  Artificial neural networks (ANN) have been successfully used in the last
years to identify patterns in astronomical images. The use of ANN in the field
of asteroid dynamics has been, however, so far somewhat limited. In this work
we used for the first time ANN for the purpose of automatically identifying the
behaviour of asteroid orbits affected by the M1:2 mean-motion resonance with
Mars. Our model was able to perform well above 85% levels for identifying
images of asteroid resonant arguments in term of standard metrics like
accuracy, precision and recall, allowing to identify the orbital type of all
numbered asteroids in the region. Using supervised machine learning methods,
optimized through the use of genetic algorithms, we also predicted the orbital
status of all multi-opposition asteroids in the area. We confirm that the M1:2
resonance mainly affects the orbits of the Massalia, Nysa, and Vesta asteroid
families.
