National Open University Library

Artificial intelligence oceanography / (Record no. 13895)

MARC details
000 -LEADER
fixed length control field 02689cam a2200241 i 4500
INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9789211963773
INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9789211963773
DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 494.5
TITLE STATEMENT
Title Artificial intelligence oceanography /
Statement of responsibility, etc Xiaofeng Li, Fan Wang, editors
Copyright Date
Place of publication Singapore :
Name of publisher Springer,
Year of publication or production [2023]
Copyright Date
Year of publication or production ©2023
PHYSICAL DESCRIPTION
Number of Pages 1 online resource (xii, 346 pages) :
Other physical details illustrations (chiefly color)
FORMATTED CONTENTS NOTE
Formatted contents note Theory and technology of artificial intelligence for oceanography -- Satellite data-driven internal wave forecast model based on machine learning techniques -- Detection and analysis of marine macroalgae based on artificial intelligence -- Tropical cyclone intensity estimation from geostationary satellite imagery -- Reconstructing marine environmental data based on deep learning -- Detecting oceanic processes from space-borne sar imagery using machine learning -- Deep convolutional neural networks-based coastal inundation mapping for un-defined least developed countries: taking madagascar and mozambique as examples -- Ai- based mesoscale eddy study -- Classifying sea ice types from sar images based on deep fully convolutional networks -- Detecting ships and extracting ship's size from SAR images based on deep learning -- Quality control of ocean temperature and salinity data based on machine learning technology -- automatic extraction of internal wave signature from multiple satellite sensors based on deep convolutional neural networks -- Automatic extraction of waterlines from large-scale tidal flats on SAR images and applications based on deep convolutional neural networks -- Forecast of tropical instability waves using deep learning -- Sea surface height prediction based on artificial intelligence
SUMMARY, ETC.
Summary, etc This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.
SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Oceanography
SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Artificial intelligence.
ADDED ENTRY--PERSONAL NAME
Personal name Li, Xiaofeng,
ADDED ENTRY--PERSONAL NAME
Personal name Wang, Fan,
ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://rave.ohiolink.edu/ebooks/ebc2/9789811963759
ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://link.springer.com/10.1007/978-981-19-6375-9
ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://go.ohiolink.edu/goto?url=https://link.springer.com/10.1007/978-981-19-6375-9
ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Books
Holdings
Permanent Location Current Location Date acquired Full call number Accession Number Koha item type
Gabriel Afolabi Ojo Central Library (Headquarters). Gabriel Afolabi Ojo Central Library (Headquarters). 11/05/2024 S494.5 .A78 2023 0195202 Books
Gabriel Afolabi Ojo Central Library (Headquarters). Gabriel Afolabi Ojo Central Library (Headquarters). 11/05/2024 S494.5 .A78 2023 0195204 Books
Gabriel Afolabi Ojo Central Library (Headquarters). Gabriel Afolabi Ojo Central Library (Headquarters). 11/05/2024 S494.5 .A78 2023 0195203 Books

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