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Machine Learning and Data Mining in Aerospace Technology

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Cover of 'Machine Learning and Data Mining in Aerospace Technology'

Table of Contents

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    Book Overview
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    Chapter 1 Tensor-Based Anomaly Detection for Satellite Telemetry Data
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    Chapter 2 Machine Learning in Satellites Monitoring and Risk Challenges
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    Chapter 3 Formalization, Prediction and Recognition of Expert Evaluations of Telemetric Data of Artificial Satellites Based on Type-II Fuzzy Sets
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    Chapter 4 Intelligent Health Monitoring Systems for Space Missions Based on Data Mining Techniques
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    Chapter 5 Design, Implementation, and Validation of Satellite Simulator and Data Packets Analysis
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    Chapter 6 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
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    Chapter 7 Data Analytics Using Satellite Remote Sensing in Healthcare Applications
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    Chapter 8 Design, Implementation, and Testing of Unpacking System for Telemetry Data of Artificial Satellites: Case Study: EGYSAT1
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    Chapter 9 Multiscale Satellite Image Classification Using Deep Learning Approach
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    Chapter 10 Security Approaches in Machine Learning for Satellite Communication
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    Chapter 11 Machine Learning Techniques for IoT Intrusions Detection in Aerospace Cyber-Physical Systems
Attention for Chapter 11: Machine Learning Techniques for IoT Intrusions Detection in Aerospace Cyber-Physical Systems
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Citations

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Chapter title
Machine Learning Techniques for IoT Intrusions Detection in Aerospace Cyber-Physical Systems
Chapter number 11
Book title
Machine Learning and Data Mining in Aerospace Technology
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-20212-5_11
Book ISBNs
978-3-03-020211-8, 978-3-03-020212-5
Authors

Yassine Maleh, Maleh, Yassine

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 12%
Student > Master 6 12%
Lecturer 5 10%
Unspecified 3 6%
Researcher 3 6%
Other 7 14%
Unknown 20 40%
Readers by discipline Count As %
Computer Science 13 26%
Engineering 5 10%
Unspecified 3 6%
Business, Management and Accounting 2 4%
Linguistics 1 2%
Other 4 8%
Unknown 22 44%

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