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- W2016098439 abstract "In this study, wall thickness for using in primary radiation shielding was determined in di erent energy ranges using tenth value layer by arti cial neural networks. Radiation energy values, tenth value layers and negative logarithm of transmission factor (n) were selected as input parameters and wall shielding thickness values selected as output parameters. Consequently, developed arti cial neural networks model outputs were compared with experimental results and it was seen that the results were harmonious." @default.
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- W2016098439 date "2013-02-01" @default.
- W2016098439 modified "2023-09-23" @default.
- W2016098439 title "Prediction Primary Radiation Shielding Wall Thickness with Artificial Neural Networks" @default.
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- W2016098439 doi "https://doi.org/10.12693/aphyspola.123.171" @default.
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