Design and Development of an Efficient AI Chatbot for Customer Service Systems

Abstract

A chatbot is a virtual character with interactive text capabilities that can successfully communicate with any human. Conversational chatbots, sometimes referred to as chatbots or dialogue systems, are computer programs created to mimic human-user dialogue, particularly on the Internet. These chatbots can be utilized in a variety of settings, including entertainment, information gathering, customer service, and instructional help. Hence, in this research proposed a novel AI chatbot for handling complex problems in comprehensive analysis of contextual response generation and intent classification for customer support systems. Initially, the data is are collected from dataset, and text data processed through the Multiscale Transformer Vector Conversion phase (MTVC) approach which used for converting the text data into vectorization form that reduce dimensionality issues. The feature extraction approach uses Improved Osprey Optimization Algorithm (IOOA) which is used to effectively optimize feature weights. Then, the classification model is called Dilated Deep Temporal Convolutional Networks with Novel Loss Function (DDTCN-NLF) which effectively utilized for intent classification, which optimizes decision boundaries while reduce computational complexity and increased accuracy. Finally, response generation method Hybrid Cross Attention assisted Recurrent Bidirectional Encoder Representations from Transformer (HCAR-BERT) which is used to enhance the authentication of context through cross attention and recurrent memory and this method provide answer of user query. Experimental findings of this research showed that the effectiveness of the proposed method which attained highest accuracy value of 98.55%, an F1-score of 98.23%, respectively. In this research indicate that the suggested approach has improve performance, efficiency and complex problem solving.