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 Vijayalakshmi, A.; Shanmugavadivu, P.; Vijayalakshmi, S.; Sivaranjani, R. Title A name given to the resource A Comprehensive Meta-Analysis on Animal Identification Using Machine Learning and Deep Learning 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 Lecture Notes in Networks and Systems;Volume;1364 LNNS;pp.501-514 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5220-4_41" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5220-4_41</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013020510?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013020510?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 Vijayalakshmi A., Department of Computer Science and Applications, Gandhigram Rural Institute (Deemed to be University), Dindigul, India; Shanmugavadivu P., Department of Computer Science and Applications, Gandhigram Rural Institute (Deemed to be University), Dindigul, India; Vijayalakshmi S., School of Sciences, Christ University, Pune, India; Sivaranjani R., Veterinary College and Research Institute, Namakkal, India Description An account of the resource Artificial Intelligence (AI)-based models have shown promising results in the identification of animals breeds. The surge in the development of new models has opened up new avenues for computer vision. The growing need to achieve cent percent accuracy in the prediction, identification and classification of data/images has motivated researchers to develop innovative strategies seamlessly. The results of various AI models are analyzed in terms of their classification accuracy. EfficientNet-B0 provided an accuracy of 95% in cat breed identification. InceptionV3 deep learning model reached the maximum accuracy of 96.75%, 96.57%, and 100% on dog, goat, and pig breed identification, respectively. ResNet attained an accuracy of 85.77% on snake species identification. This article provides an in-depth analysis of animal classification/species identification models. The inferences drawn out of this literature review would help the researchers in the selection of an ideal AI model to develop an automated animal classification model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Animal species; Breed identification; Dataset; Deep learning; Machine learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965219-8; 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