|
U. Aich, S.Banerjee, “Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization”, Applied Mathematical Modelling, vol. 38, no(11-12), pp.2800-2818, 2014.
|
|
A. W. Blocker, F. V. Bonassi, S. L. Scott, et al., “Bayes and big data: The consensus Monte Carlo algorithm”, International Journal of Management Science and Engineering Management, vol.11, no 2, pp. 78-88, 2016.
|
|
Y. Chaudhary, J. Joshi, S. Porwal, et al., “Data compression methodologies for lossless data and comparison between algorithms”, International Journal of Engineering Science and Innovative Technology (IJESIT), vol. 2, no 2, pp. 142-147, 2013.
|
|
S. Decherchi, P. Gastaldo, A. Leoncini, et al., “Efficient digital implementation of extreme learning machines for classification”, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 59, no 8, pp. 496-500, 2013.
|
|
W. Ding, H. Lo, D. Wang, et al., “Crime hotspot mapping using the crime related factors—a spatial data mining approach”, Applied intelligence, vol. 39, no. 4, pp.772-781, 2013.
|
|
B. L. Evans, J. Lin, M. Nassar, “Impulsive noise mitigation in powerline communications using sparse Bayesian learning”, IEEE Journal on Selected Areas in Communications, vol. 31, no. 7, pp.1172-1183, 2013.
|
|
M. Fazel, A. Jalali, S. Oymak, et al., “Simultaneously structured models with application to sparse and low-rank matrices”, IEEE Transactions on Information Theory, vol.61, no.5, pp. 2886-2908, 2015.
|
|
L. Guo, C. Ruan, M. Wang, et al., “A cloud simulation based environment for multi-disciplinary collaborative simulation and optimization”, in International Conference on Computer Supported Cooperative Work in Design, pp 445-450, 2017
|
|
H. Gupta, S. K. Ghosh, A. Vahid Dastjerdi, et al., “iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments”, Software: Practice and Experience, vol. 47, no.9, pp1275-1296, 2017.
|
|
J. Huang, J. W. Park, S. Shen, et al., “MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data”, Nucleic Acids Research, vol.40, no 8, pp. e61-e61, 2012.
|
|
F. Kang, J. Li, “Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes”, Journal of Computing in Civil Engineering, vol. 30, no. 3, 04015040, 2015
|
|
D. Liu, D. Y. Peng, Pan, X. Peng & J. Zhou, “Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning”, Measurement, vol. 63, pp143-151, 2015
|
|
P. Wendell, R. S. Xin, M. Zaharia, et al. Apache spark: a unified engine for big data processing”, Communications of the ACM, vol. 59, no.11, pp56-65, 2016
|
|
X. Wu, G. Q. Wu, X. Zhu, & W. Ding, “Data mining with big data”, IEEE transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 97-107,2014.
|