An Improvement on Deep Time Growing Neural Network on Biological Signals: Review

Manpreet Kaur, Jashanpreet Kaur


A novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It was employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one versus-multiple class application. In this paper different authors research papers are reviewed and different problems are stored and now these problems are resolved.

Full Text:



Arash Gharehbaghi “A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network” IEEE Transactions On Neural Networks And Learning Systems -2017.

Christine F. Martindale “Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models” , Sensors 2017.

Timothy J. O’Shea “Convolutional Radio Modulation Recognition Networks” arXiv:1602.04105v3 [cs.LG] 10 Jun 2016.

Alan Jovic “Classification of Biological Signals Based on Nonlinear Features” University of Zagreb, Unska 3, 10000 Zagreb, Croatia-2014.

Jurgen Schmidhuber “Deep Learning in Neural Networks: An Overview” Galleria 2, 6928 Manno-Lugano Switzerland 2 July 2014.

A. Graves, A. Mohamed, and G. E. Hinton, “Speech recognition with deep recurrent neural networks,” CoRR, vol. abs/1303.5778, 2013. [Online]. Available:

T. O’Shea, “Gnu radio channel simulation,” in GNU Radio Conference 2013, 2013.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR, vol. abs/1412.6980, 2014. [Online]. Available: abs/1412.6980.

F. Chollet, Keras,, 2015.

M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, “Spatial transformer networks,” CoRR, vol. abs/1506.02025, 2015. [Online]. Available:

T. N. Sainath et al., “Learning the speech front-end with raw waveform cldnns,” in Proc. Interspeech, 2015.

M. Abadi, A. Agarwal, et al., “Tensorflow: large-scale machine learning on heterogeneous systems, 2015,” Software available from tensorflow. org,



  • There are currently no refbacks.

Comments on this article

View all comments

© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.