2.Conference Paper (08)

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Author: search.filters.author.Karpagavalli, S

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    AUTOMATIC TAG RECOMMENDATION FOR JOURNAL ABSTRACTS USING STATISTICAL TOPIC MODELING
    (Springer Link, 2015) Anupriya, P; Karpagavalli, S
    Topic modeling is a powerful technique for unsupervised analysis of large document collections. Topic models conceive latent topics in text using hidden random variables, and discover that structure with posterior inference. Topic models have a wide range of applications like tag recommendation, text categorization, keyword extraction and similarity search in the broad fields of text mining, information retrieval, statistical language modeling.In this work, a dataset with 200 abstracts fall under four topics are collected from two different domain journals for tagging journal abstracts. The document model is built using LDA (Latent Dirichlet Allocation) with Collapsed Variational Bayes (CVB0) and Gibbs sampling. Then the built model is used to find appropriate tag for a given abstract. An interface is designed to extract and recommend the tag for a given abstract.
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    DIABETIC RETINAL EXUDATES DETECTION USING EXTREME LEARNING MACHINE
    (Springer Link, 2015) Asha, P R; Karpagavalli, S
    Diabetic Retinopathy is a disorder of the retina as a result of the impact of diabetes on the retinal blood vessels. It is the major cause of blindness in people like age groups between 20 & 60. Since polygenic disorder proceed, the eyesight of a patient may commence to deteriorate and causes blindness. In this proposed work, the existence or lack of retinal exudates are identified using Extreme Learning Machine(ELM). To discover the occurrence of exudates features like Mean, Standard deviation, Centroid and Edge Strength are taken out from Luv color space after segmenting the Retinal image. A total of 100 images were used, out of which 80 images were used for training and 20 images were used for testing. The classification task carried out with classifier extreme learning machine (ELM). An experimental result shows that the model built using Extreme Learning Machine outperforms other two models and effectively detects the presence of exudates in retina.
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    ANALYSIS OF TAMIL CHARACTER WRITINGS AND IDENTIFICATION OF WRITER USING SUPPORT VECTOR MACHINE
    (IEEE, 2015-01-26) 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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    TAMIL PHONEME CLASSIFICATION USING CONTEXTUAL FEATURES AND DISCRIMINATIVE MODELS
    (IEEE, 2015-11-12) Karpagavalli, S; Chandra, E
    The speech recognition systems may be designed based on any one of the sub-word unit phoneme, tri-phone and syllable. The phonemes are a set of base-forms for representing the unique sounds in a particular language. In supervised phoneme classification, the segmentation of phoneme, features and class label are given and the goal is to classify the phoneme. Phoneme classification and recognition can be useful in applications such as spoken document retrieval, named entity extraction, out-of-vocabulary detection, language identification, and spoken term detection. In trained speech, each phoneme occurs clearly in speech waveform. In spontaneous speech, due to co-articulation effect, influence of adjacent phonemes is present in each phoneme where left and right context frame information plays vital role in accurate phoneme classification. In the proposed work, three discriminative classifiers like Multilayer Perceptron, Naive Bayes and Support Vector Machine are used to classify 25 phonemes of Tamil language. The approximate boundaries of phoneme identified using Spectral Transition Measure (STM). After segmentation, Mel Frequency Cepstral Co-Efficient (MFCC) of 9 frames including 4 left context frames, 1 centre frame corresponding to the phoneme and 4 right context frames are extracted and used as input to classifiers. Tamil word dataset prepared to cover 25 phonemes of the language. The performance of the classifiers are analysed and results are presented.
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    DIABETIC RETINAL EXUDATES DETECTION USING MACHINE LEARNING TECHNIQUES
    (IEEE, 2015-11-12) Asha, P R; Karpagavalli, S
    Diabetic Retinopathy (DR) is an eye filled illness caused by the complication of polygenic disease and that is to be detected accurately for timely treatment. As polygenic disease progresses, the vision of a patient could begin to deteriorate and leads to blindness. In this proposed work, the presence or absence of retinal exudates are detected using machine learning (ML) techniques. To detect the presence of exudates features like Mean, Standard deviation, Centroid and Edge Strength are extracted from Luv color space after segmenting the Retinal image. A total of 100 images were used, out of which 80 images were used for training and 20 images were used for testing. The classification task carried out with classifiers like Naive bayes (NB), Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM). Experimental results shows that the model built using Extreme Learning Machine outperforms other two models and effectively detects the presence of exudates in retinal images.
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    LDA BASED TOPIC MODELING OF JOURNAL ABSTRACTS
    (IEEE, 2015-11-12) Anupriya, P; Karpagavalli, S
    Topic modeling is a powerful technique for unsupervised analysis of large document collections. Topic models conceive latent topics in text using hidden random variables, and discover that structure with posterior inference. Topic models have a wide range of applications like tag recommendation, text categorization, keyword extraction and similarity search in the broad fields of text mining, information retrieval, statistical language modeling. In this work, a dataset with 200 abstracts fall under four topics are collected from two different domain journals for tagging journal abstracts. The document models are built using LDA (Latent Dirichlet Allocation) with Collapsed Variational Bayes and Gibbs sampling. Then the built model is used to extract appropriate tags for abstracts. The performance of the built models are analyzed by the evaluation measure perplexity and observed that Gibbs sampling outperforms CV B0 sampling. Tags extracted by two algorithms remains almost the same.