  Context. Activity on the far side of the Sun is routinely studied through the
analysis of the seismic oscillations detected on the near side using
helioseismic techniques such as phase shift sensitive holography. Recently, the
neural network FarNet was developed to improve these detections. Aims. We aim
to create a new machine learning tool, FarNet II, which further increases the
scope of FarNet, and to evaluate its performance in comparison to FarNet and
the standard helioseismic method for detecting far side activity. Methods. We
developed FarNet II, a neural network that retains some of the general
characteristics of FarNet but improves the detections in general, as well as
the temporal coherence among successive predictions. The main novelties are the
implementation of attention and convolutional long short term memory (ConvLSTM)
modules. A cross validation approach, training the network 37 times with a
different validation set for each run, was employed to leverage the limited
amount of data available. We evaluate the performance of FarNet II using three
years of extreme ultraviolet observations of the far side of the Sun acquired
with the Solar Terrestrial Relations Observatory (STEREO) as a proxy of
activity. The results from FarNet II were compared with those obtained from
FarNet and the standard helioseismic method using the Dice coefficient as a
metric. Results. FarNet II achieves a Dice coefficient that improves that of
FarNet by over 0.2 points for every output position on the sequences from the
evaluation dates. Its improvement over FarNet is higher than that of FarNet
over the standard method. Conclusions. The new network is a very promising tool
for improving the detection of activity on the far side of the Sun given by
pure helioseismic techniques. Space weather forecasts can potentially benefit
from the higher sensitivity provided by this novel method.
