ICTACT Journal on Soft Computing

K. Saminathan ¹, T. Chakravarthy ², M. Chithra Devi ³

Affiliations
1 Department of Computer Science and Engineering, Ponnaiyah Ramajayam Institute of Science and Technology University, IN
2 Department of Computer Science, A. Veeriya Vandayar Memorial Sri Pushpam College, IN
3 Department of Software Engineering, Periyar Maniammai University, IN

Abstract

Ensuring security biometrically is essential in most of the authentication and identification scenario. Recognition based on iris patterns is a thrust area of research cause to provide reliable, simple and rapid identification system. Machine learning classification algorithm of support vector machine [SVM] is applied in this work for personal identification. The profuse as well as unique patterns of iris are acquired and stored in the form of matrix template which contains 4800 elements for each iris. The row vectors of 2400 elements are passed as inputs to SVM classifier. The SVM generates separate classes for each user and performs matching based on the template's unique spectral features of iris. The experimental results of this proposed work illustrate a better performance of 98.5% compared to the existing methods such as hamming distance, local binary pattern and various kernels of SVM. The popular CASIA (Chinese Academy of Sciences - Institute of Automation) iris database with fifty users' eye image samples are experimented to prove, that the least Square method of Quadratic kernel based SVM is comparatively better with minimal true rejection rate.

Keywords

Iris Preprocessing, Iris Template, Quadratic Kernel, Support Vector Machine, Hamming, Local Binary Pattern.

Full Text

     

K. Kalyani ¹, T. Chakravarthy ²

Affiliations
1 Department of Computer Science, T.U.K. Arts College, IN
2 Department of Computer Science, A. Veeriya Vandayar Memorial Sri Pushpam College, IN

Abstract

The chief aim of this paper is to develop an effective approach to the issue of load profile clustering by applying Improved Replacement In Bee Optimization algorithm (IRIBO). While, intelligent metering solutions like Automated Meter Reading (AMR), Automated Meter Infrastructure (AMI) are in place to address the current issues prevailing in the domain of electricity markets, algorithm using Improved Replacement In Bee Optimization has been proved beneficial and uncomplicated to apply within a selective database. In this study Load Profile (LP) clustering distribution networks based on the shape of the load profile was studied for fitness function in the selected LP clustering. The results clearly indicate that LP clustering has advantages in providing metering solutions to consumers who do not possess digital metering which can be easily operated with trivial changes in the calibrations.

Keywords

Load Profiling, Honey Bee Modeling, Improved Replacement In Bee Optimization Algorithm, Clustering Techniques.

Full Text

     

T. Chakravarthy ¹, K. Meena ², D. Nathiya ¹

Affiliations
1 A. V. V. M. Sri Pushpam College, Bharathidasan University, Tamil Nadu, IN
2 Shrimati Indira Gandhi College, Bharathidasan University, Tamil Nadu, IN

Abstract

Fingerprint classification based on statistical and structural (RNN and SVM) approach. RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in this support vector machine. SVMs are combined with a new error correcting codes scheme. This approach has two main advantages. (a) It can tolerate the presence of ambiguous fingerprint images in the training set and (b) It can effectively identify the most difficult fingerprint images in the test set. In this experiment on the fingerprint database NIST-4 (National Institute of Science and Technology), our best classification accuracy of 94.7% is obtained by training SVM on both fingerCode and RNN -extracted futures of segmentation algorithm which has used very sophisticated "region growing process".

Keywords

Support Vector Machine, Recursive Neural Network, Region Growing, Error Correction Code.

Full Text

     

S. Prakash ¹, T. Chakravarthy ¹, K. Kalyani ²

Affiliations
1 Department of Computer Science, AVVM Sri Pushpam College, Tamil Nadu, IN
2 Department of Computer Science, T.U.K Arts College, Tamil Nadu, IN

Abstract

Blogs and Web services agree to express user's opinions and interests, in the form of small text messages which gives abbreviated and highly personalized remarks in real-time. Recognizing emotion is really significant for a text-based communication tool such as blogs. Nowadays, user opinions in the structure of comments, reviews in blogs have been utilized by researchers for various purposes. Among them the application of sentiment analysis techniques to these opinions is an interesting one. This paper deals with a proposal of a software structural design for constructing Web mining applications in the blog world. The design includes blog crawling and data mining algorithms, to offer a full-fledged and flexible key for constructing general-purpose Web mining applications. The structural design allocates some significant customizations, such as the construction of adapters for reading text from different blogs, and the utilization of different pre-processing methods and data mining procedures. The core of this paper is on explaining the innovative software structural design of the general framework offering thorough information about the data mining sub-framework.

Keywords

Blog Mining, Emotional Analysis, Web Crawler, Text Extraction.

Full Text