DRHAFHNet: Dense Resolution High-Order Attention Forward Harmonic Network-based learning effectiveness of shopfloor employees with digital twin

The quick advances in the field of self-driving vehicles and connected automobiles have increased
the commercial worth of automobile applications. Digital Twin is employed as a promising
technology to modernize the automotive industry. Moreover, the development of digital twins has
offered smart manufacturing systems with knowledge-making capabilities. Hence, the training in
a virtual environment minimizes the errors on the shop floor. Still, the extraction of relevant
insights to establish the optimal course sequence for the shop floor employees is computationally
difficult. To overcome such issues, this paper develops the Dense Resolution High-order Attention
Forward Harmonic Network (DRHAFHNet)-based course sequence recommendation for learning
the effectiveness of shopfloor employees with digital twin. The shop floor owner collects the data
from the physical space, and the cloud server stores the data from the shop floor owner. The twin
manager collects the data from the cloud server and simulates in the virtual space. The virtual data
is stored in the cloud, and the course sequence recommendation is performed by the DRHAFHNet
using a digital twin E-learning platform. Moreover, the proposed model attains the Normalized
Mean Square Error (MSE), Normalized Mean Absolute Percentage Error (MAPE), and
Normalized Root MSE (RMSE) of 0.334, 0.332, and 0.342.