f) 2019-Scopus Open Access (PDF)

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/3889

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Subject: search.filters.subject.Kernels

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    LINEAR KERNEL WITH WEIGHTED LEAST SQUARE REGRESSION CO-EFFICIENT FOR SVM BASED TAMIL WRITER IDENTIFICATION
    (Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), 2019-07) Thendral, Tharmalingam; Vijaya, Vijayakumar
    Tamil writer identification is the task of identifying writer based on their Tamil handwriting. Our earlier work of this research based on SVM implementation with linear, polynomial and RBF kernel showed that linear kernel attains very low accuracy compared to other two kernels. But the observation shows that linear kernel performs faster than the other kernels and also it shows very less computational complexity. Hence, a modified linear kernel is proposed to enrich the performance of the linear kernel in recognizing the Tamil writer. Weighted least square parameter estimation method is used to estimate the weights for the dot products of the linear kernel. SVM implementation with modified linear kernel is carried out on different text images of handwriting at character, word and paragraph levels. Comparing the performance with linear kernel, the modified kernel with weighted least square parameter reported promising results.
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    AFFINITY PREDICTION USING MUTATED PROTEIN-LIGAND DOCKING WITH REGRESSION TECHNIQUES OF SCA
    (Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), 2019-07) Asha, P R; Vijaya, M S
    Drug discovery for rare genetic disorder like spinocerebellar ataxia is very complicated in biomedical research. Numerous approaches are available for drug design in clinical labs, but it is time consuming. There is a need for affinity prediction of spinocerebellar ataxia, which will help in facilitating the drug design. In this work, the proteins are mutated with the information available from HGMD database. The repeat mutations are induced manually, and that mutated proteins are docked with ligand. The model is trained with extricated features such as energy profiles, rf-score, autodock vina scores, cyscore and sequence descriptors. Regression techniques like linear, polynomial, ridge, SVM and neural network regression are implemented. The predictive models are built with various regression techniques and the predictive model implemented with support vector regression is compared with support vector regression kernel. Among all regression techniques, SVR performs well than the other regression models.