000			04018nam a22004575c 4500
988			_aSpringer_Robotics_2020
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003			ES-MaUEC
005			20230110040230.0
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008			190702s2020 gw a o |||| 0|eng d
020			_a9783030202125
024	7		_a10.1007/978-3-030-20212-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2020 EB
245	0	0	_aMachine learning and data mining in aerospace technology _cedited by Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (VIII, 232 páginas) _b97 ilustraciones, 62 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence _x1860-949X _v836
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_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.
520	3		_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.
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _aData mining _9162648
650		7	_2embne _aAstronáutica _9138156
700	1		_aHassanien, Aboul-Ella _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _997150
700	1		_aDarwish, Ashraf _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aEl-Askary, Hesham _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030202118
776	0	8	_iPrinted edition: _z9783030202132
776	0	8	_iPrinted edition: _z9783030202149
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-20212-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _eel _zSI