In this study, we propose a novel method for reconstructing
the radiation distribution of a one-dimensional radioactive source
using Machine Learning (ML) algorithms and plastic scintillation
optical fiber. The wavelength spectrum unfolding technique is used
to estimate the source position accurately. We compare the accuracy
and time efficiency of three different algorithms, namely,
Generalized reduced gradient (GRG), Maximum likelihood expectation
maximization (MLEM), and ML, in the single-source and dual-source
cases. Our results demonstrate that MLEM algorithm has a shorter
reconstruction time with comparable accuracy of position and
intensity compared to GRG algorithm for single-source case. For
dual-source case, ML algorithm provides real-time estimation of
position and intensity with acceptable errors, while GRG algorithm
has a larger error in intensity estimation and longer computation
time. Our proposed ML algorithms offer useful guidance for practical
applications in radiation source location.