Design and Development of New Interactive Chatbot System for Mobile Service Providers through Heuristic-Based Ensemble Learning

Abstract

In recent times, the human agents for chat services are used to be more in the communication system among human users according to artificial intelligence. These chatbots seem to be cost-efficient and time-saving paradigm, and so it is widely developed for usage. Still, it suffers from satisfying the customer requirements, which reduces the users being less liable to conform to desires through the chatbot. The designed interactive system has to process the queries and gets a suitable reply in the chatbot. The data collection related to diverse queries is gathered. Then, the pre-processing is carried out. Further, the word to vector is conducted for extracting the features. Then, the weighted feature vector is attained through optimizing the weight by hybrid optimization named Hybrid Water Wave Barnacle Mating Optimization (HWW-BMO). Finally, detection is performed through Heuristic-based Extreme learning + Long Short-Term Memory (H-ExtLSTM) for predicting the intent related to the queries asked by mobile service providers, thus can extract the answers based on word similarity. Here, the parameters of ELM and LSTM are optimized by the HWW-BMO algorithm. Experiments show that the proposed joint models have achieved competitive results in different datasets in multiple scenarios compared with other models.

Keywords:

• Heuristic-based Extreme learning + Long Short-Term Memory
• Hybrid Water Wave Barnacle Mating Optimization
• interactive chatbot system
• mobile service providers
• weighted feature vector