|
|
|
|
| LEADER |
04497cam a2200589 4500 |
| 001 |
183-1697879306 |
| 003 |
DE-627 |
| 005 |
20200818103222.0 |
| 007 |
tu |
| 008 |
200512s2020 sz ||||| 00| ||eng c |
| 020 |
|
|
|a 3030202119
|c hardcover
|9 3-030-20211-9
|
| 020 |
|
|
|a 9783030202118
|c hardcover
|9 978-3-030-20211-8
|
| 035 |
|
|
|a (DE-627)1697879306
|
| 035 |
|
|
|a (DE-599)KXP1697879306
|
| 035 |
|
|
|a (OCoLC)1112136749
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rda
|
| 041 |
|
|
|a eng
|
| 044 |
|
|
|c XA-CH
|
| 050 |
|
0 |
|a Q325.5
|
| 082 |
0 |
|
|a 006.3/1
|
| 084 |
|
|
|a 54.72
|2 bkl
|
| 245 |
1 |
0 |
|a Machine learning and data mining in aerospace technology
|c Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary, editors
|
| 264 |
|
1 |
|a Cham, Switzerland
|b Springer
|c [2020]
|
| 300 |
|
|
|a viii, 232 pages
|b illustrations (some color)
|c 24 cm
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
|
| 338 |
|
|
|a Band
|b nc
|2 rdacarrier
|
| 490 |
1 |
|
|a Studies in computational intelligence
|v volume 836
|
| 500 |
|
|
|a Includes bibliographical references
|
| 520 |
|
|
|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
|
| 540 |
|
|
|a Current copyright fee: GBP19.00
|
| 650 |
|
0 |
|a Machine learning
|
| 650 |
|
0 |
|a Data mining
|
| 650 |
|
0 |
|a Aerospace engineering
|x Data processing
|
| 650 |
|
4 |
|a Aerospace engineering ; Data processing
|
| 650 |
|
4 |
|a Data mining
|
| 650 |
|
4 |
|a Machine learning
|
| 689 |
0 |
0 |
|D s
|0 (DE-588)4193754-5
|0 (DE-627)105224782
|0 (DE-576)21008944X
|a Maschinelles Lernen
|2 gnd
|
| 689 |
0 |
1 |
|D s
|0 (DE-588)4428654-5
|0 (DE-627)216935180
|0 (DE-576)212347217
|a Data Mining
|2 gnd
|
| 689 |
0 |
|
|5 (DE-627)
|
| 700 |
1 |
|
|a Hassanien, Aboul Ella
|d 1964-
|e HerausgeberIn
|0 (DE-588)135922631
|0 (DE-627)616740891
|0 (DE-576)28156521X
|4 edt
|
| 700 |
1 |
|
|a Darwish, Ashraf
|e HerausgeberIn
|0 (DE-588)104829515X
|0 (DE-627)780079744
|0 (DE-576)402104838
|4 edt
|
| 700 |
1 |
|
|a El-Askary, Hesham
|e HerausgeberIn
|4 edt
|
| 776 |
1 |
|
|z 9783030202125
|
| 830 |
|
0 |
|a Studies in computational intelligence
|v volume 836
|9 836
|w (DE-627)498966216
|w (DE-576)117828025
|w (DE-600)2202445-1
|x 1860-949X
|7 ns
|
| 856 |
4 |
2 |
|u https://www.gbv.de/dms/tib-ub-hannover/1697879306.pdf
|m V:DE-601
|m B:DE-89
|q pdf/application
|3 Inhaltsverzeichnis
|
| 856 |
4 |
2 |
|u http://link.springer.com/
|x Verlag
|3 1850-9999
|
| 924 |
0 |
|
|a 3666134416
|b DE-89
|9 89
|c GBV
|d c
|g RS 5317(836)
|
| 936 |
b |
k |
|a 54.72
|j Künstliche Intelligenz
|0 (DE-627)10641240X
|
| 951 |
|
|
|a BO
|
| 980 |
|
|
|a 1697879306
|b 183
|c sid-183-col-kxpbbi
|
SOLR
| _version_ |
1797788703118065664 |
| author2 |
Hassanien, Aboul Ella, Darwish, Ashraf, El-Askary, Hesham |
| author2_role |
edt, edt, edt |
| author2_variant |
a e h ae aeh, a d ad, h e a hea |
| author_facet |
Hassanien, Aboul Ella, Darwish, Ashraf, El-Askary, Hesham |
| building |
Library A |
| callnumber-first |
Q - Science |
| callnumber-label |
Q325 |
| callnumber-raw |
Q325.5 |
| callnumber-search |
Q325.5 |
| callnumber-sort |
Q 3325.5 |
| callnumber-subject |
Q - General Science |
| collection |
sid-183-col-kxpbbi |
| contents |
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 |
| ctrlnum |
(DE-627)1697879306, (DE-599)KXP1697879306, (OCoLC)1112136749 |
| dewey-full |
006.3/1 |
| dewey-hundreds |
000 - Computer science, information, general works |
| dewey-ones |
006 - Special computer methods |
| dewey-raw |
006.3/1 |
| dewey-search |
006.3/1 |
| dewey-sort |
16.3 11 |
| dewey-tens |
000 - Computer science, information, general works |
| facet_avail |
Local |
| facet_local_del330 |
Maschinelles Lernen, Data Mining |
| finc_class_facet |
Informatik, Allgemeine Naturwissenschaft |
| fincclass_txtF_mv |
science-computerscience |
| footnote |
Includes bibliographical references |
| format |
Book |
| format_access_txtF_mv |
Book, E-Book |
| format_de14 |
Book, E-Book |
| format_de15 |
Book, E-Book |
| format_del152 |
Buch |
| format_detail_txtF_mv |
text-print-monograph-independent |
| format_dezi4 |
e-Book |
| format_finc |
Book, E-Book |
| format_legacy |
Book |
| format_legacy_nrw |
Book, E-Book |
| format_nrw |
Book, E-Book |
| format_strict_txtF_mv |
Book |
| geogr_code |
not assigned |
| geogr_code_person |
not assigned |
| hierarchy_parent_id |
183-498966216 |
| hierarchy_parent_title |
Studies in computational intelligence |
| hierarchy_sequence |
836 |
| hierarchy_top_id |
183-498966216 |
| hierarchy_top_title |
Studies in computational intelligence |
| id |
183-1697879306 |
| illustrated |
Not Illustrated |
| imprint |
Cham, Switzerland, Springer, [2020] |
| imprint_str_mv |
Cham, Switzerland: Springer, [2020] |
| institution |
FID-BBI-DE-23 |
| is_hierarchy_id |
183-1697879306 |
| is_hierarchy_title |
Machine learning and data mining in aerospace technology |
| isbn |
3030202119, 9783030202118 |
| isbn_isn_mv |
9783030202125 |
| issn_isn_mv |
1860-949X |
| language |
English |
| last_indexed |
2024-04-30T19:21:52.362Z |
| marc_error |
[geogr_code]Unable to make public java.lang.AbstractStringBuilder java.lang.AbstractStringBuilder.append(java.lang.String) accessible: module java.base does not "opens java.lang" to unnamed module @64e01542 |
| match_str |
hassanien2020machinelearninganddatamininginaerospacetechnology |
| mega_collection |
K10plus Verbundkatalog |
| multipart_link |
117828025 |
| multipart_part |
(117828025)volume 836 |
| oclc_num |
1112136749 |
| physical |
viii, 232 pages; illustrations (some color); 24 cm |
| publishDate |
[2020] |
| publishDateSort |
2020 |
| publishPlace |
Cham, Switzerland |
| publisher |
Springer |
| record_format |
marcfinc |
| record_id |
1697879306 |
| recordtype |
marcfinc |
| rvk_facet |
No subject assigned |
| series |
Studies in computational intelligence, volume 836 |
| series2 |
Studies in computational intelligence ; volume 836 |
| source_id |
183 |
| spelling |
Machine learning and data mining in aerospace technology Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary, editors, Cham, Switzerland Springer [2020], viii, 232 pages illustrations (some color) 24 cm, Text txt rdacontent, ohne Hilfsmittel zu benutzen n rdamedia, Band nc rdacarrier, Studies in computational intelligence volume 836, Includes bibliographical references, 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, Current copyright fee: GBP19.00, Machine learning, Data mining, Aerospace engineering Data processing, Aerospace engineering ; Data processing, s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd, s (DE-588)4428654-5 (DE-627)216935180 (DE-576)212347217 Data Mining gnd, (DE-627), Hassanien, Aboul Ella 1964- HerausgeberIn (DE-588)135922631 (DE-627)616740891 (DE-576)28156521X edt, Darwish, Ashraf HerausgeberIn (DE-588)104829515X (DE-627)780079744 (DE-576)402104838 edt, El-Askary, Hesham HerausgeberIn edt, 9783030202125, Studies in computational intelligence volume 836 836 (DE-627)498966216 (DE-576)117828025 (DE-600)2202445-1 1860-949X ns, https://www.gbv.de/dms/tib-ub-hannover/1697879306.pdf V:DE-601 B:DE-89 pdf/application Inhaltsverzeichnis, http://link.springer.com/ Verlag 1850-9999 |
| spellingShingle |
Machine learning and data mining in aerospace technology, Studies in computational intelligence, volume 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, Machine learning, Data mining, Aerospace engineering Data processing, Aerospace engineering ; Data processing, Maschinelles Lernen, Data Mining |
| title |
Machine learning and data mining in aerospace technology |
| title_auth |
Machine learning and data mining in aerospace technology |
| title_full |
Machine learning and data mining in aerospace technology Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary, editors |
| title_fullStr |
Machine learning and data mining in aerospace technology Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary, editors |
| title_full_unstemmed |
Machine learning and data mining in aerospace technology Aboul Ella Hassanien, Ashraf Darwish, Hesham El-Askary, editors |
| title_in_hierarchy |
volume 836. Machine learning and data mining in aerospace technology ([2020]) |
| title_short |
Machine learning and data mining in aerospace technology |
| title_sort |
machine learning and data mining in aerospace technology |
| title_unstemmed |
Machine learning and data mining in aerospace technology |
| topic |
Machine learning, Data mining, Aerospace engineering Data processing, Aerospace engineering ; Data processing, Maschinelles Lernen, Data Mining |
| topic_facet |
Machine learning, Data mining, Aerospace engineering, Aerospace engineering ; Data processing, Data processing, Maschinelles Lernen, Data Mining |
| url |
https://www.gbv.de/dms/tib-ub-hannover/1697879306.pdf, http://link.springer.com/ |
| work_keys_str_mv |
AT hassanienaboulella machinelearninganddatamininginaerospacetechnology, AT darwishashraf machinelearninganddatamininginaerospacetechnology, AT elaskaryhesham machinelearninganddatamininginaerospacetechnology |