V. Vijayalakshmi

Work place: Computer Science and Engineering, Annamalai University, Tamilnadu, INDIA

E-mail: vivenan09@gmail.com


Research Interests: Computational Learning Theory, Data Mining, Data Structures and Algorithms


V. Vijayalakshmi received the M.Tech., degree in Computer Science and Engineering from Pondicherry University, Puducherry, India in 2011. She is currently pursuing Ph.D., (Computer Science and Engineering) from Annamalai University Chidambaram, India. She has published about 30 papers in National and International journals and presented several papers in National and International conferences. Her current areas of research interest include Data Mining, Machine Learning Algorithms and Educational Data Mining. She is lifetime member of ISTE and IAENG.

Author Articles
Comparison of Predicting Student’s Performance using Machine Learning Algorithms

By V. Vijayalakshmi K. Venkatachalapathy

DOI: https://doi.org/10.5815/ijisa.2019.12.04, Pub. Date: 8 Dec. 2019

Predicting the student performance is playing vital role in educational sector so that the analysis of student’s status helps to improve for better performance. Applying data mining concepts and algorithms in the field of education is Educational Data Mining. In recent days, Machine learning algorithms are very much useful in almost all the fields. Many researchers used machine learning algorithms only. In this paper we proposed the student performance prediction system using Deep Neural Network. We trained the model and tested with Kaggle dataset using different algorithms such as Decision Tree (C5.0), Naïve Bayes, Random Forest, Support Vector Machine, K-Nearest Neighbor and Deep neural network in R Programming and compared the accuracy of all other algorithms. Among six algorithms Deep Neural Network outperformed with 84% as accuracy.

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Texture Classification Using Complete Texton Matrix

By Y.Sowjanya Kumari V.Vijaya Kumar V. Vijayalakshmi

DOI: https://doi.org/10.5815/ijigsp.2017.10.07, Pub. Date: 8 Oct. 2017

This paper presents a complete image feature representation, based on texton theory proposed by Julesz’s, called as a complete texton matrix (CTM)for texture image classification. The present descriptor can be viewed as an improved version of texton co-occurrence matrix (TCM) [1] and Multi-texton histogram (MTH) [2]. It is specially designed for natural image analysis and can achieve higher classification rate. TheCTM can express the spatial correlation of textons and can be considered as a generalized visual attribute descriptor. This paper initially quantized the original textures into 256 colors and computed color gradient from RGB vector space. Then the statistical information of eleven derived textons, on a 2 x 2 grid in a non-overlapped manner are computed to describe image features more precisely. To reduce the dimensionality the present paper extended the concept of present descriptor and derived a compact CTM (CCTM). The proposed CTM and CCTM methods are extensively tested on the Brodtaz, Outex and UIUC natural images. The results demonstrate the superiority of the present descriptor over the state-of-art representative schemes such as uniform LBP (ULBP), local ternary pattern (LTP), complete –LBP (CLBP), TCM and MTH.

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Other Articles