2.Conference Paper (08)

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/4204

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Author: search.filters.author.Vijaya, M S

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    GRAPH CUT BASED SEGMENTATION METHOD FOR TAMIL CONTINUOUS SPEECH
    (Springer Link, 2016-11-23) Laxmi Sree, B R; Vijaya, M S
    Automatic segmentation of continuous speech plays an important role in building promising acoustic models for a standard continuous speech recognition system. This needs a lot of segmented data which is rarely available for many languages. As there are no industry standard speech segmentation tools for Indian languages like Tamil, there arises a need to work on Tamil speech segmentation. Here, a segmentation algorithm that is based on Graph cut is proposed for automatic phonetic level segmentation of continuous speech. Using graph cut for speech segmentation allows viewing speech globally rather locally which helps in segmentation of vocabulary, speaker independent speech. The input speech is represented as a graph and the proposed algorithm is applied on it. Experiments on the speech database comprising utterances of various speakers shows the proposed method outperforms the existing methods Blind Segmentation using Non-Linear Filtering and Non-Uniform Segmentation using Discrete Wavelet Transform.
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    COMMUNITY DETECTION BASED ON GIRVAN NEWMAN ALGORITHM AND LINK ANALYSIS OF SOCIAL MEDIA
    (Springer Link, 2016-11-23) Sathiyakumari, K; Vijaya, M S
    Social networks have acquired much attention recently, largely due to the success of online social networking sites and media sharing sites. In such networks, rigorous and complex interactions occur among numerous one-of-a-kind entities, main to massive statistics networks with notable enterprise capacity. Community detection is an unsupervised learning task that determines the community groups based on common interests, occupation, modules and their hierarchical organization, using the information encoded in the graph topology. Finding communities from the social network is a difficult task because of its topology and overlapping of different communities. In this research, the Girvan-Newman algorithm based on Edge-Betweenness Modularity and Link Analysis (EBMLA) is used for detecting communities in networks with node attributes. The twitter data of the well-known cricket player is used right here and community of friends and fans is analyzed based on three exclusive centrality measures together with a degree, betweenness, and closeness centrality. Also, the strength of extracted communities is evaluated based on modularity score using proposed method and the experiment results confirmed that the cricket player’s network is dense.
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    PREDICTING MUSCULAR DYSTROPHY WITH SEQUENCE BASED FEATURES FOR POINT MUTATIONS
    (IEEE, 2016-03-17) Sathyavikasini, K; Vijaya, M S
    Hefty amounts of biological data are accumulated for research with the advancement of sequencing technologies. Genetic diseases are caused by the deformity in the inherited genes. Identifying trait diseases through DNA analysis is a prime task in diagnosing an ailment. Identification of disease based on mutations in the gene sequences is an essential and challenging task in the medical diagnosis of genetic disorders such as Muscular dystrophy. Muscular dystrophy is a rare disease that alters the structure and nature of the muscles that deteriorate the musculoskeletal system and hinder locomotion. There are nine major kinds of muscular dystrophy and it is vital to identify the type of muscular dystrophy for proper diagnosis and treatment. Hence a new model is proposed for predicting the disease accurately with the gene sequences, which are mutated by adopting an approach like positional cloning on the reference cDNA sequence. This paper addresses the problem by considering mutated gene sequences of fifty five genes that causes five types of muscular dystrophy and developing an efficient pattern recognition model using supervised pattern classification technique. The resultant the trained model shows the prediction accuracy of 100% by estimating using 10-fold cross validation.