Page 287 - Big Data Analytics for Intelligent Healthcare Management
P. 287
REFERENCES 281
[22] R. Storn, K. Price, Differential evolution–a simple and efficient heuristic for global optimization over con-
tinuous spaces, J. Glob. Optim. 11 (4) (1997) 341–359.
[23] G.-W. Yan, Z.-J. Hao, A novel optimization algorithm based on atmosphere clouds model, Int. J. Comput.
Intell. Appl. 12 (1) (2013) 1350002-1–1350002-16.
[24] N.S. Jaddi, J. Alvankarian, S. Abdullah, Kidney-inspired algorithm for optimization problems, Commun.
Nonlinear Sci. Numer. Simul. 42 (2017) 358–369.
[25] M.K. Taqi, R. Ali, obka-fs: an oppositional-based binary kidneyinspired search algorithm for feature selec-
tion, J. Theor. Appl. Inf. Technol. 95 (1) (2017) 9–23.
[26] M. Ehteram, et al., Reservoir operation by a new evolutionary algorithm: kidney algorithm, Water Resour.
Manag. 32 (14) (2018) 4681–4706.
[27] Z. Michalewicz, Genetic algorithms, numerical optimization, and constraints, in: Proceedings of the Sixth
International Conference on Genetic Algorithms, vol. 195, Morgan Kaufmann, San Mateo, CA, 1995.
[28] M. Ehteram, et al., Optimizing dam and reservoirs operation based model utilizing shark algorithm approach,
Knowl.-Based Syst. 122 (2017) 26–38.
[29] X.S. Yang, A new metaheuristic bat-inspired algorithm, in: Nature Inspired Cooperative Strategies for Op-
timization (NICSO 2010), Springer, 2010, pp. 65–74.
[30] J. Kennedy, Particle swarm optimization, in: Encyclopedia of Machine Learning, Springer, Boston, MA,
2011, pp. 760–766.
[31] N.S. Jaddi, S. Abdullah, Optimization of neural network using kidney-inspired algorithm with control of fil-
tration rate and chaotic map for real-world rainfall forecasting, Eng. Appl. Artif. Intell. 67 (2018) 246–259.
[32] S. Ekinci, A. Demiroren, B. Hekimoglu, Parameter optimization of power system stabilizers via kidney-
inspired algorithm. Trans. Inst. Meas. Control. (2018)https://doi.org/10.1177/0142331218780947.
[33] A.A.B. Homaid, et al., A kidney algorithm for pairwise test suite generation, Adv. Sci. Lett. 24 (10) (2018)
7284–7289.
[34] M.H. Asyali, D. Colak, O. Demirkaya, M.S. Inan, Gene expression profile classification: a review, Curr.
Bioinforma. 1 (1) (2006) 55–73.
[35] P.L. Lin, P.W. Huang, C.H. Kuo, Y.H. Lai, A size-insensitive integrity-based fuzzy c-means method for data
clustering, Pattern Recogn. 47 (5) (2014) 2042–2056.
[36] J. Nayak, B. Naik, H.S. Behera, Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to
2014, in: Computational Intelligence in Data Mining, vol. 2, Springer, 2015, pp. 133–149.
[37] K. Dhiraj, S.K. Rath, K.S. Babu, FCM for gene expression bioinformatics data, in: International Conference
on Contemporary Computing, Springer, Berlin, Heidelberg, 2009, pp. 521–532.
[38] S. Hettich, C. Blake, C. Merz, UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/
mlearn/MLRepository.html, 1998.
[39] L. Jinyan, L. Huiqing, Kentridge Bio-Medical Data Set Repository, http://datam.i2r.a-star.edu.sg/datasets/
krbd/, 2002.
[40] U. Kanimozhi, D. Manjula, An intelligent incremental filtering feature selection and clustering algorithm for
effective classification, Intell. Automat. Soft Comput. (2017) 1–9.
[41] M. Friedman, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, J.
Am. Stat. Assoc. 32 (1937) 675–701.
[42] R.L. Iman, J.M. Davenport, Approximations of the critical region of the Friedman statistic, Commun. Stat.
Theory Methods 9 (1980) 571–595.
[43] J. Nayak, B. Naik, H.S. Behera, A novel chemical reaction optimization based higher order neural network
(CRO-HONN) for nonlinear classification, Ain Shams Eng. J. 6 (3) (2015) 1069–1091.