F-KCW-Department Publications

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Author: search.filters.author.Jamuna K SSubject: search.filters.subject.Classification

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    CLASSIFICATION OF SEED COTTON YIELD BASED ON THE GROWTH STAGES OF COTTON CROP USING MACHINE LEARNING TECHNIQUES
    (IEEE Xplore and IEEE CS Digital Library, 2010-06) Jamuna K S; Karpagavalli S; Vijaya M S; Revathi P; Gokilavani S; Madhiya E
    Cotton, popularly known as "White Gold" has been an important commercial crop of national significance due to the immense influence of its rural economy. Cotton seed is an important and critical link in the chain of agricultural activities extending farmer industry linkage. Cotton yield is associated with high quality seed as the seed contains in itself the blue print for the agrarian prosperity in incipient form. Transfer of technology to identify the quality of seeds is gaining importance. Hence this work employs machine learning approach to classify the quality of seeds based on the different growth stages of the cotton crop. Machine learning techniques - Naïve Bayes Classifier, Decision Tree Classifier and Multilayer Perceptron were applied for training the model. Features are extracted from a set of 900 records of different categories to facilitate training and implementation. The performance of the model was evaluated using 10 -fold cross validation. The results obtained show that Decision Tree Classifier and Multilayer Perceptron provides the same accuracy in classifying the seed cotton yield. The time taken to build the model is higher in Multilayer Perceptron as compared to the Decision Tree Classifier.
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    PASSWORD STRENGTH PREDICTION USING SUPERVISED MACHINE LEARNING TECHNIQUES
    (International Conference on Advances in Computing, Control and Telecommunication Technologies, ACT 2009 archived in IEEE Xplore and IEEE CS Digital Library., 2009) Karpagavalli S; Jamuna K S; Vijaya M S
    Passwords are a vital component of system security. Though there are many alternatives to passwords for access control, password is the more compellingly authenticating the identity in many applications. They provide a simple, direct means of protecting a system and they represent the identity of an individual for a system. The big vulnerability of passwords lies in their nature. Users are consistently told that a strong password is essential these days to protect private data as there are so many ways for an unauthorized person with little technical knowledge or skill to learn the passwords of legitimate users. 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. In this work password strength prediction is modeled as classification task and supervised machine learning techniques were employed. Widely used supervised machine learning techniques namely C 4.5 decision tree classifier, multilayer perceptron, naive Bayes classifier and support vector machine were used for learning the model. The results of the models were compared and observed that SVM performs well. The results of the models were also compared with the existing password strength checking tools. The findings show that machine learning approach has substantial capability to classify the extreme cases - Strong and weak passwords.
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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.