{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:24:15Z","timestamp":1774895055682,"version":"3.50.1"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T00:00:00Z","timestamp":1567555200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T00:00:00Z","timestamp":1567555200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s10462-019-09760-1","type":"journal-article","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T17:02:58Z","timestamp":1567616578000},"page":"3201-3230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Machine learning in telemetry data mining of space mission: basics, challenging and future directions"],"prefix":"10.1007","volume":"53","author":[{"given":"Aboul Ella","family":"Hassanien","sequence":"first","affiliation":[]},{"given":"Ashraf","family":"Darwish","sequence":"additional","affiliation":[]},{"given":"Sara","family":"Abdelghafar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,4]]},"reference":[{"key":"9760_CR1","series-title":"Studies in computational intelligence","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/978-3-030-20212-5_4","volume-title":"Machine learning and data mining in aerospace technology","author":"S Abdelghafar","year":"2020","unstructured":"Abdelghafar S, Darwish A, Hassanien AE (2020) Intelligent health monitoring systems for space missions based on data mining techniques. In: Hassanien A, Darwish A, El-Askary H (eds) Machine learning and data mining in aerospace technology, vol 836. Studies in computational intelligence. Springer, Cham, pp 65\u201378"},{"key":"9760_CR2","series-title":"Studies in computational intelligence","volume-title":"Foundations of computational intelligence, bio-inspired data mining","author":"A Abraham","year":"2009","unstructured":"Abraham A, Hassanien AE, Carvalho A (2009) Foundations of computational intelligence, bio-inspired data mining, vol 4. Studies in computational intelligence. Springer, Berlin"},{"key":"9760_CR3","unstructured":"Bay S D, Schwabacher M (2003) Mining distance-based outliers in near linear time with randomization and a simple pruning rule. In: The ninth ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, pp 29\u201338"},{"key":"9760_CR4","unstructured":"Bouleau F, Christoph S (2014) Towards the identification of outliers in satellite telemetry data by using fourier coefficients. In: 6th international conference on agents an artificial intelligence, Angers, France, pp 211\u2013224"},{"issue":"3","key":"9760_CR5","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.jag.2011.01.008","volume":"13","author":"W Boulila","year":"2011","unstructured":"Boulila W, Farah IR, Ettabaa KS, Solaiman B, Gh\u00e9zalaa HB (2011) A data mining based approach to predict spatiotemporal changes in satellite images. Int J Appl Earth Obs Geoinf 13(3):386\u2013395","journal-title":"Int J Appl Earth Obs Geoinf"},{"issue":"1","key":"9760_CR6","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/j.solener.2019.03.079","volume":"183","author":"LC Buenoa","year":"2019","unstructured":"Buenoa LC, Mateob CC, Justoc JS, Sanza SS (2019) Machine learning regressors for solar radiation estimation from satellite data. Sol Energy 183(1):768\u2013775","journal-title":"Sol Energy"},{"issue":"4","key":"9760_CR7","doi-asserted-by":"crossref","first-page":"691","DOI":"10.3934\/mbe.2008.5.691","volume":"5","author":"G Canziani","year":"2008","unstructured":"Canziani G, Ferrati R, Marinelli C, Dukatz F (2008) Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes. Math Biosci Eng 5(4):691\u2013711","journal-title":"Math Biosci Eng"},{"key":"9760_CR8","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/978-1-4612-2270-5_11","volume-title":"Expert systems and probabilistic network models, monographs in computer science","author":"E Castillo","year":"1997","unstructured":"Castillo E, Gutierrez JM, Hadi AS (1997) Expert systems and probabilistic network models, monographs in computer science. Springer, New York, pp 481\u2013527"},{"issue":"15","key":"9760_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(15):1\u201372","journal-title":"ACM Comput Surv (CSUR)"},{"key":"9760_CR10","unstructured":"Chang C, Nallo W, Rastogi R, Beugless D, Mickey F, Shoop A (1992) Satellite diagnostic system: an expert system for Intelsat satellite operations. In: IVth European aerospace conference (EAC), pp 321\u2013327"},{"key":"9760_CR11","series-title":"Lecture notes in computer science","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1007\/978-3-319-24306-1_62","volume-title":"Computational collective intelligence","author":"I Chebbi","year":"2015","unstructured":"Chebbi I, Boulila W, Farah IR (2015) Big data: concepts, challenges and applications. In: N\u00fa\u00f1ez M, Nguyen N, Camacho D, Trawi\u0144ski B (eds) Computational collective intelligence, vol 9330. Lecture notes in computer science. Springer, Cham, pp 638\u2013647"},{"key":"9760_CR12","unstructured":"Chengxi D, Dewei W, Junyi Q (2008) Algorithm design of navigation satellite evaluation classification rules based on VPRS and DPSO. In: International conference on computer science and software engineering, Hubei, China, pp 12\u201314"},{"issue":"3","key":"9760_CR13","first-page":"273","volume":"20","author":"C Corinna","year":"1995","unstructured":"Corinna C, Vladimir V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"issue":"4","key":"9760_CR14","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"M Culloch","year":"1943","unstructured":"Culloch M, Pitts WW (1943) A logical calculus of ideas immanent in nervous activity. Bull Math Biophys 5(4):115\u2013133","journal-title":"Bull Math Biophys"},{"key":"9760_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09719-2","author":"A Darwish","year":"2019","unstructured":"Darwish A, Hassanien AE, Das S (2019) A survey of swarm and evolutionary computing approaches for deep learning. Artif Intell Rev. \nhttps:\/\/doi.org\/10.1007\/s10462-019-09719-2","journal-title":"Artif Intell Rev"},{"issue":"4","key":"9760_CR16","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s12559-016-9415-7","volume":"8","author":"K Dashtipour","year":"2016","unstructured":"Dashtipour K, Poria S, Hussain A, Cambria E, Hawalah AY, Gelbukh A, Zhou Q (2016) Multilingual sentiment analysis: state of the art and independent comparison of techniques. Cogn Comput 8(4):757\u2013771","journal-title":"Cogn Comput"},{"key":"9760_CR17","doi-asserted-by":"crossref","first-page":"19269","DOI":"10.1109\/ACCESS.2017.2754447","volume":"5","author":"L Datong","year":"2017","unstructured":"Datong L, Jingyue P, Song G, Xie W, Peng Y, Xiyuan P (2017) Fragment anomaly detection with prediction and statistical analysis for satellite telemetry. IEEE Access 5:19269\u201319281","journal-title":"IEEE Access"},{"key":"9760_CR18","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1016\/j.microrel.2015.07.010","volume":"55","author":"P Dawei","year":"2015","unstructured":"Dawei P, Datong L, Zhou J, Zhang G (2015) Anomaly detection for satellite power subsystem with associated rules based on kernel principal component analysis. Microelectron Reliab 55:2082\u20132086","journal-title":"Microelectron Reliab"},{"issue":"3","key":"9760_CR19","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1023\/A:1012454411458","volume":"46","author":"D Decoste","year":"2002","unstructured":"Decoste D, Sch B (2002) Training invariant support vector machines. Mach Learn 46(3):161\u2013190","journal-title":"Mach Learn"},{"key":"9760_CR20","doi-asserted-by":"crossref","unstructured":"Dong X, Dechang P (2014) An effective method for mining quantitative association rules with clustering partition in satellite telemetry data. In: 2014 IEEE second international conference on advanced cloud and big data, Huangshan, China, pp 26\u201335","DOI":"10.1109\/CBD.2014.12"},{"issue":"1","key":"9760_CR21","first-page":"158","volume":"11","author":"SA Ermushev","year":"2016","unstructured":"Ermushev SA, Balashov AG (2016) A complex machine learning technique for ground target detection and classification. Int J Appl Eng Res 11(1):158\u2013161","journal-title":"Int J Appl Eng Res"},{"key":"9760_CR22","doi-asserted-by":"crossref","unstructured":"Fang H, Xing Y, Luo K, Liming H (2012) Study of the long-term performance prediction methods using the spacecraft telemetry data. In: Prognostics & system health management conference (PHM-2012). IEEE, Beijing, China, pp 1\u20137","DOI":"10.1109\/PHM.2012.6228775"},{"key":"9760_CR23","unstructured":"Finzi AE, Lavagna MR, Sangiovanni G (2003) Fuzzy inductive reasoning and possibilistic logic for space systems failure smart detection and identification. In: The 7th international symposium on artificial intelligence, robotics and automation in space: (i-SAIRAS 2003), NARA, Japan, pp 1\u20139"},{"key":"9760_CR24","unstructured":"Fujimaki R, Yairi T, Machida K (2005) Adaptive limit-checking for spacecraft using relevance vector autoregressive model. In: 8th international symposium on artificial intelligence, robotics and automation in space, Munich, Germany"},{"key":"9760_CR25","unstructured":"Fukushima Y (2008) Onboard sensor and actuator failure detection using SVM for autonomy of small satellite systems. In: The 9th international symposium on artificial intelligence and robotic automation in space: iSairas, Los Angels, CA"},{"key":"9760_CR26","doi-asserted-by":"crossref","unstructured":"Ganchenko V, Doudkin A, Inyutin A, Marushko Y, Podenok L, Sadykhov R (2013) Neural network software diagnosis system of telemetry data. In: The 7th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications, Berlin, Germany, pp 376\u2013380","DOI":"10.1109\/IDAACS.2013.6662710"},{"key":"9760_CR27","doi-asserted-by":"crossref","unstructured":"Gao Y, Tianshe Y, Minqiang X, Xing N (2012) An unsupervised anomaly detection approach for spacecraft based on normal behavior clustering. In: Fifth international conference on intelligent computation technology and automation, Zhangjiajie, China, pp 478\u2013481","DOI":"10.1109\/ICICTA.2012.126"},{"key":"9760_CR28","unstructured":"Gao Y, Yang T, Feng J, Minqiang X (2012) A neural network approach for satellite telemetry data prediction. In: The 2012 international conference on electronics, communications and control, Washington, DC, USA, pp 150\u2013153"},{"key":"9760_CR29","unstructured":"George H J, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Eleventh conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 338\u2013345"},{"key":"9760_CR30","first-page":"1","volume-title":"Fuzzy sets and fuzzy logic: theory and applications","author":"JK George","year":"2003","unstructured":"George JK, Yuan B (2003) Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall of India, Upper Saddle River, pp 1\u2013592"},{"key":"9760_CR31","unstructured":"Hashimoto M, Nishigori N, Mizutani M (1997) Operating status of monitoring and diagnostic expert system for geomagnetic satellite GEOTAIL. In: The 2nd international symposium on reducing the cost of spacecraft ground systems and operation, pp 1\u20138"},{"key":"9760_CR32","doi-asserted-by":"crossref","unstructured":"Hashimoto M, Nishigori N, Mizutani M (2000) Running status of monitoring and diagnostic expert system for Mars observer Nozomi. In: The 22nd international symposium on space technology and science (ISTS)","DOI":"10.1007\/978-94-015-9395-3_38"},{"key":"9760_CR33","unstructured":"Hassanien AE, Alamry E (2015) Swarm intelligence: principles, advances, and applications. Taylor & Francis Group, Boca Raton. ISBN 9781498741064\u2014CAT# K26721"},{"key":"9760_CR34","doi-asserted-by":"crossref","first-page":"239","DOI":"10.4018\/978-1-59904-552-8","volume-title":"Rough computing: theories, technologies, and applications","author":"AE Hassanien","year":"2008","unstructured":"Hassanien AE, Suraj Z, Slezak D, Lingras P (2008) Rough computing: theories, technologies, and applications. IGI Global, Hershey, pp 239\u2013268"},{"key":"9760_CR35","unstructured":"Hayden SC, Sweet AJ, Christa SE (2004) Livingstone model-based diagnosis of earth ob-serving one. In: AIAA 1st intelligent systems technical conference, CA, USA, pp 1\u201312"},{"key":"9760_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.04.002","author":"M H\u00fcsch","year":"2018","unstructured":"H\u00fcsch M, Schyska BU, von Bremen L (2018) CorClustST\u2013correlation-based clustering of big spatio-temporal datasets. Future Gener Comput Syst. \nhttps:\/\/doi.org\/10.1016\/j.future.2018.04.002","journal-title":"Future Gener Comput Syst"},{"key":"9760_CR37","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.atmosenv.2014.11.049","volume":"102","author":"WL Junger","year":"2015","unstructured":"Junger WL, Ponce A (2015) Imputation of missing data in time series for air pollutants. Atmos Environ 102:96\u2013104","journal-title":"Atmos Environ"},{"key":"9760_CR38","unstructured":"Junyi X, Li Y, Le L, Jinyang L (2013) Multi-agent joint learning from argumentation. In: The 9th international workshop on agents and data mining interaction (ADMI 2013), Saint Paul, MN, USA, pp 14\u201325"},{"key":"9760_CR39","unstructured":"Kannan SA, Devi T (2016) Mining satellite telemetry data: comparison of rule-induction and association mining techniques. In: IEEE international conference on advances in computing applications (ICACA), Coimbatore, India, pp 259\u2013264"},{"issue":"1","key":"9760_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/2945.981847","volume":"8","author":"DA Keim","year":"2002","unstructured":"Keim DA (2002) Information visualization and visual data mining. IEEE Trans Vis Comput Gr 8(1):1\u20138","journal-title":"IEEE Trans Vis Comput Gr"},{"issue":"3","key":"9760_CR41","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1109\/TAES.2008.4655354","volume":"44","author":"S Kim","year":"2008","unstructured":"Kim S, Choi J (2008) Fault detection and diagnosis of aircraft actuators using fuzzy-tuning IMM filter. IEEE Trans Aerosp Electron Syst 44(3):940\u2013952","journal-title":"IEEE Trans Aerosp Electron Syst"},{"key":"9760_CR42","doi-asserted-by":"crossref","unstructured":"Kurien J, Dolores M (2008) Costs and benefits of model-based diagnosis. In: Aerospace conference. IEEE, MT, USA, pp 1\u201314","DOI":"10.1109\/AERO.2008.4526647"},{"issue":"3","key":"9760_CR43","first-page":"678","volume":"27","author":"YQ Li","year":"2014","unstructured":"Li YQ, Wang R, Xu M (2014) Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm. J China Aerosp 27(3):678\u2013687","journal-title":"J China Aerosp"},{"issue":"5","key":"9760_CR44","first-page":"1","volume":"12","author":"K Li","year":"2017","unstructured":"Li K, Nan Y, Pengfei L, Shimin S, Yalei W, Yang L, Meng L (2017) Multi-label spacecraft electrical signal classification method based on DBN and random forest. PLoS ONE 12(5):1\u201319","journal-title":"PLoS ONE"},{"key":"9760_CR45","unstructured":"Lin J, Keogh E, Lonardi S, Lankford JP, Nystrom DM (2004) VizTree: a tool for visually mining and monitoring massive time series databases. In: The 30th VLDB conference, Toronto, Canada, pp 1269\u20131272"},{"key":"9760_CR46","series-title":"Wiley series in probability and mathematical statistics","first-page":"1","volume-title":"Statistical analysis with missing data","author":"RJA Little","year":"1989","unstructured":"Little RJA, Rubin DB (1989) Statistical analysis with missing data. Wiley series in probability and mathematical statistics. Wiley, New York, pp 1\u2013278"},{"key":"9760_CR47","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.isprsjprs.2019.02.006","volume":"150","author":"W Michael","year":"2019","unstructured":"Michael W, Thomas S, Xiao XZ, Matthias W, Hannes T (2019) Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks. ISPRS J Photogramm Remote Sens 150:59\u201369","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"9760_CR48","unstructured":"Mingliang S, Zhang M, Zhou D, Baolong Z, Shunli L (2017) Fault diagnosis of satellite power system using variable precision fuzzy neighborhood rough set. In: 36th Chinese control conference (CCC), Dalian, China, pp 26\u201328"},{"issue":"26","key":"9760_CR49","first-page":"11","volume":"7","author":"I Minoru","year":"2009","unstructured":"Minoru I, Yoshinobu K, Kohei G, Takehisa Y, Kazuo M (2009) Adaptive limit checking for spacecraft telemetry data using kernel principal component analysis. Trans Jpn Soc Aeronaut Sp Sci Sp Technol Jpn 7(26):11\u201316","journal-title":"Trans Jpn Soc Aeronaut Sp Sci Sp Technol Jpn"},{"issue":"2","key":"9760_CR50","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s11192-011-0468-9","volume":"89","author":"A Muaz","year":"2011","unstructured":"Muaz A, Niazi Hussain A (2011) Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics 89(2):479\u2013499","journal-title":"Scientometrics"},{"key":"9760_CR51","unstructured":"Nishigori N, Hashimoto M, Choki A, Mizutani M (2001) Fully automatic and operator-less anomaly detecting ground support system for Mars probe \u2019Nozomi\u2019. In: 6th international symposium on artificial intelligence and robotics and automation in space (I-SAIRAS)"},{"issue":"5","key":"9760_CR52","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/BF01001956","volume":"11","author":"Z Pawlak","year":"1982","unstructured":"Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341\u2013354","journal-title":"Int J Comput Inf Sci"},{"issue":"11","key":"9760_CR53","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1145\/219717.219791","volume":"38","author":"Z Pawlak","year":"1995","unstructured":"Pawlak Z, Grzymala JB, Slowinski LR, Ziarko PW (1995) Rough sets. Commun ACM 38(11):88\u201395","journal-title":"Commun ACM"},{"key":"9760_CR54","doi-asserted-by":"crossref","unstructured":"Quan L, XingShe Z, Peng L, Shaomin L (2010) Anomaly detection and fault diagnosis technology of spacecraft based on telemetry-mining. In: 2010 3rd international symposium on systems and control in aeronautics and astronautics (ISSCAA). IEEE, Harbin, China, pp 233\u2013236","DOI":"10.1109\/ISSCAA.2010.5633180"},{"key":"9760_CR55","unstructured":"Rish I (2001) An empirical study of the naive Bayes classifier. In: IJCAI workshop on empirical methods in AI, New York, pp 41\u201346"},{"issue":"3","key":"9760_CR56","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1093\/biomet\/63.3.581","volume":"63","author":"DB Rubin","year":"1976","unstructured":"Rubin DB (1976) Inference and missing data. Biometrika 63(3):581\u2013592","journal-title":"Biometrika"},{"key":"9760_CR57","unstructured":"Sary C, Peterson C, Rowe I, Ames T (1998) Trend analysis for spacecraft systems using multi-modal reasoning. In: AAAI spring symposium, pp 157-162"},{"issue":"7","key":"9760_CR58","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1007\/s00521-017-3228-9","volume":"31","author":"GI Sayed","year":"2017","unstructured":"Sayed GI, Darwish A, Hassanien AE (2017) Quantum multiverse optimization algorithm for optimization problems. Neural Comput Appl 31(7):2763\u20132780","journal-title":"Neural Comput Appl"},{"issue":"2","key":"9760_CR59","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1007\/s00357-018-9261-2","volume":"35","author":"GI Sayed","year":"2018","unstructured":"Sayed GI, Darwish A, Ella Hassanien Aboul (2018) A new chaotic whale optimization algorithm for features selection. J Classif 35(2):300\u2013344","journal-title":"J Classif"},{"issue":"1","key":"9760_CR60","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/s10489-018-1261-8","volume":"49","author":"GI Sayed","year":"2019","unstructured":"Sayed GI, Tharwat A, Hassanien AE (2019) Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Appl Intell 49(1):188\u2013205","journal-title":"Appl Intell"},{"issue":"1","key":"9760_CR61","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s00521-017-2988-6","volume":"31","author":"GI Sayed","year":"2019","unstructured":"Sayed GI, Hassanien AE, Taher AA (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31(1):171\u2013188","journal-title":"Neural Comput Appl"},{"key":"9760_CR62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1201\/9781439821862","volume-title":"Analysis of incomplete multivariate data","author":"JL Schafer","year":"1997","unstructured":"Schafer JL (1997) Analysis of incomplete multivariate data. Chapman & Hall, London, pp 1\u2013444"},{"issue":"1","key":"9760_CR63","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/TNN.2008.2004373","volume":"20","author":"HA Talebi","year":"2009","unstructured":"Talebi HA, Khorasani K, Tafazoli S (2009) A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite\u2019s attitude control subsystem. IEEE Trans Neural Netw 20(1):46\u201360","journal-title":"IEEE Trans Neural Netw"},{"issue":"6","key":"9760_CR64","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.1016\/j.asr.2017.12.032","volume":"61","author":"MM Tavakoli","year":"2018","unstructured":"Tavakoli MM, Assadian N (2018) Predictive fault-tolerant control of an all-thruster satellite in 6-DOF motion via neural network model updating. Adv Sp Res 61(6):1588\u20131599","journal-title":"Adv Sp Res"},{"key":"9760_CR65","doi-asserted-by":"publisher","DOI":"10.1007\/s00357-018-9299-1","author":"A Tharwat","year":"2019","unstructured":"Tharwat A, Hassanien AE (2019) Quantum-behaved particle swarm optimization for parameter optimization of support vector machine. J Classif. \nhttps:\/\/doi.org\/10.1007\/s00357-018-9299-1","journal-title":"J Classif"},{"issue":"8","key":"9760_CR66","doi-asserted-by":"crossref","first-page":"2268","DOI":"10.1007\/s10489-017-1074-1","volume":"48","author":"A Tharwat","year":"2018","unstructured":"Tharwat A, Houssein EH, Ahmed MM, Hassanien AE, Gabel T (2018) Mogoa algorithm for constrained and unconstrained multi-objective optimization problems. Appl Intelli 48(8):2268\u20132283","journal-title":"Appl Intelli"},{"key":"9760_CR67","first-page":"211","volume":"1","author":"M Tipping","year":"2001","unstructured":"Tipping M (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211\u2013244","journal-title":"J Mach Learn Res"},{"key":"9760_CR68","doi-asserted-by":"crossref","first-page":"83461","DOI":"10.1109\/ACCESS.2019.2922835","volume":"7","author":"M Torky","year":"2019","unstructured":"Torky M, Hassanien AE, El Fiky AH, Yazeed Alsbou (2019) Analyzing space debris flux and predicting satellites collision probability in LEO orbits based on Petri nets. IEEE Access 7:83461\u201383473","journal-title":"IEEE Access"},{"key":"9760_CR69","unstructured":"Torky M, Hassanien AE (2018) Orbital Petri nets: a novel Petri net approach, pp 1\u201316. \narXiv:1806.03267"},{"issue":"2","key":"9760_CR70","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/91.669012","volume":"6","author":"M Walter","year":"1998","unstructured":"Walter M, Buijtenen V, Gerard S, Babuska R, Verbruggen HB (1998) Adaptive fuzzy control of satellite attitude by reinforcement learning. IEEE Trans Fuzzy Syst 6(2):185\u2013194","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"1","key":"9760_CR71","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1108\/AEAT-11-2012-0221","volume":"87","author":"G Wang","year":"2015","unstructured":"Wang G, Qiang L, Jinglin S, Xiaofeng M (2015) Telemetry data processing flow model: a case study. Aircr Eng Aerosp Technol Int J 87(1):52\u201358","journal-title":"Aircr Eng Aerosp Technol Int J"},{"key":"9760_CR72","unstructured":"Wijk J, Selow E (1999) Cluster and calendar based visualization of time series data. In: IEEE symposium on information visualization (infovis. i 99), San Francisco, CA, pp 4\u20139"},{"key":"9760_CR73","unstructured":"Williams B C, Nayak P P (1996) A model-based approach to reactive self-configuring systems. In: The thirteenth national conference on artificial intelligence, pp 971\u2013978"},{"key":"9760_CR74","doi-asserted-by":"crossref","unstructured":"Yairi T, Nakatsugawa M, Hori K, Nakasuka S, Machida K, Ishihama N (2004) Adaptive limit checking for spacecraft telemetry data using regression tree learning. In: 2004 IEEE international conference systems, man and cybernetics. IEEE, The Hague, The Netherlands, pp 5130\u20135135","DOI":"10.1109\/ICSMC.2004.1401008"},{"key":"9760_CR75","unstructured":"Yairi T, Kawahara Y, Fujimaki R, Sato Y, Machida K (2006) Telemetry-mining: a machine learning approach to anomaly detection and fault diagnosis for space systems. In: 2nd IEEE international conference on space mission challenges for information technology. IEEE, CA, USA, pp 8\u201315"},{"issue":"3","key":"9760_CR76","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1109\/TAES.2017.2671247","volume":"53","author":"T Yairi","year":"2017","unstructured":"Yairi T, Takeishi N, Oda T, Nakajima Y, Nishimura N, Takata N (2017) A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction. IEEE Trans Aerosp Electron Syst 53(3):1384\u20131401","journal-title":"IEEE Trans Aerosp Electron Syst"},{"issue":"2","key":"9760_CR77","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.asr.2018.10.002","volume":"63","author":"F Yao","year":"2018","unstructured":"Yao F, Li J, Chen Y, Chu X, Zhao B (2018) Task allocation strategies for cooperative task planning of multi-autonomous satellite constellation. Adv Sp Res 63(2):1073\u20131084","journal-title":"Adv Sp Res"},{"key":"9760_CR78","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.rse.2018.04.026","volume":"211","author":"G Yongnian","year":"2018","unstructured":"Yongnian G, Qin L, Shuangshuang W, Junfeng G (2018) Remote sensing of environment adaptive neural network based on segmented particle swarm optimization for remote-sensing estimations of vegetation biomass. Remote Sens Environ 211:248\u2013260","journal-title":"Remote Sens Environ"},{"issue":"456","key":"9760_CR79","first-page":"67","volume":"99","author":"L Yoonkyung","year":"2004","unstructured":"Yoonkyung L, Lin Y, Wahba G (2004) Multicategory support vector machines. J Am Stat Assoc 99(456):67\u201381","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"9760_CR80","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338\u2013353","journal-title":"Inf Control"},{"key":"9760_CR81","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.eswa.2018.12.029","volume":"121","author":"M Zein","year":"2019","unstructured":"Zein M, Adl A, Hassanien AE (2019) Spiking neural P grey wolf optimization system: novel strategies for solving non-determinism problems. Expert Syst Appl 121:204\u2013220","journal-title":"Expert Syst Appl"},{"issue":"19","key":"9760_CR82","doi-asserted-by":"crossref","first-page":"370","DOI":"10.21037\/atm.2016.06.20","volume":"4","author":"Z Zhang","year":"2016","unstructured":"Zhang Z (2016) A gentle introduction to artificial neural networks. Ann Transl Med 4(19):370\u2013375","journal-title":"Ann Transl Med"},{"key":"9760_CR83","doi-asserted-by":"crossref","unstructured":"Zhang K, Jianwu X, Martin MR, Guofei J, Pelechrinis K, Zhang H (2016) Automated IT system failure prediction: a deep learning approach. In: 2016 IEEE international conference on big data (big data), Washington, DC, USA, pp 1291\u20131300","DOI":"10.1109\/BigData.2016.7840733"},{"key":"9760_CR84","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.actaastro.2017.04.027","volume":"137","author":"Z Zheng","year":"2017","unstructured":"Zheng Z, Jian G, Eberhard G (2017) Swarm satellite mission scheduling & planning using hybrid dynamic mutation genetic algorithm. Acta Astronaut 137:243\u2013253","journal-title":"Acta Astronaut"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-019-09760-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10462-019-09760-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-019-09760-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T23:05:13Z","timestamp":1599087913000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10462-019-09760-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,4]]},"references-count":84,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["9760"],"URL":"https:\/\/doi.org\/10.1007\/s10462-019-09760-1","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,4]]},"assertion":[{"value":"4 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}