  Understanding the astrophysical nature of the first stars still remains an
unsolved problem in cosmology. The redshifted global 21-cm signal
$(\text{T}_{21})$ acts as a treasure trove to probe the Cosmic Dawn era -- when
the intergalactic medium was mostly neutral. Many experiments, like SARAS 3,
EDGES, and DARE have been proposed to probe the cosmic dawn era. However,
extracting the faint cosmological signal buried inside a brighter foreground
$\mathcal{O}(10^4)$ remains challenging. Thus we use two artificial neural
networks, one for extraction of foreground, via parameter estimation with
R-square $(R^2)$ score $(0.8034 - 0.9984)$, from the total sky-averaged
spectrum. The other is for extraction of a global 21-cm signal in the presence
of noise with $R^2$ score $(0.6960 - 0.9978)$. Considering an excess radio
background scenario, we constructed all possible $\text{T}_{21}$ signals in the
EDGES limit, along with the foreground signal, to train the neural networks.
Here, we also explore the variation in parameter estimation due to the presence
of heating of intergalactic medium by background radio radiation mediated via
Ly$\alpha$ photons from first stars, and we found that the presence and absence
of this effect can change the global 21-cm signal estimation by $\sim 33$ mK in
the EDGES limit $(\sim -0.5\text{ K})$
