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Artificial Neural Networks and Data Science
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Author(s): Trevor Bihl (Air Force Research Laboratory, USA), William A. Young II (Ohio University, USA), Adam Moyer (Ohio University, USA)and Steven Frimel (Air Force Research Laboratory, USA)
Copyright: 2023
Pages: 23
Source title:
Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch052
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Abstract
Artificial neural networks (ANNs) consist of a family of techniques that are commonly employed to recognize and interpret patterns in big data that are used in prediction, clustering, classification, and identification of other previously unknown data patterns. This article describes foundational concepts that relate to ANNs, including an understanding of how ANNs are linked to biological concepts and the underlying ANN families. The article includes an explanation of common ANN methods, architecture/hyperparameter determination for initializing ANNs, and current research directions. The article concludes with a discussion on the need for algorithmic transparency and repeatability of research.
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