October 2015 Issue Vol.5 No.10
A Novel Data Classification with Anonymity Method for Privacy Preserving in Medical
Data Mining
https://archive.org/download//v5no1001/vol5no1001.pdf
Dr.C.Senthilkumar
Associate Professor, Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India
P.Kalaiyarasi
M.Phil (Research scholar), Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India
Abstract:
Data mining is the process of extracting interesting patterns or knowledge from huge amount of data. The privacy preserving in data mining comes into picture for security. K-Anonymity is one of the easy and efficient techniques to achieve privacy preserving forsensitive data in many data publishing applications. In Kanonymity
techniques, all tuples of releasing database are generalized to make it anonymized which leads to reduce the data utility and more information loss of publishing
table. To overcome those problems, it needs to propose a model is called Novel Sensitive Class Based Anonymity Method (NSCBA).The proposed the method classifies
sensitive attributes like high sensitive and low sensitive depending upon the sensitive values. Experiment results on the SPARCS medical data sets show the proposed
methods not only can improve the accuracy of the publishing data, but also can preserve privacy, then can increase the data utility and minimum information loss and
also provide privacy with the implementation of ASP.NET.
Keywords:
K-Anonymity, Privacy Preserving, NSCBA,SPARCS, Sensitive data.
Read complete October2015
A Novel Technique for Image Recognitionand Retrieval with Binary Pattern Using
Support Vector Machine
https://archive.org/download//v5no1002/vol5no1002.pdf
Dr.S.Prasath
Assistant Professor, Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India
M.Yasothai
Assistant Professor, Department of Computer Applications, Navarasam Arts & Science College for Women, Erode, Tamil Nadu, India
Abstract:
Today, data mining involves into various fields,but till it is struggling in recognition issues. Recognition and retrieval developed into a very active research area
specializing on how to extract and recognize within images. The recognition and retrieval is a widely used biometric application for security and identification
concern. The various methods have been proposed for recognition and each method has advantages and drawbacks. The complexities in process will affects performance of existing system makes insufficient. In this paper presents recognition and retrieval with geometrical feature vector to calculate the threshold value separately and stored in feature database. The feature is generated and matching is done by Support Vector Machine (SVM) distance classification is used to measure a distance between two images. The experimental result shows that CMBLP method provides better recognition rate when compared with the existing methods such as Local Binary Pattern, Local Directional Pattern Method.
Keywords:
LBP, LDP, CMBLP, GFE, Biometric, SVM.
Read complete October2015
Dr.S.Prasath
Assistant Professor, Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India
M.Yasothai
Assistant Professor, Department of Computer Applications, Navarasam Arts & Science College for Women, Erode, Tamil Nadu, India
Abstract: Today, data mining involves into various fields,but till it is struggling in recognition issues. Recognition and retrieval developed into a very active research area specializing on how to extract and recognize within images. The recognition and retrieval is a widely used biometric application for security and identification concern. The various methods have been proposed for recognition and each method has advantages and drawbacks. The complexities in process will affects performance of existing system makes insufficient. In this paper presents recognition and retrieval with geometrical feature vector to calculate the threshold value separately and stored in feature database. The feature is generated and matching is done by Support Vector Machine (SVM) distance classification is used to measure a distance between two images. The experimental result shows that CMBLP method provides better recognition rate when compared with the existing methods such as Local Binary Pattern, Local Directional Pattern Method.
Keywords: LBP, LDP, CMBLP, GFE, Biometric, SVM.