  We consider a monolayer of graphene under uniaxial, tensile strain and
simulate Bloch oscillations for different electric field orientations parallel
to the plane of the monolayer using several values of the components of the
uniform strain tensor, but keeping the Poisson ratio in the range of observable
values. We analyze the trajectories of the charge carriers with different
initial conditions using an artificial neural network, trained to classify the
simulated signals according to the strain applied to the membrane. When the
electric field is oriented either along the Zig-Zag or the Armchair edges, our
approach successfully classifies the independent component of the uniform
strain tensor with up to 90\% of accuracy and an error of $\pm1\%$ in the
predicted value. For an arbitrary orientation of the field, the classification
is made over the strain tensor component and the Poisson ratio simultaneously,
obtaining up to 97\% of accuracy with an error that goes from $\pm5\%$ to
$\pm10\%$ in the strain tensor component and an error from $\pm12.5\%$ to
$\pm25\%$ in the Poisson ratio.
