In recent years, there has been a significant increase in the demand for intelligent and automated customer service solutions across various industries. Conversational AI-driven Question and Answer (Q&A) chatbots have emerged as a groundbreaking technology in this domain, transforming the way organizations engage with users. This project is centered on the creation of an AI-enhanced Q&A chatbot system that utilizes Natural Language Processing (NLP) and Machine Learning (ML) methodologies to analyze and comprehend user inquiries, discern intent, and produce precise, contextually relevant responses. In contrast to conventional rule-based systems, the proposed chatbot incorporates sophisticated models such as transformers (BERT, GPT) to achieve a more profound understanding of language and intent recognition. It offers multilingual support, real-time database connectivity, and sentiment analysis to facilitate human-like, tailored interactions. The system is designed to be scalable, platform-agnostic, and continually enhances its performance through user interactions via adaptive learning techniques. By automating the retrieval of information and support functions, this chatbot improves user experience, alleviates the workload on human agents, and guarantees service availability around the clock. Keywords: Sign language, information retrieval, computer vision, natural language processing, accessibility, deaf individuals.
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Mohammed Jaffar Ganjur
R Roopa
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
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Ganjur et al. (Sat,) studied this question.
www.synapsesocial.com/papers/68af59d2ad7bf08b1eade318 — DOI: https://doi.org/10.55041/ijsrem52004
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