Page 252 - Artificial Intelligence in the Age of Neural Networks and Brain Computing

Further Reading 243

[45] D. Labate, F. La Foresta, I. Palamara, G. Morabito, Bramanti, Z. Zhang, F.C. Morabito,
EEG complexity modifications and altered compressibility in mild cognitive impair-
ment and Alzheimer’s disease, in: Proceedings of the 23rd Italian Workshop on Neural
Networks (WIRN 2013), 2013.
[46] J. Dauwels, S. Kannan, Diagnosis of Alzheimer’s Disease using Electric Signals of the
Brain, A Grand Challenge, Asia-Pacific Biotech News 16 (10e11) (2012) 22e38.
[47] K. Dauwels, M.R. Srinivasan, T.V. Reddy, F.B. Musha, C. Latchoumane, C. Jeong,
Andrze, Slowing and loss of complexity in Alzheimer’s EEG: Two sides of the same
coin, Int. J. Alzheimer’s Disease 2011 (2011) 1e3.

FURTHER READING
[1] J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical Evaluation of Gated Recurrent
Neural Networks on Sequence Modeling, arXiv preprint arXiv:14123555, 2014.
[2] X. Glorot, A. Bordes, Y. Bengio, Deep sparse rectifier neural networks, in: Proc. Of the
14th Int.Conf. on Art. Int. and Statistics, vol. 15, 2011, pp. 315e323.
[3] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation 9 (8)
(1997) 1735e1780.

[4] T. Mikolov, M. Karafia ´t, L. Burget, J. Cernocky ´, S. Khudanpur, Recurrent neural network
based language model, Interspeech 2 (2010) 3.
[5] Y. Zheng, Q. Liu, E. Chen, Y. Ge, J.L. Zhao, Exploiting multi-channels deep convolu-
tional neural networks for multivariate time series classification, Frontiers of Computer
Science 10 (1) (2016) 96e112.
[6] D. Ravi, C. Wong, B. Lo, G.-Z. Yang, A deep learning approach to on-node sensor data
analytics for mobile or wearable devices, IEEE Journal of Biomedical and Health
Informatics 21 (1) (2017) 56e64.