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@article{198034,
        author = {Tanuja Kodali},
        title = {Advanced Bird Species Classification Using Convolutional Neural Networks and Transfer Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {8571-8579},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198034},
        abstract = {Many people find solace in visiting bird sanctuaries, where they may observe various birds and appreciate their unique characteristics and colors. Data about species is a cornerstone of biodiversity protection efforts. Because there are so many similarities between bird species, both within and between classes, it can be quite challenging to determine which bird species you're looking at. Convolutional neural networks (CNNs) and transfer learning models like VGG16, VGG19, ResNet-50, ResNet-101, Inception-V3, DenseNet, MobileNet, and EfficientNet have recently been useful. Quite a few cutting-edge algorithms for picture classification have accomplished outstanding results. Improving a deep learning platform for image-based bird species recognition has been the focus of this article. For both classification and prediction, a convolutional neural network is utilized. A paradigm geared at skip connections in neural networks is being presented to enhance feature extraction. For the training image, the suggested technique attained a classification accuracy of 99.00%. The suggested method makes it easy for inexperienced bird watchers to identify the species of birds in a photograph.},
        keywords = {Transfer Learning, Deep Learning, Pretrained Models, Convolutional neural network (CNN) · Bird species · VGG-19, ResNet-50, Inception-V3, DenseNet, MobileNet, EfficientNet.},
        month = {April},
        }