Call for Chapters: Machine Learning and Internet of Things in Fire Ecology

Editors

Christian Kaunert, Dublin City University Ireland and University of South Wales, UK, Ireland
Rishabha Malviya, Galgotias University, India, India
Bhupinder Singh, Sharda University, India, India
Sahil Lal, Sharda University, India, India
Manmeet Arora, Sharda University, India, India

Call for Chapters

Proposals Submission Deadline: July 7, 2024
Full Chapters Due: September 8, 2024
Submission Date: September 8, 2024

Introduction

This book elucidates and explores the interface of Fire Ecology with AI, Machine Learning & IoTs, as it has emerged as a pivotal domain with transformative potential. The destruction of millions of acres of forest land through Wild Fire is a global cause of concern and Artificial Intelligence being transformative in nature has the potential to transcend and significantly mitigating risk factor of wild fire. This book focuses on the ability of IoT to foresee the onset of forest fires with the modern censors and UAV-IoTs is of paramount importance to reduce such disaster. To provide effective and efficient forest fire forecast, Internet of Things (IoT) is utilized for data analytics and Artificial Intelligence for the prediction process. The ECO-Guard, an AI integrated AI Sensor System is an intelligent device which utilizes YOLO model and various Machine Learning sensors in forest monitoring and assisting in achieving Sustainable Development Goal 12 (SDG12). Within its Chapters the Academicians, Leading scholars and practitioners contemplates to explore the transformative potential of Machine Learning and IoT in mitigating the impact of Forest Fire.

Objective

The book will assist Environment related industries in understanding the paraphernalia and dynamics of Fire Ecology ecosystem and would be beneficial in identifying the areas which are at risk and suggest appropriate measures. The book aims to provide in-depth examination of the use of Artificial Intelligence and Internet of Things (IoT) algorithms in mitigating the risk of forest fire through data analytics and accurate predictions. It further explores the utilization of novel models and techniques such as ECO-Guard, Recurrent LSTM Neural Network (RLSTM-NN), UAV-IoT applications to conserve Fire Ecology. This book empowers the academicians, policymakers, and practitioners to harness the full potential of modern technological advancement in mitigating the risk of Forest fire by accurate predictions.

Target Audience

The target audience for this for this book includes Homeowners, Local Government Officials, Fire Department, Ecologists, Academicians, Policy Makers, Research Scholars, Environmental Specialists, Industry Experts, Non-Governmental Organizations, Undergraduate Students and Post Graduate Students. The book targets various research papers form Academicians, Lawyers, Policy Makers etc. from around the world focusing and highlighting the synergy between Fire Ecology and Artificial Intelligence. The book provides an overview of AI driven analysis in fostering Wild Fire Management Sustainability. The book further explores the Fire Ecology policy framework envisaging the utilization of all sectors of Wildlife and Forest domain with an aim to achieve sustainable development. With the advent of modern sophisticated technologies like Machine Learning, Deep Learning, and Internet of Things (IoT), the book delves into the role of UAV-IoT’s and modern sophisticated Censors in enhancing the efficiency and mitigating the risk of Wild Fire. The book also deals with specific issues of environment sustainability with regards protection of wildlife and forest ecosystem through the integration of advanced technologies. The chapters of the book delve in to the discovery of new dimensions and futuristic approach with detection of fire breakout through Recurrent LSTM Neural Network (RLSTM-NN).

Recommended Topics

1. Fire Ecology for Ethical Consideration with Artificial Intelligence and Machine Learning in achieving Sustainability. 2. Wildlife and Forest Resource Management with Artificial Intelligence. 3. Current Trends in Identifying Areas at Risk of Wildfire and the Incidence of Fires in Different Foreign Regions. 4. Advances in Using AI Techniques to Understand & Manage Wildfires. 5. Discovering New Dimensions and futuristic approach with IoT in Fire Ecology Conservation. 6. Policy, Governance and Fire Ecology Sustainability. 7. Artificial Intelligence, Machine Learning, Deep Learning and Federated Learning Technological tools in enhancing efficiency in Wildlife and Forest Conservation. 8. Machine Learning Strategies to Enhance Wild Fire Management in Arid Regions. 9. Recurrent LSTM Neural Network (RLSTM-NN) in Forest Fire Prediction. 10. ECO-Guard System in Achieving SDG-12. 11. Innovative Approaches for Wildfire Preparedness & Response. 12. Role of Censors and IoT Applications in Wildfire Management. 13. Role of UAV-IoT Networks in Future Wildfire Detection. 14. Role of ML & DL in Forest Management Through Data Analytics. 15. Neural Network Models. 16. Maneuvering Drones in Mitigating Fire Ecology. 17. Global Legal Perspective towards Wildfire and Wildlife Protection. 18. Case Studies: Integrating AI, ML & IoT in Fire Ecology. 19. Future Perspectives.

Submission Procedure

Researchers and practitioners are invited to submit on or before July 7, 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 July 21, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by September 8, 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, Machine Learning and Internet of Things in Fire Ecology. 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

July 7, 2024: Proposal Submission Deadline
July 21, 2024: Notification of Acceptance
September 8, 2024: Full Chapter Submission
October 13, 2024: Review Results Returned
November 10, 2024: Final Acceptance Notification
November 17, 2024: Final Chapter Submission



Inquiries

Christian Kaunert Dublin City University Ireland and University of South Wales, UK christian.kaunert@dcu.ie Rishabha Malviya Galgotias University, India rishabhamalviya19@gmail.com Bhupinder Singh Sharda University, India bhupindersinghlaw19@gmail.com Sahil Lal Sharda University, India adv.sahillal@gmail.com Manmeet Arora Sharda University, India manmeet.champ@gmail.com

Classifications


Business and Management; Computer Science and Information Technology; Education; Life Sciences; Medicine and Healthcare; Media and Communications; Security and Forensics; Government and Law; Social Sciences and Humanities; Physical Sciences and Engineering
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