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Artificial Neural Network What-If Theory

Artificial Neural Network What-If Theory
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Author(s): Paolo Massimo Buscema (Semeion Research Institute, Rome, Italy & University of Colorado, USA) and William J. Tastle (Ithaca College, USA)
Copyright: 2020
Pages: 29
Source title: Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch001

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Abstract

Data sets collected independently using the same variables can be compared using a new artificial neural network called Artificial neural network What If Theory, AWIT. Given a data set that is deemed the standard reference for some object, i.e. a flower, industry, disease, or galaxy, other data sets can be compared against it to identify its proximity to the standard. Thus, data that might not lend itself well to traditional methods of analysis could identify new perspectives or views of the data and thus, potentially new perceptions of novel and innovative solutions. This method comes out of the field of artificial intelligence, particularly artificial neural networks, and utilizes both machine learning and pattern recognition to display an innovative analysis.

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