A comparative study on applicability and efficiency of machine learning algorithms for modeling gamma-ray shielding behaviors

[1]  N. M. Badiger,et al.  Determination of mass attenuation coefficient for some polymers using Monte Carlo simulation , 2015 .

[2]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[3]  Héctor Pomares,et al.  Clustering-Based TSK Neuro-fuzzy Model for Function Approximation with Interpretable Sub-models , 2005, IWANN.

[4]  O. Klein,et al.  Über die Streuung von Strahlung durch freie Elektronen nach der neuen relativistischen Quantendynamik von Dirac , 1929 .

[5]  J. H. Hubbell,et al.  Review and history of photon cross section calculations , 2006, Physics in medicine and biology.

[6]  J. J. Moré,et al.  Levenberg--Marquardt algorithm: implementation and theory , 1977 .

[7]  N. Kucuk,et al.  Modeling of gamma ray energy-absorption buildup factors for thermoluminescent dosimetric materials using multilayer perceptron neural network: A comparative study , 2013 .

[8]  Iskender Akkurt,et al.  Prediction of photon attenuation coefficients of heavy concrete by fuzzy logic , 2010, J. Frankl. Inst..

[9]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[10]  S. J. Kiartzis,et al.  Short term load forecasting using fuzzy neural networks , 1995 .

[11]  João Carlos Figueira Pujol,et al.  A neural network approach to fatigue life prediction , 2011 .

[12]  H. Tekin,et al.  Validation of MCNPX with Experimental Results of Mass Attenuation Coefficients for Cement, Gypsum and Mixture , 2017 .

[13]  M. Medhat Application of neural network for predicting photon attenuation through materials , 2018, Radiation Effects and Defects in Solids.

[14]  A. Zolfaghari,et al.  Application of artificial neural network for predicting the optimal mixture of radiation shielding concrete , 2016 .

[15]  O. İçelli,et al.  A novel comprehensive utilization of vanadium slag/epoxy resin/antimony trioxide ternary composite as gamma ray shielding material by MCNP 6.2 and BXCOM , 2019 .

[16]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[17]  B. Tromborg,et al.  Coulomb corrections in non-relativistic scattering , 1973 .

[18]  A. Rezaei,et al.  Application of artificial neural network in precise prediction of cement elements percentages based on the neutron activation analysis , 2016 .

[19]  Hans A. Bethe,et al.  On the Stopping of Fast Particles and on the Creation of Positive Electrons , 1934 .

[20]  Selçuk Alp,et al.  Modelling of Multi-Objective Transshipment Problem with Fuzzy Goal Programming , 2018, International Journal of Transportation.