Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms

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  • Author : Enrico Camporeale
  • Publisher : Elsevier
  • Pages : 454 pages
  • ISBN : 0128117893
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMachine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather
  • Author : Enrico Camporeale,Simon Wing,Jay Johnson
  • Publisher : Elsevier
  • Release : 31 May 2018
GET THIS BOOKMachine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms.

The Dynamical Ionosphere

The Dynamical Ionosphere
  • Author : Massimo Materassi,Biagio Forte,Anthea J. Coster,Susan Skone
  • Publisher : Elsevier
  • Release : 28 November 2019
GET THIS BOOKThe Dynamical Ionosphere

The Dynamical Ionosphere: A Systems Approach to Ionospheric Irregularity examines the Earth’s ionosphere as a dynamical system with signatures of complexity. The system is robust in its overall configuration, with smooth space-time patterns of daily, seasonal and Solar Cycle variability, but shows a hierarchy of interactions among its sub-systems, yielding apparent unpredictability, space-time irregularity, and turbulence. This interplay leads to the need for constructing realistic models of the average ionosphere, incorporating the increasing knowledge and predictability of high variability

Machine Learning Techniques for Gait Biometric Recognition

Machine Learning Techniques for Gait Biometric Recognition
  • Author : James Eric Mason,Issa Traoré,Isaac Woungang
  • Publisher : Springer
  • Release : 04 February 2016
GET THIS BOOKMachine Learning Techniques for Gait Biometric Recognition

This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that

Earth Observation Open Science and Innovation

Earth Observation Open Science and Innovation
  • Author : Pierre-Philippe Mathieu,Christoph Aubrecht
  • Publisher : Springer
  • Release : 23 January 2018
GET THIS BOOKEarth Observation Open Science and Innovation

This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well

Machine Learning and Data Mining in Aerospace Technology

Machine Learning and Data Mining in Aerospace Technology
  • Author : Aboul Ella Hassanien,Ashraf Darwish,Hesham El-Askary
  • Publisher : Springer
  • Release : 02 July 2019
GET THIS BOOKMachine Learning and Data Mining in Aerospace Technology

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

Deep Learning

Deep Learning
  • Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
  • Publisher : MIT Press
  • Release : 10 November 2016
GET THIS BOOKDeep Learning

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.

Space Weather

Space Weather
  • Author : Volker Bothmer,Ioannis A. Daglis
  • Publisher : Springer Science & Business Media
  • Release : 10 January 2007
GET THIS BOOKSpace Weather

The editors present a state-of-the-art overview on the Physics of Space Weather and its effects on technological and biological systems on the ground and in space. It opens with a general introduction on the subject, followed by a historical review on the major developments in the field of solar terrestrial relationships leading to its development into the up-to-date field of space weather. Specific emphasis is placed on the technological effects that have impacted society in the past century at times

Deep Learning with Python

Deep Learning with Python
  • Author : Francois Chollet
  • Publisher : Manning Publications
  • Release : 28 October 2017
GET THIS BOOKDeep Learning with Python

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition,

TinyML

TinyML
  • Author : Pete Warden,Daniel Situnayake
  • Publisher : O'Reilly Media
  • Release : 16 December 2019
GET THIS BOOKTinyML

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who

Soft Computing in Industrial Applications

Soft Computing in Industrial Applications
  • Author : Ashraf Saad,Erel Avineri,Keshav Dahal,Muhammad Sarfraz,Rajkumar Roy
  • Publisher : Springer Science & Business Media
  • Release : 07 August 2007
GET THIS BOOKSoft Computing in Industrial Applications

Here is a collection of papers presented at the 11th On-line World Conference on Soft Computing in Industrial Applications, held in September-October 2006. This carefully edited book provides a comprehensive overview of recent advances in the industrial applications of soft computing and covers a wide range of application areas, including data analysis and data mining, computer graphics, intelligent control, systems, pattern recognition, classifiers, as well as modeling optimization.

Deep Learning with PyTorch

Deep Learning with PyTorch
  • Author : Eli Stevens,Luca Antiga,Thomas Viehmann
  • Publisher : Manning Publications
  • Release : 04 August 2020
GET THIS BOOKDeep Learning with PyTorch

Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Summary Every other day we hear about new

Feature Engineering and Selection

Feature Engineering and Selection
  • Author : Max Kuhn,Kjell Johnson
  • Publisher : CRC Press
  • Release : 25 July 2019
GET THIS BOOKFeature Engineering and Selection

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

Machine Learning with SAS

Machine Learning with SAS
  • Author : Anonim
  • Publisher : Unknown Publisher
  • Release : 21 June 2019
GET THIS BOOKMachine Learning with SAS

Machine learning is a branch of artificial intelligence (AI) that develops algorithms that allow computers to learn from examples without being explicitly programmed. Machine learning identifies patterns in the data and models the results. These descriptive models enable a better understanding of the underlying insights the data offers. Machine learning is a powerful tool with many applications, from real-time fraud detection, the Internet of Things (IoT), recommender systems, and smart cars. It will not be long before some form of