Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chandan, N.; Manimekala, B.; Siva Balan, R.V. Title A name given to the resource Enhanced Image Classification using Transfer Learning with ResNet50-V2: A Case Study on Wildlife Recognition Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings;pp.1565-1570 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICMCSI64620.2025.10883359" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMCSI64620.2025.10883359</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000007871?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000007871?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandan N., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Karnataka, Bangalore, 560073, India; Manimekala B., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Karnataka, Bangalore, 560073, India; Siva Balan R.V., Department of Computer Science, CHRIST (Deemed to be University), Yeshwanthpur Campus, Karnataka, Bangalore, 560073, India Description An account of the resource This study explores the application of transfer learning using the ResNet50-V2 architecture for accurate classification of Arctic wildlife species, including Arctic foxes, polar bears, and walruses. Transfer learning leverages pretrained networks to enhance performance in new tasks with limited labeled data, reducing the need for extensive data collection and computational resources. In this work, we utilized a dataset of 1000 labeled images across the three species and applied ResNet50-V2, pre-trained on ImageNet, as a feature extractor. The model achieved high accuracy, with training and validation accuracies nearing 99% and 95-97%, respectively, though minor overfitting was observed. This indicates the model's strong ability to generalize across the dataset while benefiting from pre-trained weights on diverse, non-related images. Additionally it compares with models like SSD and CycleGAN, emphasizing its capability to generalize well, handle small datasets, and mitigate overfitting. We discuss model architecture, data preprocessing, and the experimental results, focusing on improvements achievable through regularization techniques to counteract overfitting. This study demonstrates the effectiveness of transfer learning for wildlife classification, providing insights into optimizing CNNs for ecological and conservation applications. 2025 IEEE. Subject The topic of the resource CNN; CycleGAN; Model Selection; ResNet50-V2; Transfer Learning; wildlife classification Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152266-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online