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Author: search.filters.author.Karpagavalli SEnd date: 2023

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    EXPERT AUTOMATED SYSTEM FOR PREDICTION OF MULTI-TYPE DERMATOLOGY SICKNESSES USING DEEP NEURAL NETWORK FEATURE EXTRACTION APPROACH
    (IJISAE, 2023) Kalaivani A; Karpagavalli S; Kamal, Gulati
    One of the most prevalent illnesses on the planet is skin issues. Due to the complexity of types of skin, and hair types, it is difficult to evaluate it despite its popularity.Consequently, skin conditions pose a serious public health danger. When they reach the invasive stage of evolution, they become harmful. Medical professionals are very concerned about dermatological disorders. The number of people who suffer from skin illnesses is growing substantially as a result of rising pollution and bad food. People frequently ignore the early indications of skin conditions. A hybrid approach can minimize human judgment, producing positive results quickly. A thorough examination suggests that frameworks for recognizingvarious skin disorders may be built using deep learning techniques. To find skin illnesses, it is necessary to distinguish between theskin and non-skin tissue. Through the use of feature extraction-baseddeep neural network approaches, a classification system for skin diseases was established in this study. The main goal of this system is to anticipate skin diseases accurately while also storing all relevant state data efficiently and effectively for precise forecasts. The significant issues have been addressed, and a unique, feature extraction-based deep learning modelis introduced to assist medical professionals in properly detecting the type of skin condition.The pre-processing stage is when the inputdataset is first supplied, helping to clear the image of any undesired elements. Then, for the training phase, the proposed Feature Extraction Based Deep Neural Network (FEB-DNN) is fed the features collected from each of the pre-processed frames. With the use of measured parameters, the classification system categorizesincoming treatment data as various skin conditions. Finding the ideal weight values to minimizetraining error is crucial while learning the proposed framework. In this study, an optimization strategy is used to optimizethe weight in the structure. Based on the feature extraction approach, the suggested multi-type framework for diagnosing skin diseases has a 91.88% of accuracyrate for the HAM image dataset and identifies several skin disorder subtypes than the earlier models thatcan aid in treatment response and decision-makingwhich alsohelp doctors make an informed decision.
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    A REVIEW ON SUB-WORD UNIT MODELING IN AUTOMATIC SPEECH RECOGNITION
    (IOSR Journal of VLSI and Signal Processing, 2016-12) Karpagavalli S; Chandra E
    The primary issue in designing a speech recognition system is the choice of suitable modeling unit. Speech recognition systems may be based on any one of the modeling unit like, word, phoneme and syllable. The selection of sub-word unit depends on many factors such as vocabulary size, complexity of the task, language. Phoneme is the most commonly used sub-word unit in state-of-the-art speech recognition systems, which is an indivisible unit of sound of a particular language. The choice of sub-word units, and the way in which the recognizer represents words in terms of combinations of those units, is the problem of sub-word modeling. This paper explores the various sub-word unit models used in speech recognition and presents the advantages and disadvantages of each sub-word unit.
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    A REVIEW ON AUTOMATIC SPEECH RECOGNITION ARCHITECTURE AND APPROACHES
    (International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016) Karpagavalli S; Chandra E
    Speech is the most natural communication mode for human beings. The task of speech recognition is to convert speech into a sequence of words by a computer program. Speech recognition applications enable people to use speech as another input mode to interact with applications with ease and effectively. Speech recognition interfaces in native language will enable the illiterate/semi-literate people to use the technology to greater extent without the knowledge of operating with computer keyboard or stylus. For more than three decades, a great amount of research was carried out on various aspects of speech recognition and its applications. Today many products have been developed that successfully utilize automatic speech recognition for communication between human and machines. Performance of speech recognition applications deteriorates in the presence of reverberation and even low levels of ambient noise. Robustness to noise, reverberation and characteristics of the transducer is still an unsolved problem that makes the research in the area of speech recognition still very active. A detailed study on automatic speech recognition is carried out and presented in this paper that covers the architecture, speech parameterization, methodologies, characteristics, issues, databases, tools and applications.
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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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    OFFLINE HANDWRITTEN MATHEMATICAL EXPRESSION RECOGNITION
    (International Journal of Innovative Research in Computer and Communication Engineering, 2016-01) Padmapriya R; Karpagavalli S
    Recognition of handwritten mathematical expressions is helpful in writing technical documents as well as useful in converting handwritten documents with mathematical equations into electronic format. Symbol recognition in mathematical expressions is a complex task due to large character set and writer variability in size and style of symbols. In this work, mathematical expression recognition task carried out in different phases which include data collection, preprocessing, segmentation, feature extraction, symbol classification as well as mathematical expression. A set of 50 simple algebraic expressions written by 10 writers, each equation with 10 to 15 symbols converting 23 unique symbols are collected. The expressions are scanned and converted into image files. The images are preprocessed to remove noises, normalize the size and enhance. The symbols in each equation is segmented and features like, zonal, structural, skeleton based, directional are extracted. Multilayer Perceptron (MLP) and Support Vector Machine (SVM) classifiers are used to classify the symbols. The accuracy of symbol classification and whole algebraic expression recognition is analyzed. An interface to automatic mathematical expression recognition is developed with effective classifier.
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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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    A HIERARCHICAL APPROACH IN TAMIL PHONEME CLASSIFICATION USING SUPPORT VECTOR MACHINE
    (Indian Journal of Science and Technology, 2015-12) Karpagavalli S; Chandra E
    Most of the speech recognition systems are designed based on the sub-word unit phoneme which is the basic sound unit of a language. In the proposed work, a novel hierarchical approach based phoneme classification task has been carried out to reduce time complexity and search space. Hierarchical classification of set of Tamil phonemes has been done in three levels. Phoneme boundaries of the given speech utterance are identified using Spectral Transition Measure (STM) and phonemes are separated. Mel-Frequency Cepstral Coefficients (MFCC) are extracted for each phoneme represented by 9 frames including the contextual frames of corresponding phoneme. In each hierarchical level, different number of models is built using Support Vector Machine (SVM) for classifying each phoneme group/phoneme. It is observed from the results that in hierarchical approach phoneme group recognition rate at level 1 and 2 has greatly improved compared to flat classification model. Complexity of search space is significantly reduced at level 2 and level 3 contrasts to flat phoneme classification model. Hierarchical phoneme classifier can be very well employed in phoneme recognition task which is useful in applications such as spoken term detection, out-ofvocabulary detection, named entity recognition, spoken document retrieval.
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    SPEECH EMOTION RECOGNITION USING CLASSIFICATION ALGORITHMS
    (International Journal of Applied Engineering Research (IJAER), 2015) Meenakshi S; Karpagavalli S
    Emotion Recognition from one’s speech is natural activity in human beings. Emotion recognition aims at identifying the emotional state of a speaker from his/her speech signal. The emotion recognition is useful in applications that are lie detection, in car board system, authentication systems and automatic emotional detection in call centers. There are different categories of emotions such as joy, fear, disgust, surprise, anger, sadness, boredom and neutral. In this proposed work, emotional speech files are collected from Berlin Emotional Speech Database (EMO-DB) covering exclusively 3 emotions Neutral, Anger and Sad. Information on emotion is encoded mainly phonetic and acoustic properties of spoken language. Prosodic features and voice quality also infers emotion characteristics. The emotion speech files are processed to extract features like energy, pitch, intensity and Mel-Frequency Cepstral Coefficient (MFCC). Emotion recognizer is designed with classifiers like Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The experiment carried out for male and female speech files with acoustic features separately and acoustic features along with short term spectral features. The performances of the classifiers are evaluated with predictive accuracy.
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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.