Hybrid chemometric approach for estimating the heat of detonation of aromatic energetic compounds

[1]  Taoreed O. Owolabi,et al.  Modeling the magnetocaloric effect of manganite using hybrid genetic and support vector regression algorithms , 2019, Physics Letters.

[2]  T. Owolabi Development of a particle swarm optimization based support vector regression model for titanium dioxide band gap characterization , 2019, Journal of Semiconductors.

[3]  Taoreed O. Owolabi,et al.  Development of hybrid extreme learning machine based chemo-metrics for precise quantitative analysis of LIBS spectra using internal reference pre-processing method. , 2018, Analytica chimica acta.

[4]  Akhil Garg,et al.  Design optimization of battery pack enclosure for electric vehicle , 2018 .

[5]  Akhil Garg,et al.  An evolutionary framework in modelling of multi-output characteristics of the bone drilling process , 2018, Neural Computing and Applications.

[6]  Akhil Garg,et al.  Design and analysis of capacity models for Lithium-ion battery , 2018 .

[7]  Taoreed O. Owolabi,et al.  A hybrid intelligent scheme for estimating band gap of doped titanium dioxide semiconductor using crystal lattice distortion , 2017 .

[8]  Sunday Olusanya Olatunji,et al.  Extreme Learning machines and Support Vector Machines models for email spam detection , 2017, 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE).

[9]  Hamdi Abdi,et al.  Combined heat and power economic dispatch problem using gravitational search algorithm , 2016 .

[10]  Ali R. Yildiz,et al.  Structural design of vehicle components using gravitational search and charged system search algorithms , 2015 .

[11]  Sunday O. Olatunji,et al.  Development and validation of surface energies estimator (SEE) using computational intelligence technique , 2015 .

[12]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[13]  M. Keshavarz Estimating Heats of Detonation and Detonation Velocities of Aromatic Energetic Compounds , 2008 .

[14]  M. Keshavarz Predicting heats of detonation of explosives via specified detonation products and elemental composition , 2007 .

[15]  M. Keshavarz Quick estimation of heats of detonation of aromatic energetic compounds from structural parameters. , 2007, Journal of hazardous materials.

[16]  M. Keshavarz Determining heats of detonation of non-aromatic energetic compounds without considering their heats of formation. , 2007, Journal of hazardous materials.

[17]  M. Keshavarz Simple procedure for determining heats of detonation , 2005 .

[18]  H. R. Pouretedal,et al.  AN EMPIRICAL METHOD FOR PREDICTING DETONATION PRESSURE OF CHNOFCL EXPLOSIVES , 2004 .

[19]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[20]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[21]  Abdullah Alqahtani,et al.  Incorporation of GSA in SBLLM-based neural network for enhanced estimation of magnetic ordering temperature of manganite , 2017, J. Intell. Fuzzy Syst..

[22]  Taoreed Olakunle Owolabi,et al.  Computational intelligence method of estimating solid-liquid interfacial energy of materials at their melting temperatures , 2016, J. Intell. Fuzzy Syst..

[23]  Sunday O. Olatunji,et al.  Estimation of Superconducting Transition Temperature TC for Superconductors of the Doped MgB2 System from the Crystal Lattice Parameters Using Support Vector Regression , 2015 .

[24]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[25]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[26]  S. J. Jacobs,et al.  Chemistry of Detonations. I. A Simple Method for Calculating Detonation Properties of C–H–N–O Explosives , 1968 .