International Conference

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    A DEEP ENSEMBLE MODEL FOR AUTOMATED MULTICLASS CLASSIFICATION USING DERMOSCOPY IMAGES
    (IEEE, 2022-03-31) Kalaivani A; Karpagavalli S
    In medical diagnosis, manual skin tumor treatment is time consuming and exclusive, it is important to create computerized analytic strategies that can accurately classify skin lesions of many stages. A completely automatic way to classify skin lesions of many categories has been presented. Automatic dissection of skin lesions and isolation are two major and related functions in the diagnosis of computer-assisted skin cancer. Even with their widespread use, deep learning models are typically only intended to execute a single task, neglecting the potential benefits of executing both functions simultaneously. The Bootstrapping Ensembles based Convolutional Neural Networks (BE-CNN) model is proposed in this paper for the separation of skin lesions simultaneously and for classification. A Compute-Intensive Segmentation Network (CI-SN), comprise this model (improved-SN). On one hand, Compute-Intensive Segmentation Network creates uneven lesion covers that serves as a pre-bootstrapping, allowing it to reliably find and classify skin lesions. Both division and arrangement networks, in this approach, mutually transmit assistance and experience each other in a bootstrapping manner. However, to deal with the challenges posed by class inequality and simple pixel inequality, a novel method in segmentation networks is proposed. On the ISIC-HAM 10000 datasets, the proposed BE-CNN model is evaluated and found that it achieves mean skin lesion classification accuracy of 93.8 percentile, which is higher than the function of the separation of skin lesions representing the modern condition and stages techniques. Proposed outcomes demonstrate that via preparing a bound together model to execute the two tasks in a non-stop bootstrapping strategy, it is feasible to work on the presentation of skin sore division and grouping simultaneously.
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    ANALYSIS OF TAMIL CHARACTER WRITINGS AND IDENTIFICATION OF WRITER USING SUPPORT VECTOR MACHINE
    (IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 2014 indexed in IEEE Xplore Digital Library, 2014) Thendral T; Vijaya M S; Karpagavalli S
    Distinctive Handwriting is a thought provoking task in writer identification. The style and shape of the letters written by the same writer may vary and entirely different for different writers. Alphabets in the handwritten text may have loops, crossings, junctions, different directions and so on. Therefore exact prediction of individual based on his/her handwriting is highly complex and challenging task. This paper proposes a new model for learning the writer's identity constructed on Tamil handwriting. Handwritten documents written by the writers are scanned and segmented into words. Words are further segmented into characters for character level writer identification. The character writings in Tamil are analyzed and their describing features are defined. The 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 90. 6% of prediction accuracy by RBF kernel based classification model in character level writer identification.
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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.