References

Adewumi, A.A., T.O. Owolabi, I.O. Alade and S.O. Olatunji, 2016. Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach. Appl. Soft Comput., 42: 342-350.
Direct Link  |  

Basak, D., S. Pal and D.C. Partababis, 2007. Support vector regression. Neural Inf. Process. Lett. Rev., 11: 203-224.
Direct Link  |  

Bruck, E., 2005. Developments in magnetocaloric refrigeration. J. Phys. D. Appl. Phys., 38: R381-R391.

Ca, B. and M. Fe, 2014. A large magnetic entropy change near room temperature in La_0.8Ba_0.1Ca_0.1Mn_0.97Fe_0.03O₃ perovskite. J. Alloys Compd., 600: 172-177.
Direct Link  |  

Cai, C.Z., T.T. Xiao, J.L. Tang and S.J. Huang, 2013. Analysis of process parameters in the laser deposition of YBa₂Cu₃O₇ superconducting films by using SVR. Phys. C. Supercond., 493: 100-103.
Direct Link  |  

Cortes, C. and V. Vapnik, 1995. Support-vector networks. Mach. Learn., 20: 273-297.
CrossRef  |  Direct Link  |  

Ghodhbane, S., E. Tka, J. Dhahri and E.K. Hlil, 2014. A large magnetic entropy change near room temperature in La_0.8Ba_0.1Ca_0.1Mn_0.97Fe_0.03O₃ perovskite. J. Alloys Compd., 600: 172-177.
Direct Link  |  

Gschneidner Jr, K.A., V.K. Pecharsky and A.O. Tsokol, 2005. Recent developments in magnetocaloric materials. Rep. Prog. Phys., 68: 1479-1539.
CrossRef  |  

Hosseini, M., S.A.M. Naeini, A.A. Dehghani and Y. Khaledian, 2016. Estimation of soil mechanical resistance parameter by using particle swarm optimization, genetic algorithm and multiple regression methods. Soil Tillage Res., 157: 32-42.
Direct Link  |  

Khlifa, H.B., Y. Regaieg, W. Cheikhrouhou-Koubaa, M. Koubaa and A. Cheikhrouhou, 2015. Structural, magnetic and magnetocaloric properties of K-doped Pr_0.8Na_0.2-xK_xMnO₃ manganites. J. Alloys Compd., 650: 676-683.
Direct Link  |  

Liang, G., J. Xu and L. Liu, 2013. QSPR analysis for melting point of fatty acids using genetic algorithm based multiple linear regression (GA-MLR). Fluid Phase Equilib., 353: 15-21.
Direct Link  |  

Mahjoub, S., M. Baazaoui, R. M’nassri, H. Rahmouni and N.C. Boudjada et al., 2014. Effect of Iron substitution on the structural, magnetic and magnetocaloric properties of Pr_0.6Ca_0.1Sr_0.3Mn_1-xFexO₃ (0≤x≤0.075) manganites. J. Alloys Compd., 608: 191-196.
Direct Link  |  

Mleiki, A., S. Othmani, W. Cheikhrouhou-Koubaa, M. Koubaa and A. Cheikhrouhou et al., 2015. Effect of praseodymium doping on the structural, magnetic and magnetocaloric properties of Sm_0.55-xPr_xSr_0.45MnO₃ (0.1≤x≤0.4) manganites. J. Alloys Compd., 645: 559-565.
Direct Link  |  

Owolabi, T.O., K.O. Akande and S.O. Olatunji, 2016. Application of computational intelligence technique for estimating superconducting transition temperature of YBCO superconductors. Appl. Soft Comput., 43: 143-149.
Direct Link  |  

Owolabi, T.O., K.O. Akande and S.O. Olatunji, 2016. Computational intelligence approach for estimating superconducting transition temperature of disordered MgB2 superconductors using room temperature resistivity. Appl. Comput. Intell. Soft Comput., 2016: 1-7.
Direct Link  |  

Owolabi, T.O., K.O. Akande and S.O. Olatunji, 2016. Computational intelligence method of estimating solid-liquid interfacial energy of materials at their melting temperatures. J. Intell. Fuzzy Syst., 31: 519-527.
Direct Link  |  

Owolabi, T.O., K.O. Akande and S.O. Olatunji, 2017. Estimation of average surface energies of transition metal nitrides using computational intelligence technique. Soft Comput., 21: 6175-6182.
Direct Link  |  

Owolabi, T.O., K.O. Akande, S.O. Olatunji, A. Alqahtani and N. Aldhafferi, 2016. Estimation of curie temperature of manganite-based materials for magnetic refrigeration application using hybrid gravitational based support vector regression. AIP. Adv., 6: 105009-1-105009-12.
Direct Link  |  

Owolabi, T.O., K.O. Akande, S.O. Olatunji, A. Alqahtani and N. Aldhafferid, 2017. Modeling of curie temperature of manganite for magnetic refrigeration application using manual search and hybrid gravitational-based support vector regression. Soft Comput., 6: 1-10.
Direct Link  |  

Owolabi, T.O., M. Faiz, S.O. Olatunji and I.K. Popoola, 2016. Computational intelligence method of determining the energy band gap of doped ZnO semiconductor. Mater. Des., 101: 277-284.
Direct Link  |  

Owolabi, T.O., Y.F. Zakariya, S.O. Olatunji and K.O. Akande, 2016. Estimation of melting points of fatty acids using homogeneously hybridized support vector regression. Neural Comput. Appl., 1: 1-13.
Direct Link  |  

Phan, M.H. and S.C. Yu, 2007. Review of the magnetocaloric effect in manganite materials. J. Magn. Magn. Mater., 308: 325-340.
Direct Link  |  

Selmi, A., R. M’nassri, W. Cheikhrouhou-Koubaa, N.C. Boudjada and A. Cheikhrouhou, 2015. Effects of partial Mn-substitution on magnetic and magnetocaloric properties in Pr_0.7Ca_0.3Mn_0.95X_0.05O₃(Cr, Ni, Co and Fe) manganites. J. Alloys Compd., 619: 627-633.
Direct Link  |  

Selmi, A., R. M’nassri, W. Cheikhrouhou-Koubaa, N.C. Boudjada and A. Cheikhrouhou, 2015. Influence of transition metal doping (Fe, Co, Ni and Cr) on magnetic and magnetocaloric properties of Pr_0.7Ca_0.3MnO₃ manganites. Ceram. Intl., 41: 10177-10184.
Direct Link  |  

Selmi, A., R. M’nassri, W. Cheikhrouhou-Koubaa, N.C. Boudjada and A. Cheikhrouhou, 2015. The effect of Co doping on the magnetic and magnetocaloric properties of Pr_0.7Ca_0.3Mn_1-xCo_xO₃ manganites. Ceram. Intl., 41: 7723-7728.
Direct Link  |  

Thenmozhi, M. and G.S. Chand, 2016. Forecasting stock returns based on information transmission across global markets using support vector machines. Neural Comput. Appl., 27: 805-824.
Direct Link  |  

Vapnik, V.N., 2013. The Nature of Statistical Learning Theory. 2nd Edn., Springer, Berlin, Germany, ISBN:978-1-4757-3264-1, Pages: 313.

Wang, Z. and J. Jiang, 2013. Magnetic entropy change in perovskite manganites La_0.7A_0.3MnO₃La_0.7A_0.3Mn_0.9Cr_0.1O₃ (A= Sr, Ba, Pb) and Banerjee criteria on phase transition. Solid State Sci., 18: 36-41.
Direct Link  |  

Wen, J., H. Yang, X. Tong, K. Li and S. Wang et al., 2016. Optimization investigation on configuration parameters of serrated fin in plate-fin heat exchanger using genetic algorithm. Intl. J. Therm. Sci., 101: 116-125.
Direct Link  |