TY  - JOUR
VL  - 187
KW  - Decision trees; Iridium; Learning systems; Machine learning; Nuclear reactions; Tantalum
KW  -  ENDF/B-VII.0; In-buildings; Machine learning techniques; New approaches; Nuclear cross sections; Nuclear data; Nuclear data library; Random forest algorithm; Regression curve; Simulated datasets
KW  -  Isotopes
KW  -  iridium; tantalum; isotope
KW  -  Article; computer simulation; decision tree; human; machine learning; mathematical parameters; neutron; neutron radiation; random forest; simulation; machine learning
KW  -  Iridium; Isotopes; Machine Learning; Neutrons; Tantalum
ID  - scholars16434
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132228257&doi=10.1016%2fj.apradiso.2022.110306&partnerID=40&md5=eb80b0ae70f27663a406b039a2f81917
N2  - In this work, we proposed a new approach in generating nuclear data using machine learning techniques. This paper focused on generation of nuclear cross section for neutron induced-nuclear reaction on iridium isotopes (Ir-191) and tantalum isotopes (Ta-181) target, specifically 191Ir (n,p)191Os and 181Ta (n, 2n)180Ta using random forest algorithms. The input consists of experimental datasets obtained from EXOR and simulated datasets from TALYS 1.9. We found that the regression curve generated by our model is in good agreement with the evaluated nuclear data library ENDF/B-VII.0, which is set as the benchmark. This shows a potential in building a machine learning model for generating nuclear cross section data for both well studied and understudied nuclear reaction. © 2022 Elsevier Ltd
SN  - 09698043
JF  - Applied Radiation and Isotopes
N1  - cited By 0
AV  - none
PB  - Elsevier Ltd
A1  - Hamid, M.A.B.
A1  - Beh, H.G.
A1  - Shahrol Nidzam, N.N.
A1  - Chew, X.Y.
A1  - Ayub, S.
TI  - Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique
Y1  - 2022///
ER  -