Department of Computer Science (UG)

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Start date: 2009Subject: search.filters.subject.Support Vector Machine

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    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.
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    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.
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    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.
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    STOP CONSONANT-SHORT VOWEL (SCSV) CLASSIFICATION FOR TAMIL SPEECH UTTERANCES
    (2016-02) S, Karpagavalli; E, Chandra
    Tamil Language is one of the ancient Dravidian languages spoken in south India. Most of the Indian languages are syllabic in nature and syllables are in the form of Consonant-Vowel (CV) units. In Tamil language, CV pattern occurs in the beginning, middle and end of a word. In this work, CV units formed with Stop Consonant – Short Vowel (SCSV) were considered for classification task. The work carried out in three stages, Vowel Onset Point (VOP) detection, CV segmentation and classification. VOP is an event at which the consonant part ends and vowel part begins. VOPs are identified using linear Prediction residuals which provide significant characteristics of the excitation source. To segment the CV units, fixed length spectral frames before and after VOPs are considered. Both production based features - Linear Predictive Cepstral Coefficients (LPCC) and perception based features - Mel Frequency Cepstral Coefficients (MFCC) are extracted and given as input to the classifiers designed with multilayer perceptron and support vector machine. A speech corpus of 200 Tamil words uttered by 15 native speakers was used, which covers all SCSV units formed with Tamil stop consonants (/k/,/ch/,/d/,/t/,/p/) and short vowels (/a/,/i/, /u/, /e/, /o/). The classifiers are trained and tested for its performance using various measures. The results indicate that the model built with MFCC using support vector machine RBF kernel outperforms.
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    CLASSIFICATION OF LUNG DISEASE USING LOCAL AND GLOBAL DESCRIPTORS
    (International Journal of Computer Applications, 2016-02) Pradeebha R; Karpagavalli S
    Recent trends indicate that instances of chronic respiratory diseases are on the rise in India mainly due to vehicular pollution, air and dust pollution, habit of smoking and also increased population. A World Health Organization report indicates that India has a ranking number one in the world for lung disease deaths. Respiratory diseases like asthma, chronic obstructive pulmonary disease (COPD), Interstitial Lung Disease (ILD), pneumonia, tuberculosis (TB) are emerging as most important health problems in the country. The proposed work is aimed at establishing more advanced diagnostic strategy for lung diseases using CT scan images. Lung diseases such as Emphysema, Pneumonia, Bronchitis are classified using CT scan images which is collected from National Biomedical Imaging Archive (NBIA). A total of 366 images are used, out of which 300 images are used for training and 66 images are used for testing. The classification task carried out with classifier support vector machine (SVM) using Histogram of Oriented Gradient (HOG) –global descriptors and Local Binary Pattern (LBP) – local descriptors. The performance of the model built using Support Vector Machine indicates that it is effective in the prediction of lung disease with 98% predictive accuracy.
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    PREDICTION OF LUNG DISEASE USING HOG FEATURES AND MACHINE LEARNING ALGORITHMS
    (Innovative Research in Computer and Communication Engineering, 2016-01) Pradeeba R; Karpagavalli S
    Lung diseases are the one that mostly affects large number of people in the world. A sharp rise in respiratory disease in India due to infection, smoking and air pollution in the country. Respiratory diseases were no longer restricted to the elderly but were now being detected even in younger age groups. The early and correct diagnosis of any pulmonary disease is mandatory for timely treatment and prevent mortality. From a clinical standpoint, medical diagnosis tools and systems are of great importance. The proposed work is aimed at establishing more advanced diagnostic strategy for lung diseases using CT scan images. The three types of lung disease Emphysema, Pneumonia, Bronchitis are considered in this work. A dataset with 126 CT scan images of Emphysema, 120 CT scan images of Pneumonia and 120 CT scan images of Bronchitis are collected from National Biomedical Imaging Archive (NBIA) database. The classification of lung disease using Histogram of Oriented Gradients (HOG) features is carried out using classifiers Naive Bayes (NB), Decision tree (J48), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The performance of the models is compared for its predictive accuracy and the results are presented.
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    RECOGNITION OF TAMIL SYLLABLES USING VOWEL ONSET POINTS WITH PRODUCTION, PERCEPTION BASED FEATURES
    (ICTACT Journal on Soft Computing, 2016-01) Karpagavalli S; Chandra E
    Tamil Language is one of the ancient Dravidian languages spoken in south India. Most of the Indian languages are syllabic in nature and syllables are in the form of Consonant-Vowel (CV) units. In Tamil language, CV pattern occurs in the beginning, middle and end of a word. In this work, CV Units formed with Stop Consonant – Short Vowel (SCSV) were considered for classification task. The work carried out in three stages, Vowel Onset Point (VOP) detection, CV segmentation and classification. VOP is an event at which the consonant part ends and vowel part begins. VOPs are identified using linear prediction residuals which provide significant characteristics of the excitation source. To segment the CV units, fixed length spectral frames before and after VOPs are considered. In this work, production based features, Linear Predictive Cepstral Coefficients (LPCC) and perception based features, Perceptual Linear Predictive Cepstral Coefficients (PLP) and Mel Frequency Cepstral Coefficients (MFCC) are extracted which are used to build the SCSV classifier using multilayer perceptron and support vector machine. A speech corpus of 200 Tamil words uttered by 15 native speakers was used, which covers all SCSV units formed with Tamil stop consonants (/k/, /ch/, /d/, /t/, /p/) and short vowels (/a/, /i/, /u/, /e/, /o/). The classifiers are trained and tested for its performance using predictive accuracy measure. The results indicate that perception based features, MFCC and PLP provides better results than production based features, LPCC and the model built using support vector machine outperforms.
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    BREAST CANCER CLASSIFICATION USING SUPPORT VECTOR MACHINE AND GENETIC PROGRAMMING
    (International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), 2013-09) Menaka K; Karpagavalli S
    Breast cancer is one of the most leading causes of death among women. The early detection of abnormalities in breast enables the radiologist in diagnosing the breast cancer easily. Efficient tools in diagnosing the cancerous breast will help the medical experts in accurate diagnosis and timely treatment to the patients. In this work, experiments carried out using Wisconsin Diagnosis Breast Cancer database to classify the breast cancer either benign or malignant. Supervised learning algorithm Support Vector Machine (SVM) with kernels like Linear, Polynomial and Radial Basis Function and evolutionary algorithm Genetic Programming are used to train the models. The performance of the models are analysed where genetic programming approach provides more accuracy compared to Support Vector Machine in the classification of breast cancer and seems to be an fast and efficient method.
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    DISCOVERING TAMIL WRITER IDENTITY USING GLOBAL AND LOCAL FEATURES OF OFFLINE HANDWRITTEN TEXT
    (International Review on Computers and Software (IRECOS), 2013) Thendral T; Vijaya M S; Karpagavalli S
    Writer identification is the process of identifying the individual based on their handwriting. Handwriting exhibits behavioral characteristics of an individual and has been considered as unique. The style and shape of the letters written vary slightly for same writer and entirely different for different writers. Also alphabets in the handwritten text may have loops, crossings, junctions, different directions etc. Hence accurate prediction of individual based on his/her handwriting is highly complex and challenging task. This paper proposes a new model for discovering the writer’s identity based on Tamil handwriting. 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 93.8% of prediction accuracy by RBF kernel based classification model.
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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