1. Zameer, A., M. Majeed, S.M. Mirza, M.A.Z. Raja, A. Khan and N.M. Mirza, 2019. Bio-inspired heuristics for layer thickness optimization in multilayer piezoelectric transducer for broadband structures. Soft Comput., 23: 3449-3463.
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2. Raja, M.A.Z., J. Mehmood, Z. Sabir, A.K. Nasab and M.A. Manzar, 2019. Numerical solution of doubly singular nonlinear systems using neural networks-based integrated intelligent computing. Neural computing and applications, 31: 793-812.
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3. Munir, A., M.A. Manzar, N.A. Khan and M.A.Z. Raja, 2019. Intelligent computing approach to analyze the dynamics of wire coating with Oldroyd 8-constant fluid. Neural Comput. Appl., 31: 751-775.
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4. Lodhi, S., M.A. Manzar, M.A.Z. Raja, 2019. Fractional neural network models for nonlinear Riccati systems. Neural Comput. Appl., 31: 359-378.
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5. Chaudhary, N.I., S. Zubair, M.A.Z. Raja and N. Dedovic, 2019. Normalized fractional adaptive methods for nonlinear control autoregressive systems. Applied Math. Modell., 66: 457-471.
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6. Ahmad, I., A. Rehman, F. Ahmad and M.A.Z. Raja, 2019. Heuristic computational intelligence approach to solve nonlinear multiple singularity problem of sixth Painlev́e equation. Neural Comput. Appl., 31: 101-115.
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7. Syam, M.I., M.A. Raja, M.M. Syam and H.M. Jaradat, 2018. An accurate method for solving the undamped duffing equation with cubic nonlinearity. Int. J. Appl. Comput. Math., Vol. 4. No.2. 10.1007/s40819-018-0502-1.
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8. Shah, S.M., R. Samar and M.A.Z. Raja, 2018. Fractional-order algorithms for tracking Rayleigh fading channels. Nonlinear Dyn., 92: 1243-1259.
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9. Sabir, Z., M.A. Manzar, M.A.Z. Raja, M. Sherazand A.M. Wazwaz, 2018. Neuro-heuristics for nonlinear singular Thomas-Fermi systems. Appl. Soft Comput., 65: 152-169.
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10. Raja, M.A.Z., Z. Shah, M.A. Manzar, I. Ahmad, M. Awais and D. Baleanu, 2018. A new stochastic computing paradigm for nonlinear Painleve II systems in applications of random matrix theory. Eur. Phys. J. Plus, 10.1140/epjp/i2018-12080-4.
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11. Raja, M.A.Z., M.A. Manzar, F.H. Shah and F.H. Shah, 2018. Intelligent computing for Mathieu’s systems for parameter excitation, vertically driven pendulum and dusty plasma models. Appl. Soft Comput., 62: 359-372.
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12. Raja, M.A.Z., M. Umar, Z. Sabir, J.A. Khan and D. Baleanu, 2018. A new stochastic computing paradigm for the dynamics of nonlinear singular heat conduction model of the human head. Eur. Phys. J. Plus, Vol. 133 No. 9. 10.1140/epjp/i2018-12153-4.
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13. Raja, M.A.Z., F.H.Shah and M.I. Syam, 2018. Intelligent computing approach to solve the nonlinear Van der Pol system for heartbeat model. Neural Comput. Appl., 30: 3651-3675.
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14. Raja, M.A.Z., F.H. Shah, M. Tariq and I. Ahmad, 2018. Design of artificial neural network models optimized with sequential quadratic programming to study the dynamics of nonlinear Troesch’s problem arising in plasma physics. Neural Comput. Appl., 29: 83-109.
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15. Raja, M.A.Z., A.A. Shah, A. Mehmood, N.I. Chaudhary and M.S. Aslam, 2018. Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system. Neural Comput. Appl., 29: 1455-1474.
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16. Raja, M.A.Z., A. Zameer, A.K.Kiani, A. Shehzad and M.A.R. Khan, 2018. Nature-inspired computational intelligence integration with Nelder–Mead method to solve nonlinear benchmark models. Neural Comput. Appl., 29: 1169-1193.
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17. Raja, M.A.Z., A. Mehmood, S.A. Niazi and S.M. Shah, 2018. Computational intelligence methodology for the analysis of RC circuit modelled with nonlinear differential order system. Neural Comput. Appl., 30: 1905-1924.
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18. Raja, M.A.Z., A. Mehmood, A. ur Rehman, A. Khan and A. Zameer, 2018. Bio-inspired computational heuristics for Sisko fluid flow and heat transfer models. Applied Soft Comput., 71: 622-648.
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19. Mehmood, A., M.S. Aslam, N.I. Chaudhary, A. Zameer and M.A.Z. Raja, 2018. Parameter estimation for Hammerstein control autoregressive systems using differential evolution. Signal Image Video Process., 12: 1603-1610.
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20. Mehmood, A., A. Zameer, S.H. Ling and M.A.Z. Raja, 2018. Design of neuro-computing paradigms for nonlinear nanofluidic systems of MHD Jeffery–Hamel flow. J. Taiwan Ins. Chem. Eng., 91: 57-85.
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21. Mehmood, A., A. Zameer, M.A.Z. Raja, R. Bibi, N.I. Chaudhary and M.S. Aslam, 2018. Nature-inspired heuristic paradigms for parameter estimation of control autoregressive moving average systems. Neural Comput. Appl., Vol. 1. No. 24. 10.1007/s00521-018-3406-4.
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22. Mehmood, A., A. Zameer and M.A.Z. Raja, 2018. Intelligent computing to analyze the dynamics of Magnetohydrodynamic flow over stretchable rotating disk model. Appl. Soft Comput., 67: 8-28.
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23. Masood, Z., K. Majeed, R. Samar and M.A.Z. Raja, 2018. Design of epidemic computer virus model with effect of quarantine in the presence of immunity. Fundamenta Informaticae, 161: 249-273.
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24. Khan, N.A., T. Hameed, N.A. Khan and M.A.Z. Raja, 2018. A heuristic optimization method of fractional convection reaction an application to diffusion process. Therm. Sci., 22: S243-S252.
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25. Chaudhary, N.I., S. Zubair and M.A.Z. Raja, 2018. Design of momentum LMS adaptive strategy for parameter estimation of Hammerstein controlled autoregressive systems. Neural Comput. Appl., 30: 1133-1143.
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26. Chaudhary, N.I., M.A. Manzar and M.A.Z. Raja, 2018. Fractional Volterra LMS algorithm with application to Hammerstein control autoregressive model identification. Neural Comput. Appl., Vol. 1. No. 14. 10.1007/s00521-018-3362-z.
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27. Chaudhary, N.I., M. Ahmed, Z.A. Khan, S. Zubair, M.A.Z. Raja and N. Dedovic, 2018. Design of normalized fractional adaptive algorithms for parameter estimation of control autoregressive autoregressive systems. Appl. Math. Modell., 55: 698-715.
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28. Awan, S.E., Z.A. Khan, M. Awais, S.U. Rehman and M.A.Z. Raja, 2018. Numerical treatment for hydro-magnetic unsteady channel flow of nanofluid with heat transfer. Results Phys., 9: 1543-1554.
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29. Awan, S.E., M. Awais, S.U. Rehman, S.A. Niazi and M.A.Z. Raja, 2018. Dynamical analysis for nanofluid slip rheology with thermal radiation, heat generation/absorption and convective wall properties. AIP Adv., Vol. 8. No. 7. 10.1063/1.5033470.
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30. Awais, M., S.E. Awan, N.M. Aqsa, S.U. Rehman and M.A.Z.R. Rehman, 2018. Numerical and analytical approach for Sakiadis rheology of generalized polymeric material with magnetic field and heat source/sink. Therm. Sci. 10.2298/TSCI180426284A.
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31. Awais, M., S.E. Awan, K. Iqbal, Z.A. Khan and M.A.Z. Raja, 2018. Hydromagnetic mixed convective flow over a wall with variable thickness and Cattaneo-Christov heat flux model: OHAM analysis. Results physics, 8: 621-627.
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32. Ara, A., N.A. Khan, O.A. Razzaq, T. Hameed and M.A.Z. Raja, 2018. Wavelets optimization method for evaluation of fractional partial differential equations: An application to financial modelling. Adv. Differ. Equations, Vol. 1. No. 8. 10.1186/s13662-017-1461-2.
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33. Ara, A., N.A. Khan, F. Naz, M.A.Z. Raja and Q. Rubbab, 2018. Numerical simulation for Jeffery-Hamel flow and heat transfer of micropolar fluid based on differential evolution algorithm. AIP Advances, Vol. 8. No. 1. 10.1063/1.5011727.
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34. Akbar, S., F. Zaman, M. Asif, A.U. Rehman and M.A.Z. Raja, 2018. Novel application of FO-DPSO for 2-D parameter estimation of electromagnetic plane waves. Neural Comput. Appl., 10.1007/s00521-017-3318-8.
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35. Ahmad, I., S. Ahmad, M. Awais, S.U.I. Ahmad, M.A.Z. Raja, 2018. Neuro-evolutionary computing paradigm for Painleve equation-II in nonlinear optics. Eur. Phys. J. Plus, 10.1140/epjp/i2018-12013-3.
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36. Ahmad, I., F. Ahmad, M.A.Z. Raja, H. Ilyas, N. Anwar and Z. Azad, 2018. Intelligent computing to solve fifth-order boundary value problem arising in induction motor models. Neural Comput. Appl., 29: 449-466.
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37. Zameer, A., J. Arshad, A. Khan and M.A.Z. Raja, 2017. Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks. Energy Conver. Manage., 134: 361-372.
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38. Shah, S.M., R. Samar, N.M. Khan and M.A.Z. Raja, 2017. Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization. Nonlinear Dyn., 88: 839-858.
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39. Raja, M.A.Z., T. Ahmed and S.M. Shah, 2017. Intelligent computing strategy to analyze the dynamics of convective heat transfer in MHD slip flow over stretching surface involving carbon nanotubes. J. Taiwan Inst. Chem. Eng., 80: 935-953.
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40. Raja, M.A.Z., S.A. Niazi and S.A. Butt, 2017. An intelligent computing technique to analyze the vibrational dynamics of rotating electrical machine. Neurocomput., 219: 280-299.
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41. Raja, M.A.Z., S. Azad AND S.M. Shah, 2017. Bio-inspired computational heuristics to study the boundary layer flow of the Falkner-Scan system with mass transfer and wall stretching. Appl. Soft Comput., 57: 293-314.
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42. Raja, M.A.Z., R. Samar, M.A. Manzar and S.M. Shah, 2017. Design of unsupervised fractional neural network model optimized with interior point algorithm for solving Bagley–Torvik equation. Math. Comput. Simul., 132: 139-158.
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43. Raja, M.A.Z., M.S. Aslam, N.I. Chaudhary, M. Nawaz and S.M. Shah, 2017. Design of hybrid nature-inspired heuristics with application to active noise control systems. Neural Comput. Appl., 10.1007/s00521-017-3214-2.
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44. Raja, M.A.Z., J.A. Khan, A. Zameer, N.A. Khan and M.A. Manzar, 2017. Numerical treatment of nonlinear singular Flierl–Petviashivili systems using neural networks models. Neural Comput. Appl., doi.org/10.1007/s00521-017-3193-3.
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45. Raja, M.A.Z., I. Ahmad, I. Khan, M.I. Syam and A.M. Wazwaz, 2017. Neuro-heuristic computational intelligence for solving nonlinear pantograph systems. Front. Inf. Technol. Electron. Eng., 18: 464-484.
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46. Masood, Z., K. Majeed, R. Samar and M.A.Z. Raja, 2017. Design of Mexican Hat Wavelet neural networks for solving Bratu type nonlinear systems. Neurocomput., 221: 1-14.
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47. Majeed, K., Z. Masood, R. Samar and M.A.Z. Raja, 2017. A genetic algorithm optimized Morlet wavelet artificial neural network to study the dynamics of nonlinear Troesch’s system. Appl. Soft Comput., 56: 420-435.
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48. Fateh, M.F., A. Zameer, N.M. Mirza, S.M. Mirza and M.A.Z. Raja, 2017. Biologically inspired computing framework for solving two-point boundary value problems using differential evolution. Neural Comput. Appl., 28: 2165-2179.
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49. Chaudhary, N.I., S. Zubair and M.A.Z. Raja, 2017. A new computing approach for power signal modeling using fractional adaptive algorithms. ISA transactions, 68: 189-202.
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50. Chaudhary, N.I., M.S. Aslam and M.A.Z. Raja, 2017. Modified Volterra LMS algorithm to fractional order for identification of Hammerstein non-linear system. IET Signal Process., 11: 975-985.
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51. Awais, M., S.E. Awan, M.I. Syam, M.A.Z. Raja and A.M. Wazwaz, 2017. Unsteady rheology of mhd newtonian material with soret and dufours effects. Int. J. Appl. Comput. Math., 3: 1299-1311.
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52. Aslam, M.S., N.I. Chaudhary and M.A.Z. Raja, 2017. A sliding-window approximation-based fractional adaptive strategy for Hammerstein nonlinear ARMAX systems. Nonlinear Dyn., 87: 519-533.
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53. Akbar, S., M.A.Z. Raja, F. Zaman, T. Mehmood and M.A.R. Khan, 2017. Design of bio-inspired heuristic techniques hybridized with sequential quadratic programming for joint parameters estimation of electromagnetic plane waves. Wireless Pers. Commun., 96: 1475-1494.
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54. Ahmad, I., M.A.Z. Raja, M. Bilal and F. Ashraf, 2017. Neural network methods to solve the Lane–Emden type equations arising in thermodynamic studies of the spherical gas cloud model. Neural Comput. Appl., 28: 929-944.
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55. Shah, S.M., R. Samar, N.M. Khan and M.A.Z. Raja, 2016. Fractional-order adaptive signal processing strategies for active noise control systems. Nonlinear Dyn., 85: 1363-1376.
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56. Raja, M.A.Z., U. Farooq, N.I. Chaudhary and A.M. Wazwaz, 2016. Stochastic numerical solver for nanofluidic problems containing multi-walled carbon nanotubes. Applied Soft Comput., 38: 561-586.
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57. Raja, M.A.Z., R. Samar, E.S. Alaidarous and E. Shivanian, 2016. Bio-inspired computing platform for reliable solution of Bratu-type equations arising in the modeling of electrically conducting solids. Applied Math. Modell., 40: 5964-5977.
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58. Raja, M.A.Z., M.A.R Khan, T. Mahmood, U. Farooq and N.I. Chaudhary, 2016. Design of bio-inspired computing technique for nanofluidics based on nonlinear Jeffery–Hamel flow equations. Can. J. Phys., 94: 474-489.
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59. Raja, M.A.Z., J.A. Khan, N.I. Chaudhary and E. Shivanian, 2016. Reliable numerical treatment of nonlinear singular Flierl-Petviashivili equations for unbounded domain using ANN, GAs and SQP. Applied Soft Comput., 38: 617-636.
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60. Raja, M.A.Z., A.K. Kiani, A. Shehzad and A. Zameer, 2016. Memetic computing through bio-inspired heuristics integration with sequential quadratic programming for nonlinear systems arising in different physical models. SpringerPlus, Vol. 5. No. 1. 10.1186/s40064-016-3750-8.
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61. Raja, M.A.Z., A. Zameer, A.U. Khan and A.M. Wazwaz, 2016. A new numerical approach to solve Thomas–Fermi model of an atom using bio-inspired heuristics integrated with sequential quadratic programming. SpringerPlus, Vol. 5. No. 1. 10.1186/s40064-016-3093-5.
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62. Khan, N.A., S. Khan, A. Shaikh and M.A.Z. Raja, 2016. MHD stagnation point flow of nanofluids over an off centered rotating disk in a porous medium via Haar wavelet. J. Nanofluids, 5: 444-458.
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63. Khan, N.A., A. Shaikh, M.Z. Raja and S. Khan, 2016. A neural computational intelligence method based on Legendre polynomials for fuzzy fractional order differential equation. J. Appl. Math. Stat. Inf., 12: 67-82.
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64. Ahmad, I., M.A.Z. Raja, M. Bilal and F. Ashraf, 2016. Bio-inspired computational heuristics to study Lane–Emden systems arising in astrophysics model. SpringerPlus, Vol. 5. No. 1. 10.1186/s40064-016-3517-2.
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65. Raja, M.A.Z., Z. Sabir, N. Mahmood, E.S. Al-Aidarous and J.A. Khan, 2015. Design of stochastic solvers based on genetic algorithms for solving nonlinear equations. Neural Comput. Applic., 26: 1-23.
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66. Raja, M.A.Z., R. Samar, T. Haroon and S.M. Shah, 2015. Unsupervised neural network model optimized with evolutionary computations for solving variants of nonlinear MHD Jeffery-Hamel problem. Applied Math. Mech., 36: 1611-1638.
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67. Raja, M.A.Z., M.A. Manzar and R. Samar, 2015. An efficient computational intelligence approach for solving fractional order Riccati equations using ANN and SQP. Applied Math. Modell., 39: 3075-3093.
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68. Raja, M.A.Z., J.A. Khan, A.M. Siddiqui, D. Behloul, T. Haroon and R. Samar, 2015. Exactly satisfying initial conditions neural network models for numerical treatment of first Painleve equation. Applied Soft Comput., 26: 244-256.
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69. Raja, M.A.Z., J.A. Khan and T. Haroon, 2015. Stochastic Numerical treatment for thin film flow of third grade fluid using unsupervised neural networks. J. Taiwan Inst. Chem. Eng., 48: 26-39.
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70. Raja, M.A.Z., F.H. Shah, A.A. Khan and N.A. Khan, 2015. Design of bio-inspired computational intelligence technique for solving steady thin film flow of Johnson-Segalman fluid on vertical cylinder for drainage problems. J. Taiwan Inst. Chem. Eng., (In Press). 10.1016/j.jtice.2015.10.020.
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71. Raja, M.A.Z. and N.I. Chaudhary, 2015. Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems. Signal Process., 107: 327-339.
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72. Khan, J.A., M.A.Z. Raja, M.M. Rashidi, M.I. Syam and A.M. Wazwaz, 2015. Nature-inspired computing approach for solving non-linear singular Emden-Fowler problem arising in electromagnetic theory. Connection Sci., (In Press). 10.1080/09540091.2015.1092499.
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73. Khan, J.A., M.A.Z. Raja, M.I. Syam, S.A.K. Tanoli and S.E. Awan, 2015. Design and application of nature inspired computing approach for nonlinear stiff oscillatory problems. Neural Comput. Applic., 26: 1763-1780.
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74. Chaudhary, N.I., M.A.Z. Raja and A.U.R. Khan, 2015. Design of modified fractional adaptive strategies for Hammerstein nonlinear control autoregressive systems. Nonlinear Dyn., 82: 1811-1830.
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75. Chaudhary, N.I. and M.A.Z. Raja, 2015. Identification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithms. Nonlinear Dyn., 79: 1385-1397.
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76. Chaudhary, N.I. and M.A.Z. Raja, 2015. Design of fractional adaptive strategy for input nonlinear Box-Jenkins systems. Signal Process., 116: 141-151.
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77. Aslam, M.S. and M.A.Z. Raja, 2015. A new adaptive strategy to improve online secondary path modeling in active noise control systems using fractional signal processing approach. Signal Process., 107: 433-443.
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78. Shah, S.M., R. Samar, M.A.Z. Raja and J.A. Chambers, 2014. Fractional normalised filtered-error least mean squares algorithm for application in active noise control systems. Electron. Lett., 50: 973-975.
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79. Sabir, Z. and M.A.Z. Raja, 2014. Numeric treatment of nonlinear second order multi-point boundary value problems using ANN, GAs and sequential quadratic programming technique. Int. J. Ind. Eng. Comput., 5: 431-442.
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80. Raja, M.A.Z., R. Samar and M.M. Rashidi, 2014. Application of three unsupervised neural network models to singular nonlinear BVP of transformed 2D Bratu equation. Neural Comput. Applic., 25: 1585-1601.
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81. Raja, M.A.Z., J.A. Khan, S.M. Shah, R. Samar and D. Behloul, 2014. Comparison of three unsupervised neural network models for first Painleve transcendent. Neural Comput. Applic., 26: 1055-1071.
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82. Raja, M.A.Z., 2014. Unsupervised neural networks for solving Troesch's problem. Chin. Phys. B, Vol. 23, No. 1. 10.1088/1674-1056/23/1/018903.
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83. Raja, M.A.Z., 2014. Stochastic numerical treatment for solving Troesch's problem. Inform. Sci., 279: 860-873.
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84. Raja, M.A.Z., 2014. Solution of the one-dimensional Bratu equation arising in the fuel ignition model using ANN optimised with PSO and SQP. Connection Sci., 26: 195-214.
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85. Raja, M.A.Z., 2014. Numerical treatment for boundary value problems of pantograph functional differential equation using computational intelligence algorithms. Applied Soft Comput., 24: 806-821.
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86. Raja, M.A.Z. and S.I. Ahmad, 2014. Numerical treatment for solving one-dimensional Bratu problem using neural networks. Neural Comput. Applic., 24: 549-561.
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87. Raja, M.A.Z. and R. Samar, 2014. Solution of the 2-dimensional Bratu problem using neural network, swarm intelligence and sequential quadratic programming. Neural Comput. Applic., 25: 1723-1739.
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88. Raja, M.A.Z. and R. Samar, 2014. Numerical treatment of nonlinear MHD Jeffery-Hamel problems using stochastic algorithms. Comput. Fluids, 91: 28-46.
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89. Raja, M.A.Z. and R. Samar, 2014. Numerical treatment for nonlinear MHD Jeffery-Hamel problem using neural networks optimized with interior point algorithm. Neurocomputing, 124: 178-193.
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90. Raja, M.A.Z. and N.I. Chaudhary, 2014. Adaptive strategies for Parameter estimation of Box-Jenkins systems. IET Signal Process., 8: 968-980.
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91. Raja, M.A.Z., S.I. Ahmad and R. Samar, 2013. Neural network optimized with evolutionary computing technique for solving the 2-dimensional Bratu problem. Neural Comput. Applic., 23: 2199-2210.
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92. Raja, M.A.Z., J.A. Khan, S.I. Ahmad and I.M. Qureshi, 2013. Numerical Treatment for Painleve Equation I Using Neural Networks and Stochastic Solvers. In: Innovation in Intelligent Machines-3: Contemporary Achievements in Intelligent Systems, Jordanov, I. and L.C. Jain (Eds.). Chapter 7, Springer, Berlin, Germany, ISBN: 978-3-642-32176-4, pp: 103-117.
93. Khan, J.A., M.A.Z. Raja and A. Umer, 2013. Hybrid computational approach GA-SQP for antenna array signal processing in mobile communication. Sci. Int., 25: 203-213.
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94. Khan, J.A. and M.A.Z. Raja, 2013. Artificial intelligence based solver for governing model of radioactivity cooling, self-gravitating clouds and clusters of galaxies. Res. J. Applied Sci. Eng. Technol., 6: 450-456.
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95. Chaudhary, N.I., M.A.Z. Raja, J.A. Khan and M.S. Aslam, 2013. Identification of input nonlinear control autoregressive systems using fractional signal processing approach. Scient. World J. 10.1155/2013/467276.
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96. Zaman, F., I.M. Qureshi, A. Naveed, J.A. Khan and R.M.A. Zahoor, 2012. Amplitude and directional of arrival estimation: Comparison between different techniques. Progr. Electromagn. Res. B, 39: 319-335.
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97. Raja, M.A.Z., J.A. Khan, S.I. Ahmad and I.M. Qureshi, 2012. A new stochastic technique for Painleve equation-I using neural network optimized with swarm intelligence. Comput. Intell. Neurosci. 10.1155/2012/721867.
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98. Khan, J.A., M.A.Z. Raja and I.M. Qureshi, 2012. An application of evolutionary computational technique to non-linear singular system arising in polytrophic and isothermal sphere. Global J. Res. Eng.: Numer., 12: 9-15.
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99. Raja, M.A.Z., J.A. Khan and I.M. Qureshi, 2011. Swarm intelligence optimized neural network for solving fractional order systems of Bagley-Tervik equation. Eng. Intell. Syst., 19: 41-51.
100. Raja, M.A.Z., J.A. Khan and I.M. Qureshi, 2011. Solution of fractional order system of Bagley-Torvik equation using evolutionary computational intelligence. Math. Probl. Eng. 10.1155/2011/675075.
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101. Raja, M.A.Z., I.M. Qureshi and J.A. Khan, 2011. Swarm intelligence optimized neural networks for solving fractional differential equations. Int. J. Innov. Comput. Inform. Control, 7: 6301-6318.
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102. Khan, J.A., M.A.Z. Raja and I.M. Qureshi, 2011. Stochastic computational approach for complex nonlinear ordinary differential equations. Chin. Phys. Lett., Vol. 28, No. 2. 10.1088/0256-307X/28/2/020206.
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103. Khan, J.A., M.A.Z. Raja and I.M. Qureshi, 2011. Numerical treatment of nonlinear Emden-Fowler equation using stochastic technique. Ann. Math. Artif. Intell., 63: 185-207.
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104. Khan, J.A., M.A.Z. Raja and I.M. Qureshi, 2011. Novel approach for a van der Pol oscillator in the continuous time domain. Chin. Phys. Lett., Vol. 28. 10.1088/0256-307X/28/11/110205.
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105. Khan, J.A., M.A.Z. Raja and I.M. Qureshi, 2011. Hybrid evolutionary computational approach: Application to van der Pol oscillator. Int. J. Phys. Sci., 6: 7247-7261.
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106. Raja, M.A.Z., J.A. Khan and I.M. Qureshi, 2010. Evolutionary Computational Intelligence in Solving the Fractional Differential Equations. In: Intelligent Information and Database Systems, Nguyen, N.T., M.T. Le and J. Swiatek (Eds.). Springer-Verlag, Germany, ISBN: 978-3-642-12144-9, pp: 231-240.
107. Raja, M.A.Z., J.A. Khan and I.M. Qureshi, 2010. A new stochastic approach for solution of Riccati differential equation of fractional order. Ann. Math. Artif. Intell., 60: 229-250.
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108. Zahoor, R.M.A., J.A. Khan and I.M. Qureshi, 2009. Evolutionary computation technique for solving Riccati differential equation of arbitrary order. World Acad. Sci. Eng. Technol., 58: 303-309.
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109. Zahoor, R.M.A. and I.M. Qureshi, 2009. A modified least mean square algorithm using fractional derivative and its application to system identification. Eur. J. Scient. Res., 35: 14-21.
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110. Khan, J.A., M.A.Z. Raja and I.M. Qureshi, 2009. Swarm intelligence for the problem of non-linear ordinary differential equations and its application to well-known Wessinger's equation. Eur. J. Scient. Res., 34: 514-525.
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111. Junaid, A., M.A.Z. Raja and I.M. Qureshi, 2009. Evolutionary computing approach for the solution of initial value problems in ordinary differential equations. World Acad. Sci. Eng. Technol., 55: 578-581.