Classification Based on Deep Neural Cellular Automata Model

Authors: Yasser F. Hassan

Abstract:

Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: Cellular automata, neural cellular automata, deep learning, classification.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3346722

References:

[1] Sartra Wongthanavasu, Jetsada Ponkaew, A cellular automata-based learning method for classification, Expert Systems with Applications, 49, (2016), 99-111
[2] Xiaodong, S., Ganlin, Z., Feng, L., Decheng, L., Yuguo, Z., and Jinling, Y., Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model, J Arid Land 8-5(2016) 734-748
[3] Ban, J., Chang C., When are two multi-layer neural cellular networks the same?, Neural networks, 79 (2016) 12-19
[4] Yasser F. Hassan, Rough Set Classification Based on Quantum Logic, Journal of Experimental & theatrical artificial intelligence, 2017, DOI: 10.1080/0952813X.2017.1354080
[5] Yuhong Ruan, Anwei Li, a new small-world network created by Cellular Automata, Physica A: Statistical Mechanics and its Applications, 456, (2016), 106-111
[6] Yasser Hassan, Daisuke Yamaguchi and Eiichiro Tazaki, New Model Based on Cellular Automata and Multiagent Techniques, Cybernetics and Systems, 38, (2007), 47-82
[7] Hai Benzhai, Liu Lei, Qin Ge, Peng Yuwan, Li Ping, Yang Qingxiang, Wang Hailei, Simulation of wastewater treatment by aerobic granules in a sequencing batch reactor based on cellular automata, Bioprocess and Biosystems Engineering, October 2014, Vol. 37, Issue 10, pp 2049–2059
[8] Yang Wang, Yan-Yan Chen, Modeling the effect of microscopic driving behaviors on Kerner’s time-delayed traffic breakdown at traffic signal using cellular automata, Physica A: Statistical Mechanics and its Applications, Volume 463, 1 December 2016, Pages 12–24
[9] Zhu, S., Shi, Z., Sun, C., and Shen, S., Deep neural network based image annotation, pattern recognition letters 65 (2015) 103-108
[10] Zilu Liang, Yasushi Wakahara, Real-time urban traffic amount prediction models for dynamic route guidance systems, EURASIP Journal on Wireless Communications and Networking, 85, (2014), 1-15
[11] Yasser F. Hassan, Deep Learning Architecture using Rough Sets and Rough Neural Networks, International Journal of System and Cybernetics "Kybernetes", Vol. 46, No. 4, 2017
[12] Gwo Horng, Using Cellular Automata for Parking Recommendations in Smart Environments, PLOS one, 14, (2014), 1-5
[13] Jia Lee, Ferdinand Peper, Kenji Leibnitz, Ping Gu, Characterization of random fluctuation-based computation in cellular automata, Information Sciences, 352, (2016), 150-166
[14] Ahmed Moustafa, Ahmed Younes, Yasser F. Hassan, A Customizable Quantum-Dot Cellular Automata Building Block for the Synthesis of Classical and Reversible Circuits, The Scientific World Journal, vol. 2015, 9 pages, 2015. doi:10.1155/2015/705056
[15] Moein Shakeri, Arash Deldari, Hossein Deldari, Ghamarnaz Tadayon, Three Leveled Fuzzy System for Traffic Light and Urban Traffic Control Based on Cellular Automata, Technological Developments in Education and Automation pp 477-482
[16] Yu Wang, Jianmin Xu, Peiqun Lin, A Two-Lane Cellular Automata Traffic Model Under Three-Phase Traffic Theory, International Symposium on Intelligence Computation and Applications, Computational Intelligence and Intelligent Systems pp 683-688
[17] Marcelo Zamith, Regina Célia P. Leal-Toledo, Esteban Clua , Elson M. Toledo, Guilherme V.P. de Magalhães, A new stochastic cellular automata model for traffic flow simulation with drivers’ behavior prediction, Journal of Computational Science, Volume 9, July 2015, Pages 51–56
[18] Jamrozik, W., neural cellular networks for welding arc thermograms segmentation, infrared physics & technology 66 (2014) 18-28
[19] Xu, J., Luo, X., Wang, G., Gilmore, H., and Madabhushi, A., A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images, neurocomputing 191 (2016) 214-223.