp. 486-496 ◽  
Author(s):  
Ji Zhang ◽  
Tok Wang Ling ◽  
Robert M. Bruckner ◽  
Han Liu
Keyword(s):  
Robust Filtering ◽  
Large Databases ◽  
Filtering Technique

Modification of Robust Filtering of Stratified Surface Topography

2013 ◽  
Vol 20 (1) ◽  
pp. 107-118 ◽  
Author(s):  
Paweł Dobrzański ◽  
Paweł Pawlus
Keyword(s):  
Surface Topography ◽  
Surface Texture ◽  
Digital Filtering ◽  
Robust Filtering ◽  
3D Surface ◽  
Special Procedure ◽  
Surface Topographies ◽  
Filtering Technique ◽  
The Difference

Abstract Various components of surface texture are identified, namely form, waviness and roughness. Separation of these components is done by digital filtering. Several problems exist during analysis of two-process surfaces. Therefore the Gaussian robust profile filtering technique was established and has been studied here. The computer generated 2D profiles and 3D surface topographies having triangular scratches as well as measured stratified surfaces were subjected to filtration. However even robust filter applications cause distortion of profiles having valleys wider than 100 μm. In order to minimize the distortion associated with wide and deep valleys, the robust filter should be modified. A special procedure was elaborated for minimizing distortion of roughness profiles caused by filtration. Application of this method to analyses of several profiles was presented. The difference between 1-D and 2-D filtering of surface topography using the same kind of filter was discussed. As a result we found that modification of a 2-D surface topography filter was not necessary.

Converging Evidence for Developmental Trauma Disorder: Empirical Support From Large Databases

2008 ◽  
Author(s):  
Bradley C. Stolbach ◽  
Frank Putnam ◽  
Melissa Perry ◽  
Karen Putnam ◽  
William Harris ◽  
...  
Keyword(s):  
Empirical Support ◽  
Developmental Trauma ◽  
Developmental Trauma Disorder ◽  
Large Databases

The Use if GIS Tools in Tourism Research on the Example of Wodzislaw Poviat

2012 ◽  
Vol 1 (1) ◽  
pp. 51-56
Author(s):  
Katarzyna Pukowiec
Keyword(s):  
Land Development ◽  
Graphic Representation ◽  
Infrastructure Development ◽  
Tourism Research ◽  
Gis Tools ◽  
Large Databases ◽  
Tourist Infrastructure ◽  
Tourist Development ◽  
Gis Software

Abstract The activities in name of tourist development in Wodzislaw poviat are the reason to evaluate the tourist land development. The evaluation was prepared on the basis of selected indexes characterizing the level of tourist infrastructure development. It considered: the number of lodgings per km2, the number of restaurants per km2, the amount of additional attractions per km2 and the density of tourist tracks. This database was analyzed by the use of GIS tools. Using GIS software allowed working with large databases and provided the possibility to create a graphic representation of the results. The level of tourist land development is diversified and depends on it function. The cities with the best developed tourist infrastructure are Wodzislaw Slaski, Radlin, Pszow, Rydultowy and town in Odra Valley: Olza, Bukow and Nieboczowy. Pszow, Gorzyce and Godow commons have the biggest density of tourist tracks.

Alias Filtering Technique in Alias Resolution

2009 ◽  
Vol 20 (8) ◽  
pp. 2280-2288
Author(s):  
Hong-Hua ZHAO ◽  
Hua-Li BAI ◽  
Ming CHEN ◽  
Zhen-Han WEI
Keyword(s):  
Filtering Technique

LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification

2019 ◽  
Vol 15 (2) ◽  
pp. 227-236
Author(s):  
D. Franklin Vinod ◽  
V. Vasudevan
Keyword(s):  
Big Data ◽  
Medical Image ◽  
Research Study ◽  
Big Data Analytics ◽  
Learning Framework ◽  
Image Set Classification ◽  
Filtering Technique ◽  
Image Set ◽  
Hidden Patterns ◽  
Global Data

Background: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing. Methods: This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images. Results: The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally. Conclusion: The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.

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