Optimizing neutrino mass predictions with competitive chaotic learning neural network algorithm and SU(2)L×A4×Z3×Z2 symmetry

A novel neutrino mass model is analyzed, leveraging the [Formula: see text] flavor symmetry group incorporating five additional scalars. This construction yields a mass matrix that successfully explain current neutrino mass and mixing accurately by using competitive chaotic learning neural network algorithm (CCLNNA) which is the new variant of the neural network algorithm (NNA). The use of CCLNNA for optimal parameters for computing [Formula: see text] matrices and neutrino masses as: [Formula: see text], [Formula: see text], [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]) for the normal (inverted) mass hierarchy, is illustrated. Notably, this analysis provides a testable prediction for the effective neutrino mass parameters as: [Formula: see text], [Formula: see text] ([Formula: see text], [Formula: see text]) for the normal (inverted) mass hierarchy. This analysis shows that model is consistent with an inverted hierarchy not for normal mass hierarchy because of the sum of the masses are consistent with the latest cosmological bound of [Formula: see text] for inverted hierarchy. The mixing angles [Formula: see text] (with [Formula: see text] and [Formula: see text]), the Dirac phase [Formula: see text], and the Majorana CP-violating phases [Formula: see text] and [Formula: see text] are also predicted.