A comparative study on applicability and efficiency of machine learning algorithms for modeling gamma-ray shielding behaviors |
Bilmez, Bayram
(Department of Physics, Faculty of Science and Art, Yildiz Technical University)
Toker, Ozan (Department of Physics, Faculty of Science and Art, Yildiz Technical University) Alp, Selcuk (Department of Industrial Engineering, Faculty of Mechanical Engineering, Yildiz Technical University) Oz, Ersoy (Department of Statistics, Faculty of Art and Science, Yildiz Technical University) Icelli, Orhan (Department of Physics, Faculty of Science and Art, Yildiz Technical University) |
1 | N. Kucuk, Modeling of gamma ray energy-absorption buildup factors for thermoluminescent dosimetric materials using multilayer perceptron neural network: a comparative study, Radiat. Phys. Chem. 86 (2013) 10-22, https://doi.org/10.1016/j.radphyschem.2013.01.021. DOI |
2 | O. Gencel, The application of artificial neural networks technique to estimate mass attenuation coefficient of shielding barrier, 12, Int. J. Phys. Sci. 4 (2009) 743-751. |
3 | F.H. Attix, Introduction to Radiological Physics and Radiation Dosimetry, John Wiley & Sons, 2008. |
4 | The Math Works, Inc. MATLAB. Version 2020b, The Math Works, Inc., 2020 computer software, https://www.mathworks.com. |
5 | J.K. Shultis, R.E. Faw, Radiation Shielding and Radiological Protection in Handbook of Nuclear Engineering, Springer, 2010. |
6 | M. Berger, XCOM: Photon Cross Sections Database, 2010, https://doi.org/10.2172/6016002. DOI |
7 | J.H. Hubbell, Review and history of photon cross section calculations, 13, Phys. Med. Biol. 51 (2006) 245, https://doi.org/10.1088/0031-9155/51/13/R15. DOI |
8 | H.O. Tekin, P.S. Vishwanath, T. Manici, E.E. Altunsoy, Validation of MCNPX with experimental results of mass attenuation coefficients for cement, gypsum and mixture, Journal of Radiation Protection and Research 42 (3) (2017) 154-157. https://doi.org/10.14407/jrpr.2017.42.3.154. DOI |
9 | V.P. Singh, S.P. Shirmardi, M.E. Medhat, N.M. Badiger, Determination of mass attenuation coefficient for some polymers using Monte Carlo simulation, Vacuum 119 (2015) 284-288, https://doi.org/10.1016/j.vacuum.2015.06.006. DOI |
10 | O. Klein, Y. Nishina, Uber die Streuung von Strahlung durch freielektronen nach der neuen relativistischen quantendynamik von Dirac, Z. Phys. 52 (1928) 853-868. |
11 | R.H. Pratt, P.M. Bergstrom Jr., L. Kissel, New Relativistic S-Matrix Results for Scattering beyond the Usual Anomalous Factors/beyond Impulse Approximation. No. UCRL-JC-114583, Lawrence Livermore National Lab., CA (United States), 1993. CONF-9208186-5. |
12 | S. Alp, T. Ozkan, Modelling of multi-objective transshipment problem with fuzzy goal programming, International Journal of Transportation 6 (2018) 9-20, https://doi.org/10.14257/ijt.2018.6.2.02. DOI |
13 | F. Rosenblatt, Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms, Cornell Aeronautical Lab Inc. Buffalo, NY, 1961. |
14 | A.S. Lapedes, R.M. Farber, How Neural Nets Work. Neural Information Processing Systems, 1988, pp. 442-456. |
15 | A. Davydenko, R. Fildes, Forecast error measures: critical review and practical recommendations, in: Business Forecasting: Practical Problems and Solutions, 34, Wiley, 2016. |
16 | L.A. Zadeh, 3, Fuzzy sets, Information and control 8 (1965) 338-353, https://doi.org/10.1016/S0019-9958(65)90241-X. DOI |
17 | M.E. Medhat, Application of neural network for predicting photon attenuation through materials, 3-4, Radiat. Eff. Defect Solid 174 (2019) 171-181, https://doi.org/10.1080/10420150.2018.1547903. DOI |
18 | A. Yadollahi, et al., Application of artificial neural network for predicting the optimal mixture of radiation shielding concrete, Prog. Nucl. Energy 89 (2016) 69-77, https://doi.org/10.1016/j.pnucene.2016.02.010. DOI |
19 | M. Sugeno, G.T. Kang, Structure identification of fuzzy model, Fuzzy Set Syst. 28 (1) (1988) 15-33, https://doi.org/10.1016/0165-0114(88)90113-3. DOI |
20 | J.J. More, The Levenberg-Marquardt algorithm: implementation and theory, in: Numerical Analysis, Springer, Berlin, Heidelberg, 1978, pp. 105-116. |
21 | P. Goyal, P. Dollar, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, K. He, Accurate, Large Mini Batch Sgd: Training Image Net in 1 Hour, 2017 arXiv preprint arXiv:1706.02677, https://arxiv.org/abs/1706.02677v2. |
22 | H.B. Kavanoz, O. Akcali, O. Toker, B. Bilmez, M. Caglar, O. Icelli, O, A novel comprehensive utilization of vanadium slag/epoxy resin/antimony trioxide ternary composite as gamma ray shielding material by MCNP 6.2 and BXCOM, Radiat. Phys. Chem. 165 (2019) 108446, https://doi.org/10.1016/j.radphyschem.2019.108446. DOI |
23 | A.G. Bakirtzis, J.B. Theocharis, S.J. Kiartzis, K.J. Satsios, Short term load forecasting using fuzzy neural networks, IEEE Trans. Power Syst. 10 (3) (1995) 1518-1524. DOI |
24 | G. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artificial neural networks: the state of the art, Int. J. Forecast. 14 (1) (1998) 35-62. DOI |
25 | C.M. Bishop, Neural Networks for Pattern Recognition, Oxford university press, 1995. |
26 | J.C.F. Pujol, J.M.A. Pinto, A neural network approach to fatigue life prediction, Int. J. Fatig. 33 (3) (2011) 313-322. DOI |
27 | I. Akkurt, C. Basyigit, S. Kilincarslan, A. Beycioglu, Prediction of photon attenuation coefficients of heavy concrete by fuzzy logic, J. Franklin Inst. 347 (9) (2010) 1589-1597. DOI |
28 | E.E. Zadeh, S.A.H. Feghhi, G.H. Roshani, A. Rezaei, Application of artificial neural network in precise prediction of cement elements percentages based on the neutron activation analysis, The European Physical Journal Plus 131 (5) (2016) 167. DOI |
29 | L.J. Herrera, et al., Clustering-Based TSK neuro-fuzzy model for function approximation with interpretable sub-models, in: International Work-Conference on Artificial Neural Networks, Springer, Berlin, Heidelberg, 2005, https://doi.org/10.1007/11494669_49. DOI |
30 | J. Hamilton, I. Overbo, I, B. Tromborg, Coulomb corrections in non-relativistic scattering, Nucl. Phys. B 60 (1973) 443-477. DOI |
31 | H. Bethe, W. Heitler, On the stopping of fast particles and on the creation of positive electrons, Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 146 (856) (1934) 83-112, https://doi.org/10.1098/rspa.1934.0140. DOI |
![]() |