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Artificial Neural Network What-If Theory

Author

Listed:
  • Paolo Massimo Buscema

    (Semeion Research Institute, Rome, Italy & Department of Mathematical and Statistical Sciences, CCMB, University of Colorado, Denver, CO, USA)

  • William J. Tastle

    (Ithaca College, Ithaca, NY, USA)

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.

Suggested Citation

  • Paolo Massimo Buscema & William J. Tastle, 2015. "Artificial Neural Network What-If Theory," International Journal of Information Systems and Social Change (IJISSC), IGI Global, vol. 6(4), pages 52-81, October.
    • Handle: RePEc:igg:jissc0:v:6:y:2015:i:4:p:52-81
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    Cited by:

    1. Buscema, Massimo & Sacco, Pier Luigi, 2016. "MST Fitness Index and implicit data narratives: A comparative test on alternative unsupervised algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 726-746.

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