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Machine learning and data mining in ...

Hassanien, Aboul Ella.

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Machine learning and data mining in aerospace technology

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning and data mining in aerospace technology/ edited by Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary.
other author:	Hassanien, Aboul Ella.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	viii, 232 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-20212-5
ISBN:	9783030202125

Machine learning and data mining in aerospace technology
Machine learning and data mining in aerospace technology[electronic resource] /edited by Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary. - Cham :Springer International Publishing :2020. - viii, 232 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8361860-949X ;. - Studies in computational intelligence ;v.836..

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.

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.

ISBN: 9783030202125

Standard No.: 10.1007/978-3-030-20212-5doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .M33 2020

Dewey Class. No.: 006.31

Machine learning and data mining in aerospace technology
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245 0 0 $a Machine learning and data mining in aerospace technology $h [electronic resource] / $c edited by Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a viii, 232 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.836
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 0 $a Data mining. $3 562972
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Aerospace Technology and Astronautics. $3 928116
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Hassanien, Aboul Ella. $3 900657
700 1 $a Darwish, Ashraf. $3 3486151
700 1 $a El-Askary, Hesham. $3 3530216
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v.836. $3 3530217
856 4 0 $u https://doi.org/10.1007/978-3-030-20212-5
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)