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Liver Disease Diagnosis Using Quantum-based Binary Neural Network Learning Algorithm

  • Om Prakash Patel
  • Aruna Tiwari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 336)

Abstract

In this paper, a liver disease diagnosis is carried out using quantum-based binary neural network learning algorithm (QBNN-L). The proposed method constructively form the neural network architecture, and weights are decided by quantum computing concept. The use of quantum computing improves performance in terms of number of neurons at hidden layer and classification accuracy and precision. Same is compared with various classification algorithms such as logistic, linear logistic regression, multilayer perceptron, support vector machine (SVM). Results are showing improvement in terms of generalization accuracy and precision.

Keywords

Hide Layer Output Layer Hide Neuron Machine Learning Algorithm Quantum Gate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Simon, H.: Neural Networks and Learning Machines. Prentice Hall, Englewood Cliffs (2008)Google Scholar
  2. 2.
    Lu, T.C., Yu, G.R., Juang, J.C.: Quantum-based algorithm for optimizing artificial neural networks. Neural Netw. Learn. Syst. IEEE Trans. 24, 1266–1278 (2013)CrossRefGoogle Scholar
  3. 3.
    Gray, D.L., Michel, A.N.: A training algorithm for binary feedforward neural networks. Neural Netw. IEEE Trans. 3, 176–194 (1992)CrossRefGoogle Scholar
  4. 4.
    Kim, J.H., Park, S.K.: The geometrical learning of binary neural networks. Neural Netw. IEEE Trans. 6, 237–247 (1995)CrossRefGoogle Scholar
  5. 5.
    Xu, Y., Chaudhari, N.: Application of binary neural networks for classification. In: 2003 International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1343–1348. IEEE (2003)Google Scholar
  6. 6.
    Bahramirad, S., Mustapha, A., Eshraghi, M.: Classification of liver disease diagnosis: A comparative study. In: Informatics and Applications (ICIA), 2013 Second International Conference, vol. 1, pp. 42–46. IEEE (2013)Google Scholar
  7. 7.
    Han, K.H., Kim, J.H.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. Evol. Comput. IEEE Trans. 6, 580–593 (2002)CrossRefGoogle Scholar
  8. 8.
    Blake, C., Merz, C.J.: {UCI} Repository of machine learning databases. Department of Information Computer Science, University of California, Irvine [online]. Available: http://www.ics.uci.edu/mlearn/MLRepository.html (1998)

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology IndoreIndoreIndia

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