Department of Computer Science (UG)

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Author: search.filters.author.Karpagavalli SStart date: 2009

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    MULTI-CLASS CLASSIFICATION OF INSECTS USING DEEP NEURAL NETWORKS
    (IEEE Xplore, 2023-01-23) Santhiya M; Priyadharshini M; Agshalal Sheeba J; Karpagavalli S
    Insects are crucial to the functioning of nature. There are more than a million described species of living beings in the modern world. Since the majority of today’s farmers and agriculturalists are newer generations of people, identifying and classifying insects is essential. The classification of insects is a difficult undertaking in the agricultural industry. In the proposed work, multi-class classification of insects using a Convolutional Neural Network architecture, VGG19 had been carried out. In the taxonomic classification of insects, 5 insects fall within insecta class which include butterfly, dragonfly, grasshopper, ladybird, and mosquito data had been collected to train, test, and validate the convolutional neural network, The performance of the model had been analyzed using different parameters and presented.
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    BOOTSTRAPPING OF FINE-TUNED SEGMENTATION AND CLASSIFICATION NETWORK FOR EPIDERMIS DISORDER CATEGORIZATION
    (Springer Link, 2023-07-25) Kalaivani A; Karpagavalli S
    As all people have been affected by various skin related illnesses, categorization of skin disorders has become prominent in recent healthcare system. To identify and categorize skin related syndromes, many transfer learning frameworks were used. Amongst, a Fine-tuned Segmentation and Classification Network (F-SegClassNet) achieved better efficacy by using the novel unified loss function. Nonetheless, it was not apt for the datasets that lack in training images. Hence in this article, Bootstrapping of F-SegClassNet (BF-SegClassNet) model is proposed which solves the imbalanced images in the training set via generating the group of pseudo balanced training batches relying on the properties of the considered skin image dataset. This model fits the distinct abilities of Deep Convolutional Neural Network (DCNN) classifier so that it is highly useful for classifying the skin disorder image dataset with a highly imbalanced image data distribution. According to the Bootstrpping, better tradeoff between simple and complex image samples is realized to make a network model that is suitable for automatic skin disorders classification. In this model, statistics across the complete training set is calculated and a new subset is produced that retains the most essential image samples. So, the skin images are segmented and categorized by this new model to identify the varieties of epidermis infections. At last, the testing outcomes exhibits BF-SegClassNet-model accomplishes the mean accuracy with 96.14% for HAM dataset which is compared to state-of-the-art models.
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    A SURVEY AND ANALYSIS OF DEEP LEARNING TECHNIQUES FOR BIRD SPECIES CLASSIFICATION
    (IEEE Xplore, 2023-06-16) Sivaranjani B; Karpagavalli S
    The ability to accurately identify the species of a bird in an image is crucial. A bird’s species identification can be accomplished using images and audios. In earlier periods, the audio of birds are utilized to possibly recognize the different species of birds. But, background noise from things like birds, insects, and the wind makes it difficult for this method to produce a reliable result. Comparatively, observer’s finds images are better than audios. Using images, people are better able to discriminate between birds. However, because of the inexperience of most bird watchers and the similarity of bird forms and backgrounds, identifying birds can be difficult. To address this, Deep Learning (DL) models have been implemented to efficiently extract features from photos collected for recognition. DL models for bird species identification provides more accuracy. The recently proposed transfer learning and spatial pyramid pooling efficiently classify bird spicies. Another recently proposed Mask-CNN based method solved few shot classifcation problem effectively. But, both of these method are suffered to distinguish the subcategory of spicies form main categories. In this article, the of bird species identification techniques are studied in brief to encourage further research in this field. First, the review is planned to investigate the DL algorithms for identifying the different bird species types. Next, the merits and demerits of every algorithms are analyzed based on its performance. Finally, potential improvements are emphasized to achieve greater efficiency in identifying the bird species.
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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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    DEEP NEURAL NETWORK OPTIMIZATION FOR SKIN DISEASE CLASSIFICATION FORECAST ANALYSIS
    (IEEE, 2022-03-26) Kalaivani A; Karpagavalli S
    Skin lesions are a prevalent condition that causes misery, many of which can be severe, for millions of individuals worldwide. Consequently, Deep learning seems to be an increasingly popular approach in recent years, and it may be a strong tool in difficult, earlier domains, specifically in health science, which is now dealing with a number of medical resources. In this paper, presented an interactive dermoscopy images diagnosis framework based on an gathering of intelligent deep learning model system for image classification to make advances their person accuracies within the prepare of classifying dermoscopy pictures into several classes such as melanoma, keratosis and nevus when we have not sufficient annotated images to train them on. We integrate the classification layer results for two distinct deep neural network designs to obtain excellent classification accuracy. More precisely, we combining robust convolutional neural networks (CNNs) into a unified structure, with the final classification relying on the weighted outcome of the respective CNNs by predictive ensemble methods and fine-tuning classifiers utilizing ISIC2019 images. Furthermore, the outliers and the substantial class imbalance are handled in order to improve the categorization of the disease. The experimental reveal that the framework produced result that are comparable to other models of conventional art. A substantial improvement in accuracy of 96.2 percentage indicated the efficiency of the proposed Predictive Ensemble Deep Convolutional Neural Networks Classifier (PE-DCNN Classifier) model and this study effectively built a system with all the important features.
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    A DEEP ENSEMBLE MODEL FOR AUTOMATED MULTICLASS CLASSIFICATION USING DERMOSCOPY IMAGES
    (IEEE, 2022-03-31) Kalaivani A; Karpagavalli S
    In medical diagnosis, manual skin tumor treatment is time consuming and exclusive, it is important to create computerized analytic strategies that can accurately classify skin lesions of many stages. A completely automatic way to classify skin lesions of many categories has been presented. Automatic dissection of skin lesions and isolation are two major and related functions in the diagnosis of computer-assisted skin cancer. Even with their widespread use, deep learning models are typically only intended to execute a single task, neglecting the potential benefits of executing both functions simultaneously. The Bootstrapping Ensembles based Convolutional Neural Networks (BE-CNN) model is proposed in this paper for the separation of skin lesions simultaneously and for classification. A Compute-Intensive Segmentation Network (CI-SN), comprise this model (improved-SN). On one hand, Compute-Intensive Segmentation Network creates uneven lesion covers that serves as a pre-bootstrapping, allowing it to reliably find and classify skin lesions. Both division and arrangement networks, in this approach, mutually transmit assistance and experience each other in a bootstrapping manner. However, to deal with the challenges posed by class inequality and simple pixel inequality, a novel method in segmentation networks is proposed. On the ISIC-HAM 10000 datasets, the proposed BE-CNN model is evaluated and found that it achieves mean skin lesion classification accuracy of 93.8 percentile, which is higher than the function of the separation of skin lesions representing the modern condition and stages techniques. Proposed outcomes demonstrate that via preparing a bound together model to execute the two tasks in a non-stop bootstrapping strategy, it is feasible to work on the presentation of skin sore division and grouping simultaneously.
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    MULTI-LABEL CLASSIFICATION- PROBLEM TRANSFORMATION METHODS IN TAMIL PHONEME CLASSIFICATION
    (In Proceedings of 7th International Conference on Advances in Computing & Communications (ICACC August 2017), Kochi, Elsevier Procedia Computer Science., 2017) Pushpa M; Karpagavalli S
    Most of the supervised learning task has been carried out using single label classification and solved as binary or multiclass classification problems. The hierarchical relationship among the classes leads to Multi- Label (ML) classification which is learning from a set of instances that are associated with a set of labels. In Tamil language, phonemes fall into different categories according to place and manner of articulation. This motivates the application of multi-label classification methods to classify Tamil phonemes. Experiments are carried out using Binary Relevance (BR) and Label Powerset (LP) and BR’s improvement Classifier Chains (CC) methods with different base classifiers and the results are analysed.
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    ANALYSIS OF TAMIL CHARACTER WRITINGS AND IDENTIFICATION OF WRITER USING SUPPORT VECTOR MACHINE
    (IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 2014 indexed in IEEE Xplore Digital Library, 2014) 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
    (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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    PHONEME AND WORD BASED MODEL FOR TAMIL SPEECH RECOGNITION USING GMM-HMM
    (International Conference on Advanced Computing & Communication Systems, held at Sri Eshwar College of Engineering, Coimbatore during 5-7 January 2015 and published in the conference proceedings, indexed in IEEE Xplore Digital Library., 2015-01-05) Karpagavalli S; Chandra E
    Speech is the standard means of communication among people. Automatic Speech Recognition (ASR) applications facilitate the users to interact with machines through speech and perform their tasks effortlessly. Speech Recognition applications in native languages will enable illiterate and semi-illiterate people to use computer services without any/little knowledge to operate computers and to lead better life. In the proposed work, speaker independent isolated- phoneme and word recognition systems have been developed for the Indian regional language Tamil. The Hidden Markov Tool Kit (HTK) was used for developing speaker independent phoneme and word based Tamil speech recognition system. The work involves main tasks like Feature Extraction, Acoustic Model Building and Decoding. Mel-Frequency Cepstral Coefficients (MFCC) is extracted from the speech utterances and Hidden Markov Model (HMM) used to build the acoustic model. In building acoustic model, Multivariate Gaussian Mixture Model with different number of components is used to estimate the state emission probabilities and finally Viterbi Decoder employed to recognize the test speech utterances. A small vocabulary of 50 words which are collected from 10 native speakers of Tamil language was used to build and test the model. The performance of both phoneme and word based models have been analyzed and the recognition accuracy and word error rate of the models are discussed.