Enhanced Decision Making Through Deep Learning-Driven Robotic Process Automation

Robotics process automation (RPA) is a key technique in modern technology. The RPA guarantees that tasks are completed accurately and consistently, which is essential for decision-making. Further, the performance of the RPA can be enhanced by combined with deep learning (DL) models. DL models perform prediction more effectively than traditional rule-based methods, where RPA handles repetitive tasks and reduces human effort with less processing time. Nowadays, more digital information is generated due to the development of new technologies. DL-driven RPA can effectively process these pieces of digital information. Therefore, this paper presents the performance of DL-driven RPA for name and gender identification. RPA will further use the result of the prediction for automated email drafting and other digital activities. This paper combines three DL models- convolutional neural network, long short-term memory, and gated recurrent unit models with RPA to predict the gender of a specific name. Finally, the proposed model is extensively analyzed through simulation results. The simulation results showed the DL-driven RPA models' efficacy based on accuracy, loss, precision, recall, F1-score, R2-score, mean-squared error, mean absolute error, and confusion matrix.