International Conference

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/162

Browse

Subject: search.filters.subject.Support Vector Machine

Search Results

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    TAMIL PHONEME CLASSIFICATION USING CONTEXTUAL FEATURES AND DISCRIMINATIVE MODELS
    (International Conference on Communication and Signal Processing (ICCSP’15), Adhiparasakthi Engineering College, Melmaruvathur, indexed in IEEE Xplore Digital Library, 2015, 2015) 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.
  • No Thumbnail Available
    Item
    ELECTROCARDIOGRAM BEAT CLASSIFICATION USING SUPPORT VECTOR MACHINE AND EXTREME LEARNING MACHINE
    (Springer, 2014) Banu Priya C V; Karpagavalli S
    The Electrocardiogram (ECG) is of significant importance in assessing patients with abnormal activity in their heart. ECG Recordings of the patient taken for analyzing the abnormality and classify what type of disorder present in the heart functionality. There are several classes of heart disorders including Premature Ventricular Contraction (PVC), Atrial Premature beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Paced Beat (PB), and Atrial Escape Beat (AEB).To analyze ECG various feature extraction methods and classification algorithms are used. The proposed work employed discrete wavelet transform (DWT) in feature extraction on ECG signals obtained from MIT-BIH Arrhythmia Database. The Machine Learning Techniques, Support Vector Machine (SVM) and Extreme Learning Machine (ELM) have been used to classify four types of heart beats that include PVC, LBBB, RBBB and Normal. The Performance of the classifiers are analyzed and observed that ELM-Radial Basis Function Kernel taken less time to build model and out performs SVM in predictive accuracy.
  • No Thumbnail Available
    Item
    AN INTERACTIVE TOOL FOR YARN STRENGTH PREDICTION USING SUPPORT VECTOR REGRESSION
    (CPS and indexed in Thompson CSI, 2010) Selvanayaki M; Vijaya M S; Jamuna K S
    Cotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance. The physical characteristics of cotton such as fiber length, length distribution, trash value, color grade, strength, shape, tenacity, density, moisture absorption, dimensional stability, resistance, thermal reaction, count, etc., contributes to determine the quality of cotton and in turn yarn strength. In this paper yarn strength prediction has been modeled using regression. Support Vector regression, the supervised machine learning technique has been employed for predicting the yarn strength. The trained model was evaluated based on mean squared error and correlation coefficient and was found that the prediction accuracy of SVR based model, the intelligence reasoning method is higher compared with the traditional statistical regression, the least square regression model.
  • No Thumbnail Available
    Item
    AN EFFICIENT LEAF RECOGNITION ALGORITHM FOR PLANT CLASSIFICATION USING SUPPORT VECTOR MACHINE
    (Periyar University, Salem., 2012-03-21) Arunpriya C; Balasaravanan T; Antony Selvadoss Thanamani
    Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. This paper uses an efficient machine learning approach for the classification purpose. This proposed approach consists of three phases such as preprocessing, feature extraction and classification. The preprocessing phase involves a typical image processing steps such as transforming to gray scale and boundary enhancement. The feature extraction phase derives the common DMF from five fundamental features. The main contribution of this approach is the Support Vector Machine (SVM) classification for efficient leaf recognition. 12 leaf features which are extracted and orthogonalized into 5 principal variables are given as input vector to the SVM. Classifier tested with flavia dataset and a real dataset and compared with k-NN approach, the proposed approach produces very high accuracy and takes very less execution time.
  • No Thumbnail Available
    Item
    AN EFFICIENT CANCER CLASSIFICATION USING EXPRESSIONS OF VERY FEW GENES USING SUPPORT VECTOR MACHINE
    (Sun College of Engineering and Technology, Nagercoil, 2011-03-24) Arunpriya C; Balasaravanan T; Antony Selvadoss Thanamani
    Gene expression profiling by microarray technique has been effectively utilized for classification and diagnostic guessing of cancer nodules. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data. Though, these techniques have not been proposed to deal with the particular needs of gene microarray examination. Initially, microarray data is featured by a high-dimensional feature space repeatedly surpassing the sample space dimensionality by a factor of 100 or higher. Additionally, microarray data contains a high degree of noise. The majority of the existing techniques do not sufficiently deal with the drawbacks like dimensionality and noise. Gene ranking method is later introduced to overcome those problems. Some of the widely used Gene ranking techniques are T-Score, ANOVA, etc. But those techniques will sometimes wrongly predict the rank when large database is used. To overcome these issues, this paper proposes a technique called Enrichment Score for ranking purpose. The classifier used in the proposed technique is Support Vector Machine (SVM). The experiment is performed on lymphoma data set and the result shows the better accuracy of classification when compared to the conventional method.