Department of Computer Science (UG)

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Author: search.filters.author.Vijaya M SSubject: search.filters.subject.Support Vector Machine

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    AN INTERACTIVE TOOL FOR YARN STRENGTH PREDICTION USING SUPPORT VECTOR REGRESSION
    (CPS and indexed in Thompson CSI, 2010) Selvanayaki M; Vijaya M S; Jamuna K S
    Cotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance. The physical characteristics of cotton such as fiber length, length distribution, trash value, color grade, strength, shape, tenacity, density, moisture absorption, dimensional stability, resistance, thermal reaction, count, etc., contributes to determine the quality of cotton and in turn yarn strength. In this paper yarn strength prediction has been modeled using regression. Support Vector regression, the supervised machine learning technique has been employed for predicting the yarn strength. The trained model was evaluated based on mean squared error and correlation coefficient and was found that the prediction accuracy of SVR based model, the intelligence reasoning method is higher compared with the traditional statistical regression, the least square regression model.
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    DISCOVERING TAMIL WRITER IDENTITY USING GLOBAL AND LOCAL FEATURES OF OFFLINE HANDWRITTEN TEXT
    (International Review on Computers and Software (IRECOS), 2013) Thendral T; Vijaya M S; Karpagavalli S
    Writer identification is the process of identifying the individual based on their handwriting. Handwriting exhibits behavioral characteristics of an individual and has been considered as unique. The style and shape of the letters written vary slightly for same writer and entirely different for different writers. Also alphabets in the handwritten text may have loops, crossings, junctions, different directions etc. Hence accurate prediction of individual based on his/her handwriting is highly complex and challenging task. This paper proposes a new model for discovering the writer’s identity based on Tamil handwriting. Writer identification problem is formulated as classification task and a pattern classification technique namely Support Vector Machine has been employed to construct the model. It has been reported about 93.8% of prediction accuracy by RBF kernel based classification model.
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    A NOVEL APPROACH FOR PASSWORD STRENGTH ANALYSIS THROUGH SUPPORT VECTOR MACHINE
    (Academy Publishers, Finland, 2009-11) Karpagavalli S; Jamuna K S; Vijaya M S
    Passwords are ubiquitous authentication methods and they represent the identity of an individual for a system. Users are consistently told that a strong password is essential these days to protect private data. Despite the existence of more secure methods of authenticating users, including smart cards and biometrics, password authentication continues to be the most common means in use. Thus it is important for organizations to recognize the vulnerabilities to which passwords are subjected, and develop strong policies governing the creation and use of passwords to ensure that those vulnerabilities are not exploited. This work employs machine Learning technique to analyze the strength of the password to facilitate organizations launch a multi-faceted defense against password breach and provide a highly secure environment. A supervised learning algorithm namely Support Vector Machine is used for classification of password. The linear and nonlinear SVM classification models are trained using the features extracted from the password dataset. The trained model shows the prediction accuracy of about 98% for 10-fold cross validation