In this work, we apply a machine learning algorithm to the regression analysis of the nuclear cross-section of neutron-induced nuclear reactions of molybdenum isotopes, 92Mo at incident neutron energy around 14 MeV. The machine learning algorithms used in this work are the Random Forest (RF), Gaussian Process Regression (GPR), and Support Vector Machine (SVM). The performance of each algorithm is determined and compared by evaluating the root mean square error (RMSE) and the correlation coefficient (R2). We demonstrate that machine learning can produce a better regression curve of the nuclear cross-section for the neutron-induced nuclear reaction of 92Mo isotopes compared to the simulation results using EMPIRE 3.2 and TALYS 1.9 from the previous literature. From our study, GPR is found to be better compared to RF and SVM algorithms, with R2=1 and RMSE =0.33557. We also employed the crude estimation of property (CEP) as inputs, which consist of simulation nuclear cross-section from TALYS 1.9 and EMPIRE 3.2 nuclear code alongside the experimental data obtained from EXFOR (1 April 2021). Although the Experimental only (EXP) dataset generates a more accurate cross-section, the use of CEP-only data is found to generate an accurate enough regression curve which indicates a potential use in training machine learning models for the nuclear reaction that is unavailable in EXFOR.

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We investigated the generation of proton- and alpha-induced nuclear cross-section data in the production of Indium-111 (111In) for application in nuclear medicine. Here, we are interested in three reaction channels, which are 109Ag (α, 2n), 111Cd (p, n) and 112Cd (p, 2n), in the production of 111In. A random forest algorithm was used to generate nuclear cross-section data by using an experimental nuclear cross-section from the Experimental Nuclear Reaction Data (EXFOR) database as input. Hence, reasonably accurate regression curves of nuclear cross-section data could be produced with the evaluated nuclear data library ENDF/B-VII.0 set as the benchmark.