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Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide, , , , , , and . Environmental Earth Sciences, 75 (3): 1-11 (2016)cited By 0.A multi-objective evolutionary algorithm for energy management of agricultural systems - A case study in Iran, , , , , , and . Renewable and Sustainable Energy Reviews, (2015)cited By 13.Influence of clay particles on Al<inf>2</inf>O<inf>3</inf> and TiO<inf>2</inf> nanoparticles transport and retention through limestone porous media: measurements and mechanisms, , , , and . Journal of Nanoparticle Research, (2015)cited By 8.Potential of support vector regression for optimization of lens system, , , , , , and . CAD Computer Aided Design, (2015)cited By 3.Potential of adaptive neuro-fuzzy inference system for evaluation of drought indices, , , , and . Stochastic Environmental Research and Risk Assessment, 29 (8): 1993-2002 (2015)cited By 9.Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point, , , , , and . Soil and Tillage Research, (2017)cited By 4.Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature, , , , and . Computers and Electronics in Agriculture, (2016)cited By 3.An automated system for skelet al maturity assessment by extreme learning machines, , , , and . PLoS ONE, (2015)cited By 8.Transport and retention of engineered Al<inf>2</inf> O<inf>3</inf>, TiO<inf>2</inf>, and SiO<inf>2</inf> nanoparticles through various sedimentary rocks, , , and . Scientific Reports, (2015)cited By 10.Load balancing in grid computing: Taxonomy, trends and opportunities, , , , and . Journal of Network and Computer Applications, (2017)cited By 0.