In this paper, we demonstrate that void fraction could be predicted independent of type of flow regime in twophase
flows using 60Co source and one scintillator NaI detector. For this purpose, firstly three features (Feature No. 1:
counts under Compton continuum; Feature No. 2: counts under full energy peak of 1173 keV; Feature No. 3: counts under
full energy peak of 1333 keV) were extracted from registered gamma-ray spectrum in detector. Secondly, these three
features were utilized as the inputs of artificial neural network model of multilayer perceptron (MLP) in order to achieve
the best structure for predicting the void fraction. In each structure, void fraction was considered constantly as the output of
MLP network. Using the optimum MLP network structure, void fraction was predicted independent of type of flow regime
in gas–liquid two-phase flow with MRE of less than 2.5%. Although obtained error using one detector for predicting the
void fraction is more than when two or more detectors are utilized, using fewer detectors has advantages such as making
the detection system simpler and reducing economical expenses.