n) 2012 - 17 Documents

Permanent URI for this communityhttps://dspace.psgrkcw.com/handle/123456789/3994

Browse

Author: search.filters.author.Vijaya, M S

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    EFFICIENT PREDICTION OF PHISHING WEBSITES USING SUPERVISED LEARNING ALGORITHMS (Conference Paper)
    (Elsevier Ltd, 2012) Santhana Lakshmi, V; 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.
  • No Thumbnail Available
    Item
    LABEL SEQUENCE LEARNING BASED PROTEIN SECONDARY STRUCTURE PREDICTION USING HYDROPHOBICITY SCALES (Conference Paper)
    (Springer Link, 2012) Vinodhini, R; Vijaya, M S
    Proteins are complex molecules, each comprised of its own combination of twenty different amino acids. Protein secondary structure is a polypeptide that has formed an arrangement of amino acids that are located next to one another in a linear fashion. Protein secondary structure prediction refers to the prediction of the conformational state of each amino acid residue of a protein sequence as one of the three possible states, namely helices, strands, or coils, denoted as H, E, and C, respectively. Protein sequence is the only resource that provides the information to survive denaturing process, so it is essential to find the secondary structure of a protein sequence. The existing methodology uses only one hydrophobicity scale called Kyte-Doolittle whereas in this paper three scales such as, Kyte-Doolittle scale, Hopp-Woods scale and Rose scale are used for protein secondary structure prediction. This Paper formulates secondary structure prediction task as sequence labeling and a new coding scheme is introduced with multiple windows to predict secondary structure of proteins using hydrophobicity scales. Protein sequences with their physical and chemical properties are learned using SVMhmm that creates a learned model, which is then used to predict protein secondary structure of an unknown primary sequence. It is reported 77.11% accuracy based on Q3 measures, when SVMhmm is used.
  • Thumbnail Image
    Item
    STOCK PRICE PREDICTION USING SUPPORT VECTOR REGRESSION (Conference Paper)
    (Springer Link, 2012) Abirami, R; Vijaya, M S
    Forecasting stock price is an important task as well as difficult problem. Stock price prediction depends on various factors and their complex relationships. Prediction of stock price is an important issue in finance. Stock price prediction is the act of trying to determine the future value of a company stock. The successful prediction of a stock future price could yield significant profit. Hence an efficient automated prediction system is highly essential for stock forecasting. This paper demonstrates the applicability of support vector regression, a machine learning technique, for predicting the stock price by learning the historic data. The stock data for the period of four years is collected and trained with various parameter settings. The performance of the trained model is evaluated by 10-fold cross validation for its predictive accuracy. It has been observed that the support vector regression model with RBF kernel shows better performance when compared with other models.