Machine Learning Based Air Traffic Surveillance System Using Image Processing
Language: English
Published by Emerald Publishing Limited, GB, 2026
- Hardcover
- New
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
AbeBooks seller since June 11, 2025
Condition: New
£ 120.25
Quantity: Over 20 available
Add to basketItem description from seller
Machine Learning Based Air Traffic Surveillance System Using Image Processing analyses how advanced machine learning algorithms and image processing technologies are revolutionising air-traffic management. By integrating real-time visual data analysis with sophisticated artificial intelligence techniques, this book highlights the potential to enhance situational awareness, safety, and efficiency in managing increasingly complex and congested airspaces. It delves into the use of convolutional neural networks (CNNs) and deep learning models to identify, track, and analyse aircraft movements, offering precise and actionable insights for air-traffic controllers.This comprehensive resource combines theoretical foundations with practical applications, including real-world case studies and discussions on system implementation. It addresses critical aspects such as object detection, anomaly identification, and trajectory prediction, alongside regulatory, ethical, and cybersecurity considerations. With its blend of cutting-edge research and practical insights, this book is an invaluable guide for professionals, researchers, and students in aerospace engineering, artificial intelligence, and computer vision, providing a roadmap for advancing air-traffic surveillance and management in the era of intelligent systems.…
Seller Inventory # LU-9781805920632
- Title
- Machine Learning Based Air Traffic Surveillance System Using Image Processing
- Author
- Faizan Ahmad
- Publisher
- Emerald Publishing Limited, GB
- Publication year
- 2026
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 1805920634
- ISBN 13
- 9781805920632
- Item weight
- 530 grams
Machine Learning Based Air Traffic Surveillance System Using Image Processing analyses how advanced machine learning algorithms and image processing technologies are revolutionising air-traffic management. By integrating real-time visual data analysis with sophisticated artificial intelligence techniques, this book highlights the potential to enhance situational awareness, safety, and efficiency in managing increasingly complex and congested airspaces. It delves into the use of convolutional neural networks (CNNs) and deep learning models to identify, track, and analyse aircraft movements, offering precise and actionable insights for air-traffic controllers.
This comprehensive resource combines theoretical foundations with practical applications, including real-world case studies and discussions on system implementation. It addresses critical aspects such as object detection, anomaly identification, and trajectory prediction, alongside regulatory, ethical, and cybersecurity considerations. With its blend of cutting-edge research and practical insights, this book is an invaluable guide for professionals, researchers, and students in aerospace engineering, artificial intelligence, and computer vision, providing a roadmap for advancing air-traffic surveillance and management in the era of intelligent systems.
"Synopsis" may belong to another edition of this title.
About the Author
Jay Kumar Pandey is an Assistant Professor in the Department of Electrical and Electronics Engineering at Shri Ramswaroop Memorial University, India.
Mritunjay Rai is an Assistant Professor in the Department of Electrical and Electronics Engineering at Shri Ramswaroop Memorial University, India.
Faizan Ahmad is a Lecturer in Computer Science and/or Games Development at Cardiff School of Technologies, Cardiff Metropolitan University, UK.
"About the title" may belong to another edition of this title.
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