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Machine Learning For Planetary Science

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Machine Learning for Planetary Science

Machine Learning for Planetary Science Book
Author : Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
Publisher : Elsevier
Release : 2021-03-01
ISBN : 0128187220
Language : En, Es, Fr & De

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Book Description :

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences Book
Author : N.A
Publisher : Academic Press
Release : 2020-09-25
ISBN : 0128216840
Language : En, Es, Fr & De

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Book Description :

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. Provides high-level reviews of the latest innovations in geophysics Written by recognized experts in the field Presents an essential publication for researchers in all fields of geophysics

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather Book
Author : Enrico Camporeale,Simon Wing,Jay Johnson
Publisher : Elsevier
Release : 2018-05-31
ISBN : 0128117893
Language : En, Es, Fr & De

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Book Description :

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

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy Book
Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publisher : CRC Press
Release : 2012-03-29
ISBN : 143984173X
Language : En, Es, Fr & De

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Book Description :

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation Book
Author : Petr Skoda,Fathalrahman Adam
Publisher : Elsevier
Release : 2020-04-10
ISBN : 0128191554
Language : En, Es, Fr & De

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Book Description :

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin Brazil

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin  Brazil Book
Author : Elezhan Zhakiya
Publisher :
Release : 2016
ISBN :
Language : En, Es, Fr & De

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Book Description :

Machine Learning techniques are being widely used in Social Sciences to find connections amongst various variables. Machine Learning connects features across different fields that do not seem to have known mathematical relationships with each other. In natural resource prospecting, machine learning can be applied to connect geochemical, geophysical, and geological variables. However, the biggest challenge in machine learning remains obtaining the data to train the ML algorithms. Here, we have applied machine learning on data extracted from maps via image processing. While the overall accuracy of prediction remains as low as 33% at this stage, we see places where the algorithm can be improved and the accuracy increased.

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition Book
Author : Petra Perner
Publisher : Springer Science & Business Media
Release : 2009-07-21
ISBN : 364203070X
Language : En, Es, Fr & De

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Book Description :

There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. 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. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.

Space Science and Astronomy Theatre

Space Science and Astronomy Theatre Book
Author : Margaret Boone Rappaport,Christopher J. Corbally
Publisher : Archway Publishing
Release : 2017-08-09
ISBN : 148084845X
Language : En, Es, Fr & De

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Book Description :

If youre a teacher or parent struggling to get youngsters or young adults interested in space science and astronomyor an inquisitive studentthen youll love this fun-filled book of theatrical scenes. In addition to astronomers and astronauts, the scenes also feature engineers, accountants, graphic artists, public relations practitioners, biologists, meteorologists, and others who play a critical role in space adventures. Scenarios will take you into the past and into the future and include: A cosmologist and a computer graphics artist are preparing a presentation for public television on theories about the distribution of galaxies in the universe, and the placement of voids where no galaxies are found. An astrobiologist and an engineer discover the first positive biosignature data from an exoplanet near Earth. The findings provide a big surprise. Two recent high school graduates explore a star factory (nebula) in the constellation Orion, and using a video arcade game, they make speculations about the future. While the props and costumes needed for scripts are minimal, the scenes promote deep learning. Get ready to be entertained and informed with Space Science and Astronomy Theatre.

Soft Computing in Machine Learning

Soft Computing in Machine Learning Book
Author : Sang-Yong Rhee,Jooyoung Park,Atsushi Inoue
Publisher : Springer
Release : 2014-07-08
ISBN : 331905533X
Language : En, Es, Fr & De

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Book Description :

As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It consists of 11 contributions that features illumination change detection, generator of electronic educational publications, intelligent call triage system, recognition of rocks at uranium deposits, graphics processing units, mathematical model of hit phenomena, selection and mutation in genetic algorithm, hands and arms motion estimation, application of wavelet network, Kanizsa triangle illusion, and support vector machine regression. Also, it describes how to apply the machine learning for the intelligent systems. This edition is published in original, peer reviewed contributions covering from initial design to final prototypes and verifications.

Earth and Space Science Information Systems

Earth and Space Science Information Systems Book
Author : Arthur Zygielbaum
Publisher : A I P Press
Release : 1994
ISBN :
Language : En, Es, Fr & De

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Book Description :

The ISY Conference was organized to promote and enhance international scientific communication and co-operation for the collection, processing, archiving, distribution and analysis of earth and space science data. These are the proceedings of this conference.

Using Machine Learning Particle Tracking and Grain Shape Modeling to Characterize Bedload Sediment Transport

Using Machine Learning  Particle Tracking  and Grain Shape Modeling to Characterize Bedload Sediment Transport Book
Author : Matthew R. Rushlow (S.B.)
Publisher :
Release : 2020
ISBN :
Language : En, Es, Fr & De

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Book Description :

Rivers are generally understood through their bulk characteristics and on a river by river scale, while the motion and characteristics of the individual sediment that progresses through those rivers is poorly understood. This project sought to track the bed-load transport of individual natural and artificial sediment grains through a flume to understand the effects of grain shape on motion, and creation of multi spherical approximations of natural sediment grains for use in numerical simulations. Machine learning tools processed the position of millions of grains through a flume. Successful identification and tracking of nearly 75% of all grains within a flume, and multi spherical approximations of natural grains using 20 spheres or less that reproduced important shape characteristics of natural grains were achieved. Accurate grain locations allowed the possibility for velocities, accelerations, entrainments, and flux to be studied with uniquely high resolution. Efficient flume simulations that better represent actual sediment became possible.

Space and Humanity

Space and Humanity Book
Author : L. G. Napolitano
Publisher : Elsevier
Release : 2013-10-22
ISBN : 148328722X
Language : En, Es, Fr & De

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Book Description :

This volume contains a selection of 27 papers that are chiefly survey, state-of-the-art, review or programmatic in nature. The volume itself is structured in three parts: Part I, The System, that deals with Space Transportation, Space Stations and Platforms; Part II, Hard and Soft Technologies, that deals with Technology Applications, Astrodynamics, Space Power and Propulsion; Part III, The Utilization, that addresses the two main lines Space Exploration and Applications, including Earth Observation, Telecommunication and Space Education, Microgravity, Safety and Rescue.

AAAI 99

AAAI 99 Book
Author : American Association for Artificial Intelligence
Publisher : Aaai Press
Release : 1999
ISBN :
Language : En, Es, Fr & De

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Book Description :

The annual AAAI National Conference provides a forum for information exchange and interaction among researchers from all disciplines of AI. Contributions include theoretical, experimental, and empirical results. Topics cover principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform. Distributed for the AAAI Press

Computational Intelligence in Archaeology

Computational Intelligence in Archaeology Book
Author : Barcelo, Juan A.
Publisher : IGI Global
Release : 2008-07-31
ISBN : 1599044919
Language : En, Es, Fr & De

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Book Description :

Provides analytical theories offered by innovative artificial intelligence computing methods in the archaeological domain.

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification Book
Author : Anil Kumar,Priyadarshi Upadhyay,A. Senthil Kumar
Publisher : CRC Press
Release : 2020-08-30
ISBN : 1000091546
Language : En, Es, Fr & De

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Book Description :

This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.

Machine Learning and Statistical Modeling Approaches to Image Retrieval

Machine Learning and Statistical Modeling Approaches to Image Retrieval Book
Author : Yixin Chen,Jia Li,James Z. Wang
Publisher : Springer Science & Business Media
Release : 2006-04-11
ISBN : 1402080352
Language : En, Es, Fr & De

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Book Description :

In the early 1990s, the establishment of the Internet brought forth a revolutionary viewpoint of information storage, distribution, and processing: the World Wide Web is becoming an enormous and expanding distributed digital library. Along with the development of the Web, image indexing and retrieval have grown into research areas sharing a vision of intelligent agents. Far beyond Web searching, image indexing and retrieval can potentially be applied to many other areas, including biomedicine, space science, biometric identification, digital libraries, the military, education, commerce, culture and entertainment. Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results. The topics of this book reflect authors' experiences of machine learning and statistical modeling based image indexing and retrieval. This book contains detailed references for further reading and research in this field as well.