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Periodical
Inteligencia artificial : revista iberoamericana de inteligencia artificial.
Author:
ISSN: 11373601 19883064 Year: 1997 Publisher: [Valencia, Spain?] : Asociación Española para la Inteligencia Artificial (AEPIA),


Periodical
Advances in artificial intelligence.
ISSN: 16877489 16877470 Year: 2008 Publisher: New York, NY : Hindawi Publ.


Periodical

Periodical
CAAI transactions on intelligence technology.
Authors: ---
ISSN: 24682322 Year: 2016 Publisher: [London, UK] : [Stevenage, UK] : [Hoboken N.J.] : Elsevier B.V. : Chongqing University of Technology The Institution of Engineering and Technology John Wiley & Sons [on behalf of the Institution of Engineering and Technology and Chongqing University of Technology]


Periodical
人工知能学会論文誌 [[ジンコウチノウガッカイロンブンシ]]
Author:
ISSN: 13468030 Year: 2001 Publisher: [Tōkyō] : Jinkō Chinō Gakkai,


Periodical
Energy and AI
Author:
ISSN: 26665468 Year: 2020 Publisher: [London] Elsevier Ltd.

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Abstract

The journal focuses on innovative applications of AI that address the critical challenges in energy systems, energy materials, energy chemistry, energy utilization & conversion, and energy & society, as well as other important pressing issues in energy research. The journal also considers papers on the development of bespoke AI technologies and methodologies for advancing energy, decarbonization and sustainable development, such as data-driven approaches, optimization algorithms and AI ethics


Book
Artificial Neural Networks : Methods and Applications
Author:
ISBN: 1603271015 1588297187 Year: 2009 Publisher: Totowa, NJ : Humana Press : Imprint: Humana,

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Abstract

As an extension of artificial intelligence research, artificial neural networks (ANN) aim to simulate intelligent behavior by mimicking the way that biological neural networks function. In Artificial Neural Networks, an international panel of experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. In the tradition of the highly successful Methods in Molecular Biology™ series, this volume exhibits clear, easy-to-use information with many step-by-step laboratory protocols. Comprehensive and state-of-the-art, Artificial Neural Networks is an excellent guide to this accelerating technological field of study.

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