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Author: search.filters.author.Karpagavalli SSubject: search.filters.subject.Deep learning

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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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    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.