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ITU JOURNAL: ICT Discoveries, Vol. 1(2), December 2018
[10] D. Radosavljevik, and P. Vander Putten, [22] Q. Ruiyun, L. Zhu, and B. Jiang, (2013), “Fault-
“Large scale predictive modelling for micro- tolerance reconfigurable control for MIMO
simulation of 3G air interface load”, Systems using online fuzzy identification”,
Proceedings of the 20th ACM SIGKDD International Journal of Innovative Computing
International Conference on Knowledge Information and Control, vol. 9, no. 10,
Discovery and Data Mining KDD’14, pp. 3915-3928.
pp. 1620-1629, New York, USA, August 24-27,
2014. [18] A. B. Adeyemo, “Soft Computing for weather
and climate change studies”, African Journal
[11] N. S. Jaddi, S. Abdullah, and A. R. Hamdan, of Computing and ICT, vol.6, no. 2, pp. 77-90,
“Multi-population cooperative bat algorithm- 2013.
based optimization of artificial neural
network model”, Information Sciences, vol. [19] S. Firoozi, M. J. Sheikhdavoodi, and M. Sami,
294, pp. 628-644, 2014. “Evaluating the ability of different Artificial
Intelligence-based modelling techniques in
[12] V. Aggarwal, R. Jana, J. Pang, K. K. prediction of yield using energy inputs data of
Ramakrishnan, and N. K. Shankaranarayanan, Farms”, Journal of Life Science and
“Characterizing fairness for 3G wireless Biomedicine, vol. 4, no. 4, pp. 305-311, 2014.
networks”, 18 IEEE Workshop on Local &
th
Metropolitan Area Networks (LANMAN), [20] M. Heydari, and P. H. Talaee, “Prediction of
Chapel Hill, USA, 13-14 October, 2011. flow through rockfill dams using a Neuro-
Fuzzy Computing technique”, The Journal of
[13] M.-S. Kim, and S.-G. Kong, “Time series Mathematics and Computer Science, vol. 2,
prediction using the parallel-structure fuzzy no. 3, pp. 515-528, 2011.
system”, IEEE International Fuzzy Systems
Conference Proceedings, Seoul, South Korea, [21] S. Hemachandra, and R. V. S. Satyanarayana,
pp. 934-938, 22-25 August, 1999. “Co-active Neuro-Fuzzy Inference System for
prediction of electric load”, International
[14] A. Ghaffari, A. Khodayari, and F. Alimardani, H. Journal of Electrical and Electronics, vol. 3,
Sadati, “MANFIS-Based overtaking no. 2, pp. 217-222, 2013.
manoeuvre modeling and prediction of a
driver-vehicle-unit in real traffic flow”, 2012 [22] S. K. Bhuvaneswari, P. Geetha, and K. J. Devi,
IEEE International Conference on Vehicular “Semantic classification and region growing
Electronics and Safety, Istanbul, Turkey, of MRI using CANFIS model for Tumor
pp. 387-392, July 24-27, 2012. identification”, Australian Journal of Basic and
Applied Sciences, vol. 8, no. 3, pp. 43-52, 2014.
[15] K. Aziz, A. Rahman, A. Y. Shamseldin, and M.
Shaoib, “Co-active Neuro-Fuzzy System for [23] L. Parthiban, and R. Subramanian, “Intelligent
regional flood estimation in Australia, Journal heart disease prediction system using CANFIS
of Hydrology and Environment Research, and Genetic algorithm”, International Journal
vol. 1, no. 1, pp. 11-20, 2013. of Biological and Life Science, vol. 3, no. 3,
pp. 157-160, 2007.
[16] T. O. Hanafy, A. S. Al-Osaimy, M. M. Al-Harthi,
and A. A. Aly, “Identification of uncertain [24] National Communications Authority, Telecom
nonlinear MIMO spacecraft systems using Statistics, September 2017.
Coactive NeuroFuzzy Inference System https://www.nca.org.gh/industry-data-
(CANFIS)”, International Journal of Control, 2/market-share-statistics-2/voice-2/
st
Automation and Systems, vol. 3, no. 2, [Accessed: 21 June, 2018]
pp. 25-37, 2014.
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