  In this paper, we introduce a novel methodology for leveraging shape-based
characteristics of magnetograms of active region (AR) patches and provide a
novel capability for predicting solar flares covering the entirety of the solar
disk (AR patches spanning from -90$^{\circ}$ to +90$^{\circ}$ of solar
longitude). We create three deep learning models: (i) ResNet34, (ii) MobileNet,
and (iii) MobileViT to predict $\geq$M-class flares and assess the efficacy of
these models across various ranges of solar longitude. Given the inherent
imbalance in our data, we employ augmentation techniques alongside
undersampling during the model training phase, while maintaining imbalanced
partitions in the testing data for realistic evaluation. We use a composite
skill score (CSS) as our evaluation metric, computed as the geometric mean of
the True Skill Score (TSS) and the Heidke Skill Score (HSS) to rank and compare
models. The primary contributions of this work are as follows: (i) We introduce
a novel capability in solar flare prediction that allows predicting flares for
each ARs throughout the solar disk and evaluate and compare the performance,
(ii) Our candidate model (MobileNet) achieves a CSS=0.51 (TSS=0.60 and
HSS=0.44), CSS=0.51 (TSS=0.59 and HSS=0.44), and CSS=0.48 (TSS=0.56 and
HSS=0.40) for AR patches within $\pm$30$^{\circ}$, $\pm$60$^{\circ}$,
$\pm$90$^{\circ}$ of solar longitude respectively. Additionally, we demonstrate
the ability to issue flare forecasts for ARs in near-limb regions (regions
between $\pm$60$^{\circ}$ to $\pm$90 $^{\circ}$) with a CSS=0.39 (TSS=0.48 and
HSS=0.32), expanding the scope of AR-based models for solar flare prediction.
This advancement opens new avenues for more reliable prediction of solar
flares, thereby contributing to improved forecasting capabilities.
