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 Book Chapter Faculty Publications- Book Chapter 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 Kumar, Binod; Ghosh, Oindrilla Title A name given to the resource An overview of AI applications in wildlife conservation 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 AI and Machine Learning Techniques for Wildlife Conservation;pp.19-48 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-6935-7.ch002" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-6935-7.ch002</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000090799?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000090799?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 Kumar B., Radha Govind University, India; Ghosh O., Christ University, Bangalore, India Description An account of the resource The integration of artificial intelligence (AI) into wildlife conservation has revolutionized methodologies for monitoring species, enhancing habitat management, and combating poaching. This chapter examines various AI applications that contribute to the protection and preservation of biodiversity. Remote sensing technologies, powered by machine learning algorithms, assist in assessing habitat health and tracking changes over time. AI- driven image recognition tools enable the identification of individual animals from camera trap photos, facilitating more accurate population estimates and behavioral studies. Moreover, predictive analytics play a crucial role in forecasting human- wildlife conflicts and informing proactive management strategies. This synthesis of AI technologies demonstrates their potential to enhance conservation efforts, optimize resource allocation, and ultimately foster more effective wildlife protection initiatives. The ongoing advancement of AI in this field promises to create innovative solutions to some of the most pressing challenges. 2025, IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836936937-1; 979-836936935-7; Language A language of the resource English Type The nature or genre of the resource Book chapter 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