Cracking Sex Trafficking: Data Analysis, Pattern Recognition, and Path Prediction

2020 ◽  
Author(s):  
Burcu B. Keskin ◽  
Gregory J. Bott ◽  
Nickolas K. Freeman
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Sex Trafficking ◽  
Analysis Pattern ◽  
Path Prediction

scholarly journals A Wavelet Relational FuzzyC-Means Algorithm for 2D Gel Image Segmentation

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Shaheera Rashwan ◽  
Mohamed Talaat Faheem ◽  
Amany Sarhan ◽  
Bayumy A. B. Youssef
Keyword(s):  
Pattern Recognition ◽  
Image Segmentation ◽  
Data Analysis ◽  
The Other ◽  
Fetal Alcohol ◽  
High Quality ◽  
Analysis Pattern ◽  
Segmentation Quality ◽  
2D Gel

One of the most famous algorithms that appeared in the area of image segmentation is the FuzzyC-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the FuzzyC-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhance its performance especially in the area of 2D gel images. Both proposed modifications aim to minimize the oversegmentation error incurred by previous algorithms. The experimental results of comparing both the FuzzyC-Means (FCM) and the Wavelet FuzzyC-Means (WFCM) to the proposed algorithm on real 2D gel images acquired from human leukemias, HL-60 cell lines, and fetal alcohol syndrome (FAS) demonstrate the improvement achieved by the proposed algorithm in overcoming the segmentation error. In addition, we investigate the effect of denoising on the three algorithms. This investigation proves that denoising the 2D gel image before segmentation can improve (in most of the cases) the quality of the segmentation.

THE “KVAZAR” PACKAGE FOR PATTERN RECOGNITION AND ITS APPLICATIONS

1993 ◽  
Vol 03 (04) ◽  
pp. 439-444 ◽  
Author(s):  
VLADIMIR S. KAZANTSEV
Keyword(s):  
Experimental Data ◽  
Pattern Recognition ◽  
Data Analysis ◽  
Linear Inequalities ◽  
Processing Data ◽  
Experimental Data Analysis ◽  
Ibm Pc

The package of applied programs named KVAZAR has been elaborated to be used for classification, diagnostic, predicative, experimental data analysis problems. The package may be used in medicine, biology, geology, economics, engineering and some other problems. The algorithmical base of the package is the method of pattern recognition, based on the linear inequalities and committee constructions. Other algorithms are used too. The package KVAZAR is intended to be used with IBM PC AT/XT. The range of processing data is bounded by 40,000 numbers.

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