Machine Learning for Planetary Science

Machine Learning for Planetary Science
Author: Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
Publsiher: Elsevier
Total Pages: 232
Release: 2022-03-25
ISBN 10: 0128187220
ISBN 13: 9780128187227
Language: EN, FR, DE, ES & NL

Machine Learning for Planetary Science Book Review:

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 in Heliophysics

Machine Learning in Heliophysics
Author: Thomas Berger,Enrico Camporeale,Bala Poduval,Veronique A. Delouille,Sophie A. Murray
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2021-11-24
ISBN 10: 2889716716
ISBN 13: 9782889716715
Language: EN, FR, DE, ES & NL

Machine Learning in Heliophysics Book Review:

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather
Author: Enrico Camporeale,Simon Wing,Jay Johnson
Publsiher: Elsevier
Total Pages: 454
Release: 2018-05-31
ISBN 10: 0128117893
ISBN 13: 9780128117897
Language: EN, FR, DE, ES & NL

Machine Learning Techniques for Space Weather Book Review:

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

Planetary Remote Sensing and Mapping

Planetary Remote Sensing and Mapping
Author: Bo Wu,Kaichang Di,Jürgen Oberst,Irina Karachevtseva
Publsiher: CRC Press
Total Pages: 332
Release: 2018-10-29
ISBN 10: 0429000502
ISBN 13: 9780429000508
Language: EN, FR, DE, ES & NL

Planetary Remote Sensing and Mapping Book Review:

The early 21st century marks a new era in space exploration. The National Aeronautics and Space Administration (NASA) of the United States, The European Space Agency (ESA), as well as space agencies of Japan, China, India, and other countries have sent their probes to the Moon, Mars, and other planets in the solar system. Planetary Remote Sensing and Mapping introduces original research and new developments in the areas of planetary remote sensing, photogrammetry, mapping, GIS, and planetary science resulting from the recent space exploration missions. Topics covered include: Reference systems of planetary bodies Planetary exploration missions and sensors Geometric information extraction from planetary remote sensing data Feature information extraction from planetary remote sensing data Planetary remote sensing data fusion Planetary data management and presentation Planetary Remote Sensing and Mapping will serve scientists and professionals working in the planetary remote sensing and mapping areas, as well as planetary probe designers, engineers, and planetary geologists and geophysicists. It also provides useful reading material for university teachers and students in the broader areas of remote sensing, photogrammetry, cartography, GIS, and geodesy.

Applications of Convolutional Neural Networks to Problems in Astronomy and Planetary Science

Applications of Convolutional Neural Networks to Problems in Astronomy and Planetary Science
Author: Emma Torres Chickles
Publsiher: Unknown
Total Pages: 72
Release: 2021
ISBN 10: 1928374650XXX
ISBN 13: OCLC:1261320797
Language: EN, FR, DE, ES & NL

Applications of Convolutional Neural Networks to Problems in Astronomy and Planetary Science Book Review:

Large-scale astronomical surveys and planetary missions have produced huge amounts of data. The exponential growth in data volume has allowed the application of novel data science techniques, including machine learning. We use machine learning methods to analyze and extract new information from two enormous datasets: images of impact craters captured by the Mars Reconnaissance Orbiter (MRO) and time-series data collected by the Transiting Exoplanet Survey Satellite (TESS). Using images of impact craters captured by the MRO, we infer the spatial variation in the retention of ejecta deposits on Mars. We do this by training a convolutional neural network (CNN) to detect the presence of ejecta deposits around small craters. Our machine learning method to detect pre- served ejecta deposits will enable the study of the processes driving landscape evolution on Mars. In a methodologically relevant but independent study, we conduct a census of different types of variability of nearby stars using photometric time-series data produced by TESS. We do this by extracting representational features from light curves using a convolutional autoencoder and clustering these features. Our unsupervised machine learning method will accelerate the augmentation of variable star catalogues, which are essential for studies of stellar magnetism, stellar evolution, and the habitability of hosted exoplanets.

Deep Learning for the Earth Sciences

Deep Learning for the Earth Sciences
Author: Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Publsiher: John Wiley & Sons
Total Pages: 432
Release: 2021-08-18
ISBN 10: 1119646162
ISBN 13: 9781119646167
Language: EN, FR, DE, ES & NL

Deep Learning for the Earth Sciences Book Review:

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Advances in Subsurface Data Analytics

Advances in Subsurface Data Analytics
Author: Shuvajit Bhattacharya,Haibin Di
Publsiher: Elsevier
Total Pages: 376
Release: 2022-05-27
ISBN 10: 0128223081
ISBN 13: 9780128223086
Language: EN, FR, DE, ES & NL

Advances in Subsurface Data Analytics Book Review:

Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world Offers an analysis of future trends in machine learning in geosciences

Machine Learning

Machine Learning
Author: Yagang Zhang
Publsiher: BoD – Books on Demand
Total Pages: 446
Release: 2010-02-01
ISBN 10: 9533070331
ISBN 13: 9789533070339
Language: EN, FR, DE, ES & NL

Machine Learning Book Review:

Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning. The author also attempts to promote a new design of thinking machines and development philosophy. Considering the growing complexity and serious difficulties of information processing in machine learning, in Part II of the book, the theoretical foundations of machine learning are considered, and they mainly include self-organizing maps (SOMs), clustering, artificial neural networks, nonlinear control, fuzzy system and knowledge-based system (KBS). Part III contains selected applications of various machine learning approaches, from flight delays, network intrusion, immune system, ship design to CT and RNA target prediction. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners.

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
Author: Anonim
Publsiher: Academic Press
Total Pages: 316
Release: 2020-09-25
ISBN 10: 0128216840
ISBN 13: 9780128216842
Language: EN, FR, DE, ES & NL

Machine Learning and Artificial Intelligence in Geosciences Book Review:

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 Optimization and Data Science

Machine Learning  Optimization  and Data Science
Author: Giuseppe Nicosia,Varun Ojha,Emanuele La Malfa,Giorgio Jansen,Vincenzo Sciacca,Panos Pardalos,Giovanni Giuffrida,Renato Umeton
Publsiher: Springer Nature
Total Pages: 666
Release: 2021-01-06
ISBN 10: 3030645800
ISBN 13: 9783030645809
Language: EN, FR, DE, ES & NL

Machine Learning Optimization and Data Science Book Review:

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Large Meteorite Impacts and Planetary Evolution VI

Large Meteorite Impacts and Planetary Evolution VI
Author: Wolf Uwe Reimold,Christian Koeberl
Publsiher: Geological Society of America
Total Pages: 644
Release: 2021-09-23
ISBN 10: 081372550X
ISBN 13: 9780813725505
Language: EN, FR, DE, ES & NL

Large Meteorite Impacts and Planetary Evolution VI Book Review:

"This volume contains a sizable suite of contributions dealing with regional impact records (Australia, Sweden), impact craters and impactites, early Archean impacts and geophysical characteristics of impact structures, shock metamorphic investigations, post-impact hydrothermalism, and structural geology and morphometry of impact structures - on Earth and Mars"--

Discovery Science

Discovery Science
Author: Nathalie Japkowicz,Stan Matwin
Publsiher: Springer
Total Pages: 342
Release: 2015-10-04
ISBN 10: 3319242822
ISBN 13: 9783319242828
Language: EN, FR, DE, ES & NL

Discovery Science Book Review:

This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2015, held in banff, AB, Canada in October 2015. The 16 long and 12 short papers presendted together with 4 invited talks in this volume were carefully reviewed and selected from 44 submissions. The combination of recent advances in the development and analysis of methods for discovering scienti c knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scienti c domains, on the one hand, with the algorithmic advances in machine learning theory, on the other hand, makes every instance of this joint event unique and attractive.

Algorithmic Learning Theory

Algorithmic Learning Theory
Author: Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles
Publsiher: Springer
Total Pages: 395
Release: 2015-10-04
ISBN 10: 3319244868
ISBN 13: 9783319244860
Language: EN, FR, DE, ES & NL

Algorithmic Learning Theory Book Review:

This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.

An Astrobiology Strategy for the Search for Life in the Universe

An Astrobiology Strategy for the Search for Life in the Universe
Author: National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Space Studies Board,Committee on Astrobiology Science Strategy for the Search for Life in the Universe
Publsiher: National Academies Press
Total Pages: 188
Release: 2019-04-20
ISBN 10: 0309484162
ISBN 13: 9780309484169
Language: EN, FR, DE, ES & NL

An Astrobiology Strategy for the Search for Life in the Universe Book Review:

Astrobiology is the study of the origin, evolution, distribution, and future of life in the universe. It is an inherently interdisciplinary field that encompasses astronomy, biology, geology, heliophysics, and planetary science, including complementary laboratory activities and field studies conducted in a wide range of terrestrial environments. Combining inherent scientific interest and public appeal, the search for life in the solar system and beyond provides a scientific rationale for many current and future activities carried out by the National Aeronautics and Science Administration (NASA) and other national and international agencies and organizations. Requested by NASA, this study offers a science strategy for astrobiology that outlines key scientific questions, identifies the most promising research in the field, and indicates the extent to which the mission priorities in existing decadal surveys address the search for life's origin, evolution, distribution, and future in the universe. This report makes recommendations for advancing the research, obtaining the measurements, and realizing NASA's goal to search for signs of life in the universe.

The Atlas of AI

The Atlas of AI
Author: Kate Crawford
Publsiher: Yale University Press
Total Pages: 336
Release: 2021-04-06
ISBN 10: 0300209576
ISBN 13: 9780300209570
Language: EN, FR, DE, ES & NL

The Atlas of AI Book Review:

The hidden costs of artificial intelligence, from natural resources and labor to privacy and freedom What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind "automated" services, to the data AI collects from us. Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world.

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
Author: Petr Skoda,Fathalrahman Adam
Publsiher: Elsevier
Total Pages: 472
Release: 2020-04-10
ISBN 10: 0128191554
ISBN 13: 9780128191552
Language: EN, FR, DE, ES & NL

Knowledge Discovery in Big Data from Astronomy and Earth Observation Book Review:

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

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
Author: Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publsiher: CRC Press
Total Pages: 744
Release: 2012-03-29
ISBN 10: 1439841748
ISBN 13: 9781439841747
Language: EN, FR, DE, ES & NL

Advances in Machine Learning and Data Mining for Astronomy Book Review:

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

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
Author: Ulf Brefeld,Elisa Fromont,Andreas Hotho,Arno Knobbe,Marloes Maathuis,Céline Robardet
Publsiher: Springer Nature
Total Pages: 804
Release: 2020-04-30
ISBN 10: 3030461335
ISBN 13: 9783030461331
Language: EN, FR, DE, ES & NL

Machine Learning and Knowledge Discovery in Databases Book Review:

The three volume proceedings LNAI 11906 – 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019. The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. The contributions were organized in topical sections named as follows: Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization. Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing. Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track.

Issues in Artificial Intelligence Robotics and Machine Learning 2013 Edition

Issues in Artificial Intelligence  Robotics and Machine Learning  2013 Edition
Author: Anonim
Publsiher: ScholarlyEditions
Total Pages: 1209
Release: 2013-05-01
ISBN 10: 1490105972
ISBN 13: 9781490105970
Language: EN, FR, DE, ES & NL

Issues in Artificial Intelligence Robotics and Machine Learning 2013 Edition Book Review:

Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Expert Systems. The editors have built Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Expert Systems in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Encyclopedia of Computer Science and Technology

Encyclopedia of Computer Science and Technology
Author: Allen Kent,James G. Williams
Publsiher: CRC Press
Total Pages: 500
Release: 2021-07-28
ISBN 10: 1000444279
ISBN 13: 9781000444278
Language: EN, FR, DE, ES & NL

Encyclopedia of Computer Science and Technology Book Review:

This 41st volume covers Application of Bayesan Belief Networks to Highway Construction to Virtual Reality Software and Technology.