m) 2012-Scopus Open Access (PDF)

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    PERFORMANCE EVALUATION OF SEMANTIC BASED AND ONTOLOGY BASED TEXT DOCUMENT CLUSTERING TECHNIQUES (Conference Paper)
    (Elsevier, 2012) Punitha, S.C; Punithavalli, M
    The amount of digital information is created and used is steadily growing along with the development of sophisticated hardware and software. This has increased the need for powerful algorithms that can interpret and extract interesting knowledge from these data. Data mining is a technique that has been successfully exploited for this purpose. Text mining, a category of data mining, considers only digital documents or text. Text Clustering is the process of grouping text or documents such that the document in the same cluster are similar and are dissimilar from the one in other clusters. This paper studies the working of two sophisticated algorithms. The first work is a hybrid method that combines pattern recognition process with semantic driven methods for clustering documents, while the second uses an ontology-based approach to cluster documents. Through experiments, the performance of both the selected algorithms is analyzed in terms of clustering efficiency and speed of clustering.
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    EFFICIENT PREDICTION OF PHISHING WEBSITES USING SUPERVISED LEARNING ALGORITHMS (Conference Paper)
    (Elsevier B.V, 2012-12-09) Lakshmi V, Santhana; Vijaya, M S
    Phishing is one of the luring techniques used by phishing artist in the intention of exploiting the personal details of unsuspected users. Phishing website is a mock website that looks similar in appearance but different in destination. The unsuspected users post their data thinking that these websites come from trusted financial institutions. Several antiphishing techniques emerge continuously but phishers come with new technique by breaking all the antiphishing mechanisms. Hence there is a need for efficient mechanism for the prediction of phishing website. This paper employs Machine-learning technique for modelling the prediction task and supervised learning algorithms namely Multi layer perceptron, Decision tree induction and Naïve bayes classification are used for exploring the results. It has been observed that the decision tree classifier predicts the phishing website more accurately when comparing to other learning algorithms.
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    IN VITRO ANTIBACTERIAL ACTIVITY OF HIBISCUS ROSA–SINENSIS FLOWER EXTRACT AGAINST HUMAN PATHOGENS (Article)
    (Elsevier, 2012-05) Ruban, P; Gajalakshmi, K
    To access the in vitro antibacterial activity of Hibiscus rosa–sinensis H. rosa-sinensis) flower extract against human pathogens.
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    INVESTIGATION OF BENZOTHIAZOLE DERIVATIVES AS CORROSION INHIBITORS FOR MILD STEEL (Article)
    (Portugaliae Electrochimica Acta, 2012-04-30) Parameswari, K; Chitra, S; Selvaraj, A; Brindha, S; Menaga, M
    The influence of benzothiazole derivatives on corrosion inhibition of mild steel in 1 M H2SO4 was studied by weight loss, potentiodynamic polarization and AC-impedance techniques. The synergistic effect by the addition of halide ions had been studied. The experimental results showed that the inhibition efficiency increases with increasing inhibitor concentration, but decreases with increasing temperature; potentiodynamic polarization curves showed that benzothiazole derivatives acted as cathodic inhibitors in 1 M H2SO4. This was supported by the impedance measurements which showed a change in the charge transfer resistance and double layer capacitance, indicating adsorption of Benzothiazole derivatives on the mild steel surface. Atomic absorption spectroscopy studies showed that the inhibition efficiency increases with increasing inhibitor concentration.