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000922225 020__ $$a9783030202125$$q(electronic book)
000922225 020__ $$a3030202127$$q(electronic book)
000922225 020__ $$z9783030202118
000922225 020__ $$z3030202119
000922225 0248_ $$a10.1007/978-3-030-20
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000922225 050_4 $$aQ325.5$$b.M33 2020
000922225 08204 $$a006.3/1$$223
000922225 24500 $$aMachine learning and data mining in aerospace technology /$$cAboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary, editors.
000922225 264_1 $$aCham :$$bSpringer,$$c[2020]
000922225 300__ $$a1 online resource (viii, 232 pages) :$$billustrations.
000922225 336__ $$atext$$btxt$$2rdacontent
000922225 337__ $$acomputer$$bc$$2rdamedia
000922225 338__ $$aonline resource$$bcr$$2rdacarrier
000922225 4901_ $$aStudies in computational intelligence ;$$vvolume 836
000922225 5050_ $$aTensor-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.
000922225 506__ $$aAccess limited to authorized users.
000922225 520__ $$aThis 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.
000922225 588__ $$aDescription based on online resource; title from digital title page (viewed on August 26, 2019).
000922225 650_0 $$aMachine learning.
000922225 650_0 $$aData mining.
000922225 7001_ $$aHassanien, Aboul Ella.
000922225 7001_ $$aDarwish, Ashraf.
000922225 7001_ $$aEl-Askary, Hesham.
000922225 77608 $$iPrint version: $$z3030202119$$z9783030202118$$w(OCoLC)1097315684
000922225 830_0 $$aStudies in computational intelligence ;$$vv. 836.
000922225 852__ $$bebk
000922225 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-20212-5$$zOnline Access$$91397441.1
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000922225 980__ $$aEBOOK
000922225 980__ $$aBIB
000922225 982__ $$aEbook
000922225 983__ $$aOnline
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