International Conference

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Author: search.filters.author.Karpagavalli SStart date: 2020

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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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    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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    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.