Call for Chapters: AI and Machine Learning Techniques for Wildlife Conservation

Editors

Yogita Raghav, K.R. Mangalam University, India
Aditi Chauhan, Finsure, Australia, Australia
Pallavi Pandey, K R Mangalam University, India, India
Surbhi Bhatia Khan, University of Salford, Manchester, United Kingdom, United Kingdom

Call for Chapters

Proposals Submission Deadline: August 30, 2024
Full Chapters Due: October 14, 2024

Introduction

In the face of unprecedented environmental challenges, "Advancing Wildlife Conservation through Machine Learning and Artificial Intelligence" explores a transformative frontier in conservation science. As the world grapples with the alarming rate of biodiversity loss, this book unveils the potential of cutting-edge technologies, namely machine learning (ML) and artificial intelligence (AI), to revolutionize the way we approach wildlife conservation. From sophisticated sensor technologies to innovative AI algorithms, our journey begins with an overview of the foundational tools driving this paradigm shift, providing a comprehensive understanding of their applications in safeguarding biodiversity. Navigating through the Spatial Monitoring and Reporting Tool (SMART) and proposing advanced animal detection systems, we delve into the intricacies of feature extraction and precise identification. Beyond traditional boundaries, the book explores predictive modeling, data ethics, citizen science, and the integration of satellite data, offering a holistic perspective on the dynamic intersection of technology and conservation.

Objective

The objectives of "Advancing Wildlife Conservation through Machine Learning and Artificial Intelligence" are multifaceted. First and foremost, the book aims to provide readers with a comprehensive understanding of the transformative technologies of machine learning (ML) and artificial intelligence (AI) and their profound applications in wildlife conservation. Through an exploration of innovative tools such as sensors, AI algorithms, and ML models, the book seeks to showcase practical applications with real-world examples and case studies, illustrating the tangible impact of these technologies on addressing pressing conservation challenges. It further endeavors to navigate key tools and systems like the Spatial Monitoring and Reporting Tool (SMART) and animal detection systems, unraveling their functionalities and contributions to the conservation landscape. The book places a strong emphasis on ethical considerations, addressing privacy concerns associated with wildlife data collection, and promoting responsible and sustainable conservation practices. Lastly, by delving into predictive modeling, the book aims to demonstrate how ML algorithms can predict species distribution, facilitating effective conservation planning and resource allocation. Throughout, the book advocates for the engagement of citizen science initiatives with AI, fostering a collaborative approach to wildlife conservation that leverages the power of technology for a sustainable future.

Target Audience

Researchers, Academicians, undergraduate and post graduate students.

Recommended Topics

Introduction to AI in Wildlife Conservation Predictive Modeling for Conservation Planning Data Ethics and Privacy in Wildlife Monitoring Citizen Science and AI Animal Detection Systems Automated Monitoring with AI Challenges and Limitations of AI in Conservation Integration of Satellite Data and AI for Habitat Monitoring Human-Wildlife Conflict Mitigation with AI Blockchain Technology for Wildlife Conservation Robotic Systems for Field Monitoring Case Studies and Success Stories

Submission Procedure

Researchers and practitioners are invited to submit on or before August 30, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by September 10, 2024 about the status of their proposals and sent chapter guidelines. Full chapters of a minimum of 10,000 words (word count includes references and related readings) are expected to be submitted by October 14, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, AI and Machine Learning Techniques for Wildlife Conservation. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

August 30, 2024: Proposal Submission Deadline
September 10, 2024: Notification of Acceptance
October 14, 2024: Full Chapter Submission
October 21, 2024: Review Results Returned
November 15, 2024: Final Acceptance Notification
November 30, 2024: Final Chapter Submission



Inquiries

Dr. Yogita Yashveer Raghav
K R Mangalam University, Gurugram, Haryana, India
yogita.raghav@krmangalam.edu.in



Classifications


Business and Management; Computer Science and Information Technology; Education; Security and Forensics; Government and Law; Social Sciences and Humanities
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