The Role of Chatbots and Virtual Assistants in Enhancing Customer Service and Engagement: A Stacked BiLSTM and A-GRU Technique

By analyzing the performance of machine learning-powered chatbots and virtual assistants, this approach seeks to automate customer support. To keep up with the ever-increasing need for scalable, efficient, and quick customer service, businesses are increasingly relying on DL and other innovative technologies. These technologies are being utilized to enhance customer support operations, and this proposal compares their effectiveness to that of more traditional types of assistance. Preprocessing, feature selection, and model training are all part of the suggested method. The purpose of preprocessing is to convert it into identically lengthened sequences of numbers. Problems with intuition and specification make up feature selection. During the whole training process, the Stacked A-GRU-BiLSTM model was continually employed. Compared to GRU and BiLSTM, this cutting-edge approach achieves a mean accuracy of 93.29%.