LEADER 04498nam 22006255 450 001 9910484962803321 005 20240417201406.0 010 $a3-030-20212-7 024 7 $a10.1007/978-3-030-20212-5 035 $a(CKB)4100000008618264 035 $a(DE-He213)978-3-030-20212-5 035 $a(MiAaPQ)EBC5940781 035 $a(PPN)243769288 035 $a(EXLCZ)994100000008618264 100 $a20190702d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning and Data Mining in Aerospace Technology /$fedited by Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (VIII, 232 p. 97 illus., 62 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v836 311 $a3-030-20211-9 327 $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. 330 $aThis book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ?eagle eyes? that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites ? which can determine satellites? current status and predict their failure based on telemetry data ? is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v836 606 $aComputational intelligence 606 $aAerospace engineering 606 $aAstronautics 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aAerospace Technology and Astronautics$3https://scigraph.springernature.com/ontologies/product-market-codes/T17050 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aAerospace engineering. 615 0$aAstronautics. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aAerospace Technology and Astronautics. 615 24$aArtificial Intelligence. 676 $a006.31 676 $a006.31 702 $aHassanien$b Aboul Ella$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDarwish$b Ashraf$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aEl-Askary$b Hesham M$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484962803321 996 $aMachine Learning and Data Mining in Aerospace Technology$92854259 997 $aUNINA