Informatics Publishing Limited

M. Suresh Kumar ¹, N. Kamalraj ¹

Affiliations
1 Department of Computer Technology, Dr. SNS Rajalakshmi College of Arts & Science, Coimbatore-49, TN, IN

Abstract

Security break and transaction fraud is increasing, so that in a daily secure identification and personal verification technology came into picture. Every Organization needs to protect their secured information from either internal or external threat. Various biometric techniques have been developed for having secured information in the organization. Recently, Biometric techniques are integrated with the cryptographic technique for better security and authentication. This paper concentrates on the integration of biometric features with cryptographic techniques for better security and authentication using the retinal biometrics. Retina is considered as the most accurate and secure biometric feature. The retinal feature is extracted based on the steps like Extraction of Retinal Vascular Tree, Thinning and Joining, Feature Extraction. Reed-Solomon (RS) error correcting algorithm is utilized to encrypt and decrypt the data. The performance of the proposed approach is compared with the existing approach which uses iris feature and the results confirms that the proposed approach has better FAR (False Acceptance Ratio) and FRR (False Reject Ratio).

Keywords

Security, Retinal Vascular Tree, Reed-Solomon (RS), FAR (False Acceptance Ratio) and FRR (False Reject Ratio).

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K. Sheela ¹, N. Kamalraj ²

Affiliations
1 Dr. SNS Rajalakshmi College of Arts and Science, IN
2 Dept. of Computer Technology, Dr. SNS Rajalakshmi College of Arts and Science, IN

Abstract

In Data mining clustering is one of the important tools. Several research areas clustering is to be used and it describe the method for grouping the data. Describes the K-Means clustering algorithm and it has used the best validity index (Calinski index) for the attribute selection, having the value of the validity index as fitness function. Calinski index is to find the best number of clusters for the whole data set. The method is to study the maximum value max_k of i_k (where k is the number of clusters and i_k is the Calinski index value for k clusters). Number of cluster can be calculated by using Calinski index values along with NMF. The rand index value and the accuracy for the Calinski value is obtained, which proves that rand index value and accuracy is better than the existing clusters.

Keywords

K-Means Clustering, Calinski Index Value, Rand Index Value, WBC Dataset.

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N. Kamalraj ¹, V. Shobana ²

Affiliations
1 Department in Computer Technology, Dr. SNS Rajalakshmi College of Arts and Science, Tamil Nadu, IN
2 Dr. SNS Rajalakshmi College of Arts and Science, Tamil Nadu, IN

Abstract

Clustering is a technique adapted in many real world applications. Generally clustering can be thought of as partitioning the data into group or subsets, which contain analogous objects. A lot of clustering techniques like K-Means algorithm, Fuzzy C-Means algorithm (FCM), spectral clustering algorithm and so on has been proposed earlier in literature. Recently, clustering algorithms are extensively used for mixed data types to evaluate the performance of the clustering techniques. This paper presents a survey on various clustering algorithms that are proposed earlier in literature. Moreover it provides an insight into the advantages and limitations of some of those earlier proposed clustering techniques. The comparison of various clustering techniques is provided in this paper. The future enhancement section of this paper provides a general idea for improving the existing clustering algorithms to achieve better clustering accuracy.

Keywords

Artificial Intelligence, Clustering, Mixed Dataset, Learning Algorithm, Image Processing.

Full Text

     

M. Suresh Kumar ¹, N. Kamalraj ¹

Affiliations
1 Department of Computer Technology, Dr. SNS Rajalakshmi College of Arts and Science, IN

Abstract

Security break and transaction fraud is increasing, so that in a daily secure identification and personal verification technology came into picture. Every Organization needs to protect their secured information from either internal or external threat. Various biometric techniques have been developed for having secured information in the organization. Recently, Biometric techniques are integrated with the cryptographic technique for better security and authentication. This paper concentrates on the integration of biometric features with cryptographic techniques for better security and authentication using the retinal biometrics. Retina is considered as the most accurate and secure biometric feature. The retinal feature is extracted based on the steps like Extraction of Retinal Vascular Tree, Thinning and Joining, Feature Extraction. Reed-Solomon (RS) error correcting algorithm is utilized to encrypt and decrypt the data. The performance of the proposed approach is compared with the existing approach which uses iris feature and the results confirms that the proposed approach has better FAR (False Acceptance Ratio) and FRR (False Reject Ratio).

Keywords

Security, Retinal Vascular Tree, Reed-Solomon (RS), FAR (False Acceptance Ratio) and FRR (False Reject Ratio).

Full Text