Technology analysis of artificial intelligence using Bayesian inference for neural networks

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    At present, artificial intelligence (AI) technology is receiving much attention and applied in each field of society. AI is one of the key technologies to lead the fourth industrial revolution along with the internet of things and big data. Therefore, many companies and research institutes are trying to systematically analyze AI technology in order to understand the AI itself correctly. In this paper, we also study on a method to analyze AI technology based on quantitative approach. We correct the patent documents related to AI technology, and analyze them using statistical modelling. We use Bayesian inference for neural networks to build our proposed method. To verify the validity of our research, we carry out a case study using the AI patent documents.


  • Keywords


    Artificial intelligence; Technology analysis; Bayesian inference; Neural networks; Patent.

  • References


      [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Third Edition, Essex, UK, Pearson, 2014.

      [2] J. Hossen, S. Sayeed, T. Bhuvaneswari, C. Venkataseshaiah, J. Emerson and C. R. Fatt, “An Automated Fuzzy Logic Based Low Cost Floor Cleaning Mobile Robot”, Journal of Engineering and Applied Sciences, 12(1), 119-126, 2017.

      [3] N. Sao, “A Review Paper on Mobile Technology for Activity Recognition”, Journal of Engineering and Applied Sciences,12(23), 6118-6122, 2017.

      [4] P. Dwivedi, S. Sankaranarayanan and V. Choudhary, “IoT Based Smart Garbage Management System”, International Journal of Advanced Trends in Computer Science and Engineering, 6(4), 71-76, 2017.

      [5] M. Gooroochurn, D. Kerr and K. Bouazza-Marouf, “A Polynomial Neural Network Classifier based on Gabor Features for the Extraction of Ear Tragus and Eye Corners”, International Journal of Science and Applied Information Technology, 5(4), 14-27, 2016

      [6] J. R. Flynn, What is intelligence?: Beyond the Flynn effect. Cambridge University Press, 2007.

      [7] L. S. Gottfredson, “Mainstream science on intelligence: An editorial with 52 signatories, history, and bibliography”, Intelligence, 24(1), 13-23, 1997.

      [8] M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects”, Science, 349(6245), 255-260, 2015.

      [9] K. P. Murphy, Machine Learning: A Probabilistic Perspective, Cambridge MA, MIT Press, 2012.

      [10] K. Doya, S. Ishii, A. Pouget and R. P. N. Rao, Bayesian Brain, Probabilistic Approaches to Neural Coding, Cambridge, MA, MIT Press, 2011.

      [11] M. Akritas, Probability and Statistics with R for Engineers and Scientists, Boston, Pearson, 2016.

      [12] R. M. Neal, Bayesian learning for neural networks (Vol. 118), Springer Science & Business Media, 1996.

      [13] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning Representations by back-propagation errors”, Nature, 323, 533-536, 1986.

      [14] D. J. C. Mackay, “A Practical Bayesian Framework for backpropagation networks”, Neural Computation, 4, 448-472, 1992.

      [15] S. Chatterjee and A. S. Hadi, Regression Analysis by Example, 5th edition, Hoboken, NJ, Wiley, 2012.

      [16] USPTO, The United States Patent and Trademark Office, http://www.uspto.gov, 2017.

      [17] WIPSON, WIPS Corporation, http://www.wipson.com, http://global.wipscorp.com, 2017.

      [18] WIPO IPC, International Patent Classification(IPC), World Intellectual Property Organization, http://www.wipo.org, 2017.

      [19] H. M. Koduvely, Learning Bayesian Models with R, Birmingham, UK, Packt, 2015.

      [20] R Development Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org, 2017.

      [21] I. Feinerer and K. Hornik, Package ‘tm’ Ver. 0.7-3, Text Mining Package, CRAN of R project, 2017.

      [22] P. P. Rodriguez and D. Gianola, Package ‘brnn’, Version 0.6, Bayesian Regularization for Feed-Forward Neural Networks, CRAN of R project, 2017.

      [23] B. L. Bowerman, R. T. O’Connell and A. B. Koehler, Forecasting, Time Series, and Regression, Independence, KY, Brooks/Cole, 2005.


 

View

Download

Article ID: 9965
 
DOI: 10.14419/ijet.v7i2.3.9965




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.