Utvidet returrett til 31. januar 2025

Machine Learning and Data Mining in Aerospace Technology

Om Machine Learning and Data Mining in Aerospace Technology

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.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9783030202149
  • Bindende:
  • Paperback
  • Sider:
  • 232
  • Utgitt:
  • 14. august 2020
  • Utgave:
  • 12020
  • Dimensjoner:
  • 155x235x0 mm.
  • Vekt:
  • 454 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 21. desember 2024
Utvidet returrett til 31. januar 2025

Beskrivelse av Machine Learning and Data Mining in Aerospace Technology

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.

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