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Titlebook: Intelligent Astrophysics; Ivan Zelinka,Massimo Brescia,Dalya Baron Book 2021 The Editor(s) (if applicable) and The Author(s), under exclus

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发表于 2025-3-21 19:46:04 | 显示全部楼层 |阅读模式
书目名称Intelligent Astrophysics
编辑Ivan Zelinka,Massimo Brescia,Dalya Baron
视频video
概述Presents new developments, advancements, and selected topics in the fields of artificial intelligence and related algorithms in the astrophysical data processing.Discusses new ideas and interdisciplin
丛书名称Emergence, Complexity and Computation
图书封面Titlebook: Intelligent Astrophysics;  Ivan Zelinka,Massimo Brescia,Dalya Baron Book 2021 The Editor(s) (if applicable) and The Author(s), under exclus
描述.This present book discusses the application of the methods to astrophysical data from different perspectives. In this book, the reader will encounter interesting chapters that discuss data processing and pulsars, the complexity and information content of our universe, the use of tessellation in astronomy, characterization and classification of astronomical phenomena, identification of extragalactic objects, classification of pulsars and many other interesting chapters. The authors of these chapters are experts in their field and have been carefully selected to create this book so that the authors present to the community a representative publication that shows a unique fusion of artificial intelligence and astrophysics. .
出版日期Book 2021
关键词Artificial Intelligence; Computational Intelligence; Big Data; Intelligent Astrophysics; Astrophysics
版次1
doihttps://doi.org/10.1007/978-3-030-65867-0
isbn_softcover978-3-030-65869-4
isbn_ebook978-3-030-65867-0Series ISSN 2194-7287 Series E-ISSN 2194-7295
issn_series 2194-7287
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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发表于 2025-3-21 20:31:20 | 显示全部楼层
2194-7287 sical data processing.Discusses new ideas and interdisciplin.This present book discusses the application of the methods to astrophysical data from different perspectives. In this book, the reader will encounter interesting chapters that discuss data processing and pulsars, the complexity and informa
发表于 2025-3-22 03:50:22 | 显示全部楼层
Comparison of Outlier Detection Methods on Astronomical Image Data,plied to data extracted from SDSS stripe 82. After discussing the sensitivity of each method to its own set of hyperparameters, we combine the results from each method to rank the objects and produce a final list of outliers.
发表于 2025-3-22 04:43:34 | 显示全部楼层
Book 2021er interesting chapters. The authors of these chapters are experts in their field and have been carefully selected to create this book so that the authors present to the community a representative publication that shows a unique fusion of artificial intelligence and astrophysics. .
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Ensemble Classifiers for Pulsar Detection,pically improved classification performance, while class imbalance can be addressed through careful sampling or through cost-sensitive classification. Our results demonstrate that such dedicated ensembles yield better results compared to methods that do not consider class balance.
发表于 2025-3-22 22:43:13 | 显示全部楼层
发表于 2025-3-23 05:07:30 | 显示全部楼层
The Voronoi Tessellation Method in Astronomy,he moving-mesh cosmology simulation. We briefly describe these results paying more attention to the practical application of the Voronoi tessellation related to the spatial large-scale galaxy distribution.
发表于 2025-3-23 09:17:36 | 显示全部楼层
Statistical Characterization and Classification of Astronomical Transients with Machine Learning in is based on a test campaign performed on simulated data. The classification was carried out by comparing the performances among several Machine Learning algorithms on statistical parameters extracted from the light curves. The results make in evidence some critical aspects related to the data quali
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