International Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/164

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Start date: 2009End date: 2009

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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
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    MACHINE LEARNING APPROACH FOR PREOPERATIVE ANAESTHETIC RISK PREDICTION
    (Academy Publishers, Finland, 2009-05) Karpagavalli S; Jamuna K S; Vijaya M S
    Risk is ubiquitous in medicine but anaesthesia is an unusual speciality as it routinely involves deliberately placing the patient in a situation that is intrinsically full of risk. Patient safety depends on management of those risks; consequently, anaesthetist has been at the forefront of clinical risk management. Anaesthetic risk classification is of prime importance not only in carrying out the day-to-day anaesthetic practice but coincides with surgical risks and morbidity condition. The preoperative assessment is made to identify the patients risk level based on American Society of Anesthesiologists (ASA) score that is widely used in anaesthetic practice. This helps the anaesthetist to make timely clinical decision. Machine Learning techniques can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care. This paper presents the implementation of three supervised learning algorithms, C4.5 Decision tree classifier, Naive Bayes and Multilayer Perceptron in WEKA environment, on the preoperative assessment dataset. The classification models were trained using the data collected from 362 patients. The trained models were then used for predicting the anaesthetic risk of the patients. The prediction accuracy of the classifiers was evaluated using 10-fold cross validation and the results were compared.