As the world grapples with the alarming rate of biodiversity loss, the potential of cutting-edge technologies, namely machine learning (ML) and artificial intelligence (AI), revolutionize the way we approach wildlife conservation. From sophisticated sensor technologies to innovative AI algorithms, foundational tools driving this paradigm shift provide a comprehensive understanding of their applications in safeguarding biodiversity. The navigation of systems such as the Spatial Monitoring and Reporting Tool (SMART) and advanced animal detection systems can be used to delve into the intricacies of feature extraction and precise identification. This exploration of predictive modeling, data ethics, citizen science, and the integration of satellite data offers a holistic perspective on the dynamic intersection of technology and conservation. AI and Machine Learning Techniques for Wildlife Conservation illustrates the tangible impact of these technologies on addressing pressing conservation challenges and advocates for the engagement of citizen science initiatives with AI. It fosters a collaborative approach to wildlife conservation that leverages the power of technology for a sustainable future. Covering topics including Internet of Things (IoT), satellite data, and predictive ecosystem management, this book is an excellent resource for conservationists, computer scientists, researchers, professionals, academicians, scholars, and more.
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Yogita Yashveer Raghav is an assistant professor in the computer science department at K R Mangalam University, Gurugram, Haryana is currently pursuing PhD from Banasthali Vidyapith, Rajasthan and has already achieved impressive qualifications, including UGCNET and HTET. With research interests focused on cloud computing and data mining, she has published several articles in national and international journals and conferences, showcasing her expertise in these areas. As a faculty member, she is committed to teaching and mentoring students, sharing her knowledge and passion for computer science with the next generation of professionals. Their dedication to research and publication is a testament to their ongoing commitment to advancing the field and making meaningful contributions to the academic community. She is IEEE member.
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Buch. Etat : Neu. AI and Machine Learning Techniques for Wildlife Conservation | Yogita Yashveer Raghav (u. a.) | Buch | Englisch | 2025 | IGI Global | EAN 9798369369357 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 131622549
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Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As the world grapples with the alarming rate of biodiversity loss, the potential of cutting-edge technologies, namely machine learning (ML) and artificial intelligence (AI), revolutionize the way we approach wildlife conservation. From sophisticated sensor technologies to innovative AI algorithms, foundational tools driving this paradigm shift provide a comprehensive understanding of their applications in safeguarding biodiversity. The navigation of systems such as the Spatial Monitoring and Reporting Tool (SMART) and advanced animal detection systems can be used to delve into the intricacies of feature extraction and precise identification. This exploration of predictive modeling, data ethics, citizen science, and the integration of satellite data offers a holistic perspective on the dynamic intersection of technology and conservation. AI and Machine Learning Techniques for Wildlife Conservation illustrates the tangible impact of these technologies on addressing pressing conservation challenges and advocates for the engagement of citizen science initiatives with AI. It fosters a collaborative approach to wildlife conservation that leverages the power of technology for a sustainable future. Covering topics including Internet of Things (IoT), satellite data, and predictive ecosystem management, this book is an excellent resource for conservationists, computer scientists, researchers, professionals, academicians, scholars, and more. N° de réf. du vendeur 9798369369357
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