2.Conference Paper (06)

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    PERFORMANCE EVALUATION OF SEMANTIC BASED AND ONTOLOGY BASED TEXT DOCUMENT CLUSTERING TECHNIQUES (Conference Paper)
    (Elsevier Ltd, 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 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.
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    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.
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    AN EFFICIENT LEAF RECOGNITION ALGORITHM FOR PLANT CLASSIFICATION USING SUPPORT VECTOR MACHINE (Conference Paper)
    (IEEE, 2012-05-31) Arun Priya, C; Balasaravanan, T; Antony Selvadoss, Thanamani
    Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. This paper uses an efficient machine learning approach for the classification purpose. This proposed approach consists of three phases such as preprocessing, feature extraction and classification. The preprocessing phase involves a typical image processing steps such as transforming to gray scale and boundary enhancement. The feature extraction phase derives the common DMF from five fundamental features. The main contribution of this approach is the Support Vector Machine (SVM) classification for efficient leaf recognition. 12 leaf features which are extracted and orthogonalized into 5 principal variables are given as input vector to the SVM. Classifier tested with flavia dataset and a real dataset and compared with k-NN approach, the proposed approach produces very high accuracy and takes very less execution time.
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    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.
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    ISOLATED TAMIL DIGIT SPEECH RECOGNITION USING TEMPLATE-BASED AND HMM-BASED APPROACHES (Conference Paper)
    (Springer Link, 2012) Karpagavalli, S; Deepika, R; Kokila, P; Usha Rani, K; Chandra, E
    For more than three decades, a great amount of research was carried out on various aspects of speech signal processing and its applications. Highly successful application of speech processing is Automatic Speech Recognition (ASR). Early attempts to ASR consisted of making deterministic models of whole words in a small vocabulary and recognizing a given speech utterance as the word whose model comes closest to it. The introduction of Hidden Markov Models (HMMs) in the early 1980 provided much more powerful tool for speech recognition. And the recognition can be done for continuous speech using large vocabulary, in a speaker independent manner. Two approaches like conventional template-based and Hidden Markov Model usually performs speaker independent isolated word recognition. In this work, speaker independent isolated Tamil digit speech recognizers are designed by employing template based and HMM based approaches. The results of the approaches are compared and observed that HMM based model performs well and the word error rate is greatly reduced.