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Skeleton-based Human Action Recognition (SHAR) is one of the most trending research topics in computer vision, which relies on the investigation of multi-modal data acquired from different sensory devices. Due to its faster execution speed, skeleton-based automated SHAR systems are widely adopted in real-time applications such as surveillance systems, behavior analysis, gesture recognition, and security systems. To build such an efficient system, the recognition model needs to be trained on a large-scale multi-modal dataset to accurately identify multi-class actions. However, the recognition of multi-class actions with higher accuracy requires the extraction of the spatio-temporal discriminative features, which is a challenging task. Moreover, the traditional handcrafted feature-based Machine Learning (ML) models when dealing with a large dataset, fail to preserve spatio-temporal correlations, resulting in poor …