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Detection of redshifted \ion{H}{i} 21cm emission is a potential probe forinvestigating the Universe's first billion years. However, given thesignificantly brighter foreground, detecting 21cm is observationally difficult.The Earth's ionosphere considerably distorts the signal at low frequencies byintroducing directional-dependent effects. Here, for the first time, we reportthe use of Artificial Neural Networks (ANNs) to extract the global 21cm signalcharacteristics from the composite all-sky averaged signal, includingforeground and ionospheric effects such as refraction, absorption, and thermalemission from the ionosphere's F and D-layers. We assume a 'perfect' instrumentand neglect instrumental calibration and beam effects. To model the ionosphericeffect, we considered the static and time-varying ionospheric conditions forthe mid-latitude region where LOFAR is situated. In this work, we trained theArtificial Neural Network (ANN) model for various situations using a syntheticset of the global 21cm signals created by altering its parameter space based onthe "$\rm \tanh$" parameterized model and the Accelerated Reionization EraSimulations (ARES) algorithm. The obtained result shows that the ANN model canextract the global signal parameters with an accuracy of $\ge 96 \% $ in thefinal study when we include foreground and ionospheric effects. On the otherhand, a similar ANN model can extract the signal parameters from the finalprediction dataset with an accuracy ranging from $97 \%$ to $98 \%$ whenconsidering more realistic sets of the global 21cm signals based on physicalmodels.

Extracting the Global 21cm signal from Cosmic Dawn and Epoch of Reionization in the presence of Foreground and Ionosphere