Advances in data science

Advances in data science

by Demir, Ilke Lou, Yifei Wang, Xu Welker, Kathrin

Type Book
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This volume highlights recent advances in data science, including image processing and enhancement on large data, shape analysis and geometry processing in 2D/3D, exploration and understanding of neural networks, and extensions to atypical data types such as social and biological signals. The contributions are based on discussions from two workshops under Association for Women in Mathematics (AWM), namely the second Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop t…

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Title Statements

  • Full title :Advances in data science / Ilke Demir, Yifei Lou, Xu Wang, Kathrin Welker, editors.

Summary

This volume highlights recent advances in data science, including image processing and enhancement on large data, shape analysis and geometry processing in 2D/3D, exploration and understanding of neural networks, and extensions to atypical data types such as social and biological signals. The contributions are based on discussions from two workshops under Association for Women in Mathematics (AWM), namely the second Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place between July 29 and August 2, 2019 at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, and the third Women in Shape (WiSh) Research Collaboration Workshop that took place between July 16 and 20, 2018 at Trier University in Robert-Schuman-Haus, Trier, Germany. These submissions, seeded by working groups at the conference, form a valuable source for readers who are interested in ideas and methods developed in interdisciplinary research fields. The book features ideas, methods, and tools developed through a broad range of domains, ranging from theoretical analysis on graph neural networks to applications in health science. It also presents original results tackling real-world problems that often involve complex data analysis on large multi-modal data sources.

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Edition Statements

  • Publication Date:2021

Publication Statements

  • Publisher(s):Springer
  • Place of Publication:Cham, Switzerland
  • Language:eng
  • Publication Date:2021

The Physical Object

  • Description:xx, 364 pages : : illustrations (some color) ; : 25 cm.

Edition Identifiers

  • ISBN:9783030798901, 3030798909
  • oclc:on1253473361
  • LC Classification:QA76.9.M35
  • Dewey Decimal:004.01/51

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