  Accurate estimation of the Cosmic Microwave Background (CMB) angular power
spectrum is enticing due to the prospect for precision cosmology it presents.
Galactic foreground emissions, however, contaminate the CMB signal and need to
be subtracted reliably in order to lessen systematic errors on the CMB
temperature estimates. Typically bright foregrounds in a region lead to further
uncertainty in temperature estimates in the area even after some foreground
removal technique is performed and hence determining the underlying full-sky
angular power spectrum poses a challenge. We explore the feasibility of
utilizing artificial neural networks to predict the angular power spectrum of
the full sky CMB temperature maps from the observed angular power spectrum of
the partial sky in which CMB temperatures in some bright foreground regions are
masked. We present our analysis at large angular scales with two different
masks. We produce unbiased predictions of the full-sky angular power spectrum
and the underlying theoretical power spectrum using neural networks. Our
predictions are also uncorrelated to a large extent. We further show that the
multipole-multipole covariances of the predictions of the full-sky spectra made
by the ANNs are much smaller than those of the estimates obtained using the
method of pseudo-$C_l$.
