Call for Chapters: Innovative Machine Learning Applications in the Aerospace Industry

Editors

Venkata Ponnada, Collins Aerospace, United States

Call for Chapters

Proposals Submission Deadline: August 4, 2024
Full Chapters Due: September 1, 2024
Submission Date: September 1, 2024

Introduction

The aerospace industry, known for its technological advancements and rigorous safety standards, has been at the forefront of innovation for decades. In recent years, the integration of machine learning (ML) has revolutionized various aspects of aerospace operations, from design and manufacturing to maintenance and flight operations. This book explores the transformative power of machine learning in the aerospace sector, highlighting key applications, case studies, and future trends.

Objective

The primary objective of "Innovative Machine Learning Applications in the Aerospace Industry" is to provide a comprehensive and insightful exploration of how machine learning (ML) is transforming the aerospace sector. This book aims to: Educate: Offer a thorough understanding of the fundamental concepts of machine learning and its relevance to aerospace engineering, design, manufacturing, and operations. Inspire: Showcase real-world applications and case studies that demonstrate the successful integration of machine learning in various aerospace processes, highlighting the tangible benefits and innovations achieved. Inform: Present the latest trends and advancements in the field, including emerging technologies and future directions for machine learning in aerospace. Guide: Provide practical insights and methodologies for implementing machine learning solutions in aerospace projects, addressing challenges and best practices. Advocate: Highlight the importance of safety, regulatory compliance, and ethical considerations when applying machine learning technologies in the aerospace industry. By achieving these objectives, the book aims to serve as a valuable resource for aerospace professionals, researchers, students, and enthusiasts, fostering a deeper appreciation and understanding of the transformative potential of machine learning in the aerospace industry.

Target Audience

"Innovative Machine Learning Applications in the Aerospace Industry" is designed to cater to a diverse audience, including: Aerospace Engineers and Professionals: Engineers and professionals involved in aircraft design, manufacturing, maintenance, and operations who are interested in understanding how machine learning can enhance their work. Data Scientists and Machine Learning Practitioners: Individuals working in data science and machine learning who seek to apply their expertise to the aerospace industry, exploring new opportunities and applications. Researchers and Academics: Researchers and academics in both aerospace engineering and computer science who are studying the intersection of these fields and looking for comprehensive resources and case studies. Students and Educators: Students pursuing degrees in aerospace engineering, computer science, or related fields, as well as educators looking for material to incorporate into their curricula. Industry Leaders and Decision-Makers: Executives, managers, and policymakers in the aerospace industry who need to understand the strategic implications of machine learning technologies for their organizations. Aerospace Enthusiasts: Individuals with a keen interest in aerospace and technology who want to stay informed about the latest innovations and trends in the industry. By addressing the needs and interests of this varied audience, the book aims to provide valuable insights and practical knowledge that will contribute to the advancement and integration of machine learning in the aerospace industry.

Recommended Topics

Chapter 1: The Intersection of Aerospace and Machine Learning Chapter 2: Machine Learning in Aerospace Design and Manufacturing Chapter 3: Predictive Maintenance and Safety Enhancements Chapter 4: Improving Flight Operations with Machine Learning Chapter 5: Autonomous Systems and Drones Chapter 6: Future Trends and Innovations Conclusion The aerospace industry is poised for unprecedented transformation with the advent of machine learning. By leveraging the power of ML, the industry can achieve new heights in efficiency, safety, and innovation. This book has explored the myriad ways in which machine learning is reshaping aerospace, offering insights into current applications and future possibilities. As technology continues to evolve, the partnership between aerospace and machine learning will undoubtedly lead to groundbreaking advancements, propelling humanity towards a future of limitless possibilities. References Comprehensive list of references and further reading materials

Submission Procedure

Researchers and practitioners are invited to submit on or before August 4, 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 August 10, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by September 1, 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, Innovative Machine Learning Applications in the Aerospace Industry. 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 4, 2024: Proposal Submission Deadline
August 10, 2024: Notification of Acceptance
September 1, 2024: Full Chapter Submission
October 6, 2024: Review Results Returned
November 3, 2024: Final Acceptance Notification
November 10, 2024: Final Chapter Submission



Inquiries

Venkata Ponnada Collins Aerospace emailtodrvenkat@gmail.com

Classifications


Computer Science and Information Technology; Physical Sciences and Engineering
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