Abstract
Current study results on Support Vector Machines have been published. According to news reporting originating from Jilin, People’s Republic of China, by NewsRx correspondents, research stated, “Tagged neutron detection system combined with support vector machine (SVM) is proposed for the detection of explosives hidden inside walls. The detection system was based on an ING-27 neutron generator as neutron source, two lutetium yttrium silicate (LYSO) detectors as gamma-detectors, and one silicon detector as alpha-detector.” Financial support for this research came from Ministry of Science and Technology of Jilin Province. Our news editors obtained a quote from the research from Northeast Normal University, “The difference in gamma-ray counts within the time window for different samples was combined with the peak area ratios of the elemental peaks in the gamma-energy spectra and used as input vectors for an SVM. A Gaussian kernel function was used as a kernel function and a grid search method as optimization of the penalty factor c and hyperparameter g of the SVM in this experiment. Fivefold cross-validation was used to evaluate the models developed. The correctness of the support vector machine was found to be 100%, 98.3%, and 95% for the target sample detection in that order, and the fivefold cross-validation accuracy was 100%, 97.5%, and 93.3%, respectively.”
NETL
NSTL