Malidarre, Roya BoodaghiArslankaya, SeherNar, MelekKırelli, YasinKarpuz, NurdanÖzhan Doğan, SerapMalidarreh, Parisa Boodaghi2022-06-072022-06-072022Malidarre, R. B., Arslankaya, S., Nar, M., Kirelli, Y., Erdamar, I. Y. D., Karpuz, N., . . . Malidarreh, P. B. (2022). Deep learning prediction for gamma-ray attenuation behavior of the KNN-LMN based lead-free ceramics. Emerging Materials Research, doi:10.1680/jemmr.22.000122046-0147https://doi.org/10.1680/jemmr.22.00012https://hdl.handle.net/20.500.12713/2821The significance and novelty of the present work is the preparation of the non-lead ceramic by the general formula of (1-x) K0.5Na0.5NbO3-xLaMn0.5Ni0.5O3 with different x (0<x<20) (mol%) to examine the shielding qualities of KNN-LMN ceramic. This is done using PhyX/PSD calculation and predicts the attenuation behavior of the samples utilizing the Deep Learning (DL) algorithm. From the attained results it is seen that the higher the x (concentration of the LMN in KNN-LMN lead-free ceramics) the better shielding proficiency is observed in terms of gamma shielding performances for chosen KNN-LMN based lead-free ceramics. In all sections, a good agreement is observed between PhyX/PSD results and DL predictions. © 2022 ICE Publishing: All rights reserved.eninfo:eu-repo/semantics/closedAccessArticleWOS:0009816505000082-s2.0-85129752416Q310.1680/jemmr.22.00012Q3