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Xiaoqing Gu
Changzhou University

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Fuzzy c-Means and Mean Shift Algorithm for 3DPoint Clouds Denoising Tongguang Ni; Xiaoqing Gu; Hongyuan Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 7: July 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i7.pp5546-5551

Abstract

In  many  applications,  denoising  is  necessary  since  point-sampled  models  obtained  by  laser  scanners  with  insufficient  precision.  An  algorithm  for  pointsampled surface is presented, which combines fuzzy c-means clustering with mean shift filtering algorithm. By using fuzzy c-means clustering, the large-scale noise is deleted  and  a  part  of  small-scale  noise  also  is  smooth.  The  cluster  centers  are regarded  as  the  new  points.  After  acquiring  new  point  sets  being  less  noisy,  the remains noise  is smooth by mean shift  method. Experimental results demonstrate that the algorithm can produce a more accurate point-sample model efficiently while having better feature preservation.
Online Imbalanced Support Vector Machine for Phishing Emails Filtering Xiaoqing Gu; Tongguang Ni; Wei Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 6: June 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Phishing emails are a real threat to internet communication and web economy. In real-world emails datasets, data are predominately composed of ham samples with only a small percentage of phishing ones. Standard Support Vector Machine (SVM) could produce suboptimal results in filtering phishing emails, and it often requires much time to perform the classification for large data sets. In this paper, an online version of imbalanced SVM (OISVM) is proposed. First an email is converted into 20 features which are well selected based on its content and link characters. Second, OISVM is developed to optimize the classification accuracy and reduce computation time, which is used a novel method to adjust the separation hyperplane of imbalanced date sets and an online algorithm to make the retaining process much fast. Compared to the existing methods, the experimental results show that OISVM can achieve significantly using a proposed expressive evaluation method. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.4562
Co-Authors Hongyuan Wang Tongguang Ni Wei Wang