Machine learning and data mining in aerospace technology / Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary, editors.
Hassanien, Aboul Ella.; Darwish, Ashraf.; El-Askary, Hesham.
2020
Q325.5 .M33 2020
Available Online

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Title Machine learning and data mining in aerospace technology / Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary, editors.
ISBN 9783030202125 (electronic book)
3030202127 (electronic book)
9783030202118
3030202119
Published Cham : Springer, [2020]
Language English
Description 1 online resource (viii, 232 pages) : illustrations.
Other Standard Identifiers 10.1007/978-3-030-20
Call Number Q325.5 .M33 2020
Dewey Decimal Classification 006.3/1
Summary This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the 'eagle eyes that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites - which can determine satellites current status and predict their failure based on telemetry data - is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.
Access Note Access limited to authorized users.
Source of Description Description based on online resource; title from digital title page (viewed on August 26, 2019).
Added Author Hassanien, Aboul Ella.
Darwish, Ashraf.
El-Askary, Hesham.
Series Studies in computational intelligence ; v. 836.
Available in Other Form Print version: 9783030202118
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Tensor-based anomaly detection for satellite telemetry data
Machine learning in satellites monitoring and risk challenges
Formalization, prediction and recognition of expert evaluations of telemetric data of artificial satellites based on type-II fuzzy sets
Intelligent health monitoring systems for space missions based on data mining techniques
Design, implementation, and validation of satellite simulator and data packets analysis
Crop yield estimation using decision trees and random forest machine learning algorithms on data from terra (EOS AM-1) & aqua (EOS PM-1) satellite data
Data analytics using satellite remote sensing in healthcare applications
Design, Implementation, and Testing of Unpacking System for Telemetry Data of Artificial Satellites: Case Study: EGYSAT1
Multiscale Satellite Image Classification using Deep Learning Approach
Security approaches in machine learning for satellite communication
Machine learning techniques for IoT intrusions detection in aerospace cyber physical systems.

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