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Machine Learning and Data Mining in Aerospace Technology, Hassanien Aboul Ella, Darwish Ashraf, El-Askary Hesham


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Автор: Hassanien Aboul Ella, Darwish Ashraf, El-Askary Hesham
Название:  Machine Learning and Data Mining in Aerospace Technology
ISBN: 9783030202149
Издательство: Springer
Классификация:

ISBN-10: 3030202143
Обложка/Формат: Paperback
Страницы: 232
Вес: 0.34 кг.
Дата издания: 14.08.2020
Серия: Studies in computational intelligence
Язык: English
Издание: 1st ed. 2020
Иллюстрации: 62 illustrations, color; 35 illustrations, black and white; viii, 232 p. 97 illus., 62 illus. in color.
Размер: 23.39 x 15.60 x 1.30 cm
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.


Machine Learning and Data Mining in Aerospace Technology

Автор: Aboul Ella Hassanien, Ashraf Darwish
Название: Machine Learning and Data Mining in Aerospace Technology
ISBN: 3030202119 ISBN-13(EAN): 9783030202118
Издательство: Springer
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Цена: 27950.00 р.
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Описание: 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.

An Introduction to Machine Learning

Автор: Miroslav Kubat
Название: An Introduction to Machine Learning
ISBN: 3319348868 ISBN-13(EAN): 9783319348865
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.

Statistics, data mining, and machine learning in astronomy :

Автор: Ivezic?, Z?eljko,
Название: Statistics, data mining, and machine learning in astronomy :
ISBN: 0691198306 ISBN-13(EAN): 9780691198309
Издательство: Wiley
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Цена: 12355.00 р.
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Описание:

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.

An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.

  • Fully revised and expanded
  • Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets
  • Features real-world data sets from astronomical surveys
  • Uses a freely available Python codebase throughout
  • Ideal for graduate students, advanced undergraduates, and working astronomers

The Art of Feature Engineering: Essentials for Machine Learning

Автор: Pablo Duboue
Название: The Art of Feature Engineering: Essentials for Machine Learning
ISBN: 1108709389 ISBN-13(EAN): 9781108709385
Издательство: Cambridge Academ
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Цена: 6970.00 р.
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Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.

Principles and Theory for Data Mining and Machine Learning

Автор: Clarke
Название: Principles and Theory for Data Mining and Machine Learning
ISBN: 0387981349 ISBN-13(EAN): 9780387981345
Издательство: Springer
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Цена: 27950.00 р.
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Описание: Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering

Principles and Theory for Data Mining and Machine Learning

Автор: Bertrand Clarke; Ernest Fokoue; Hao Helen Zhang
Название: Principles and Theory for Data Mining and Machine Learning
ISBN: 1461417074 ISBN-13(EAN): 9781461417071
Издательство: Springer
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Цена: 21661.00 р.
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Описание: This book provides a thorough introduction to the most important topics in data mining and machine learning. All the topics covered have undergone rapid development and this treatment offers a modern perspective emphasizing the most recent contributions.

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

Автор: Clara Pizzuti; Marylyn D. Ritchie; Mario Giacobini
Название: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
ISBN: 3642011837 ISBN-13(EAN): 9783642011832
Издательство: Springer
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Цена: 9781.00 р.
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Описание: Constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009, held in Tubingen, Germany, in April 2009 co located with the Evo 2009 events. This book includes such topics as biomarker discovery, cell simulation and modeling, and ecological modeling.

Machine Learning for Protein Subcellular Localization Prediction

Автор: Shibiao Wan,Man-Wai Mak
Название: Machine Learning for Protein Subcellular Localization Prediction
ISBN: 1501510487 ISBN-13(EAN): 9781501510489
Издательство: Walter de Gruyter
Цена: 13008.00 р.
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Описание: Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.

Machine Learning and Data Mining in Pattern Recognition

Автор: Perner
Название: Machine Learning and Data Mining in Pattern Recognition
ISBN: 3319419196 ISBN-13(EAN): 9783319419190
Издательство: Springer
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Цена: 13416.00 р.
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Описание: The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.

Machine Learning and Data Mining in Pattern Recognition

Автор: Petra Perner
Название: Machine Learning and Data Mining in Pattern Recognition
ISBN: 3319624156 ISBN-13(EAN): 9783319624150
Издательство: Springer
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Цена: 9781.00 р.
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Описание: The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

Machine Learning and Data Mining in Pattern Recognition

Автор: Petra Perner
Название: Machine Learning and Data Mining in Pattern Recognition
ISBN: 3319210238 ISBN-13(EAN): 9783319210230
Издательство: Springer
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Цена: 8944.00 р.
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Описание: The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.

Machine Learning and Data Mining for Computer Security

Автор: Marcus A. Maloof
Название: Machine Learning and Data Mining for Computer Security
ISBN: 1849965447 ISBN-13(EAN): 9781849965446
Издательство: Springer
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Цена: 19564.00 р.
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Описание: "Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security.


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