Informatics Publishing Limited

B. Thendral Hepsibha ¹, S. Sathiya ², C. Saravana Babu ³, V. Premalakshmi ⁴, T. Sekar

Affiliations
1 Dept. of Biotechnology, Alpha Arts and Science College, Chennai – 600 116, IN
2 Centre for Toxicology and Developmental Research, Sri Ramachandra University, Chennai – 600 116, IN
3 Dept. of Horticulture, Agricultural College and Research Institute, Madurai – 625 104, IN
4 Dept. of Botany, Pachaiyappa’s College, Chennai – 600 030, IN

Abstract

The leaves of Azima tetracantha (Salvadoraceae) commonly known as "mulluchangu" is best known for its medicinal properties. In the present study, the antioxidant potency of successive extracts (petroleum ether, hexane, ethylacetate&methanol) was investigated, by determining the secondary metabolites such as flavonoids, tannin and phenolic compounds. The free radical scavenging activity of various solvent extracts of the leaves was assessed by using 1, 1-diphenyl-2-picrylhydrazyl (DPPH), nitric oxide radical (NO), superoxide anion, hydroxyl radical and lipid peroxide generating models. Reducing power and Vit E level in the leaves were also measured. The results show that the methanol extract of the plant A. tetracantha contains significant quantities of the antioxidant principles compared to the other solvent extracts. Similar observations were seen in scavenging the free radicals by the methanolic extract of the leaves of A. tetracantha. Thus the therapeutic property of the leaves of the plant A. tetracantha can be attributed to the antioxidant principles which scavenge the free radicals responsible for pathological severity.

Keywords

Azima tetracantha, Phytochemicals, Antioxidants, Free Radical Scavenger

Full Text

   

S. Sathiya ¹, V. P. Dhivya ¹

Affiliations
1 Department of Information Technology, K.S. Rangasamy College of Technology, Tamilnadu, IN

Abstract

Clustering Ensemble combines the several partitions generated by different clustering algorithm into single clustering solution. The optimization-based method is proposed for the combination of cluster ensembles for the class of problems with intracluster criteria, such as Minimum-Sum-of-Squares-Clustering (MSSC). To find the solution for MSSC problem we are using simple and efficient algorithm called improved Exact Method for cluster ensemble re-clustering algorithm which uses similarity measures and distance between the weak clusters. The solution obtained by the single clustering algorithm does not provide better solution. The solution obtained by this algorithm guarantees better solutions than the ones in the individual cluster. For the MSSC problem in particular, a prototype implementation of improved Exact Method for cluster ensemble algorithm will produce a new better solution. The algorithm is particularly effective when the number of clusters is large, in which case it is able to escape the local minima found by K-means type algorithms by recombining the solutions in a Set-Covering context. The stability of the algorithm is also establish by running this algorithm several times for the same clustering problem instance, produce high-quality solutions. Finally, in experiments utilize external criteria to compute the validity of clustering. The algorithm is capable of producing high-quality results that are comparable in quality to those of the best known clustering algorithms.

Keywords

Clustering Ensemble, MSSC, K-Means Algorithm, Set Covering Context.

Full Text

     

S. Sathiya ¹, V. Rathikarani ¹, M. Balasubramanian ¹, P. Dhanalakshmi ¹

Affiliations
1 Annamalai University, Annamalai Nagar-608002, IN

Abstract

This paper helps the neurologists to diagnose and determine weather a patient is epileptic or non epileptic. EEG standard is an acronym for Electroencephalogram has been traditionally used to diagnose the brain activity of a patient which corresponds to epilepsy and other brain disorders. The features are extracted using medical data signals and SVM is trained using the extracted signals. This paper focuses on developing a classification technique called SVM for multichannel EEG recordings. Traditional SVMs are used to exploit both spatial and temporal characteristics of EEG data. The proposed technique is tested on two EEG data sets acquired from ten patients respectively SVM is based on the principle of structural risk minimization and it can be used for non linear regression. This study is a necessary application of data mining to advance the diagnosis and treatment of human epilepsy. The performance system achieves the identification rate of 96.0% for 10 subjects.

Keywords

Electroencephalogram (EEG) Classification, Epilepsy Diagnosis, Discrete Wavelet Transform and Support Vector Machine.

Full Text

     

M. Mubarak Ali ¹, S. Sathiya ¹

Affiliations
1 Department of Chemistry, Chikkaiah Naicker College, Erode 638 004, IN

Abstract

This study offers a comprehensive characterization of anodic surface layers formed in sodium oxalate electrolyte and systematically analyses the influence of addition of sodium silicate. X-ray diffraction, scanning electron microscope–EDAX and atomic force microscope studies are used to establish the correlation between electrolyte composition and anodized aluminium (Al). Stable anodic Al layers comprising tailored porosity and surface characteristics are achieved under optimized conditions, which exhibit corrosion current density (I_corr: 1.04 × 10^–5 μA cm^–2) with high corrosion resistance of 7.03 × 10² Ω cm^–2. Effect of anodization variables upon surface morphology, kinetic parameters, microstructural characteristics and thickness of anodized Al is evaluated. Gradient distribution of Si and O also indicates the relative content of silicate as illustrated by EDAX. Interpreted results from electrochemical impedance spectra and Tafel analysis are well-matched with physico-chemical analysis. Stable anodic Al layers comprising tailored morphology with high corrosion resistance are achieved under optimized conditions.

Keywords

Aluminium, Anodization, Corrosion Resistance, Sodium Oxalate, Sodium Silicate.

Full Text