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Articles by D. Pugazhenthi
Total Records ( 4 ) for D. Pugazhenthi
  D. Pugazhenthi and S.P. Rajagopalan
  Drug discovery refers to the finding of a new drug which could be a completely new compound or a new derivative of existing compounds. Drug discovery is the ultimate goal of drug design which concerned with the design of a chemical compound that exhibits a desired pharmacological activity. Machine learning tools, in particular Support Vector Machines (SVM), Particle Swarm Optimisation (PSO) and Genetic Programming (GP), are increasingly used in pharmaceuticals research and development. They are inherently suitable for use with noisy, high dimensional data, as is commonly used in cheminformatic, bioinformatics and other types of drug research studies. These aspects are demonstrated via review of their current usage and future prospects in context with drug discovery activities.
  A. Jayasudha and D. Pugazhenthi
  In this study, a method for classification of texture images using patch based energy features is proposed. The ability of Discrete Wavelet Transform (DWT) to capture the texture properties of given image is exploited. First, the texture image is decomposed by using DWT. Then the proposed patch based energy features are extracted from the selected wavelet coefficients in each sub-band based on the edge intensities. The performance of the system is evaluated by varying the decomposition level, No. of selected wavelet coefficients and size of the patch used to extract the energy features. Brodatz album is used for the proposed classification task. The performance of the system is analyzed with other state of art techniques and the results are tabulated. Experimental results show the efficiency of the proposed system in terms of classification accuracy and better accuracy of over 99% is obtained.
  K. Priya and D. Pugazhenthi
  In this study, a new decision based morpho filter is proposed for denoising images that are highly corrupted images by salt and pepper noise. The main problem of de-noising is how to keep the poise between degrading image noise and preserving image edge information. Hence, the main aim is to construct a de-noising algorithm which not only eliminate the noises but also preserves image edge information. The algorithm replaces the noisy pixels by morphological operations. Experiments are carried out on benchmark images such as Lena, Barbara, Baboon and peppers. A competitive denoising is achieved in comparison with Standard Median Filter (SMF), Adaptive Median Filter (AMF) and Decision Based Algorithm (DBA).
  D. Pugazhenthi and S.P. Rajagopalan
  Problem statement: Activities of drug molecules can be predicted by Quantitative Structure Activity Relationship (QSAR) models, which overcome the disadvantage of high cost and long cycle by employing traditional experimental methods. With the fact that number of drug molecules with positive activity is rather fewer than that with negatives, it is important to predict molecular activities considering such an unbalanced situation. Approach: Asymmetric bagging and feature selection was introduced into the problem and Asymmetric Bagging of Support Vector Machines (AB-SVM) was proposed on predicting drug activities to treat unbalanced problem. At the same time, features extracted from structures of drug molecules affected prediction accuracy of QSAR models. Hybrid algorithm named SPRAG was proposed, which applied an embedded feature selection method to remove redundant and irrelevant features for AB-SVM. Results: Numerical experimental results on a data set of molecular activities showed that AB-SVM improved AUC and sensitivity values of molecular activities and SPRAG with feature selection further helps to improve prediction ability. Conclusion: Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which could be furthermore improved by performing feature selection to select relevant features from the drug.
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