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The Intelligent Chatbot Ticketing System leverages advanced artificial intelligence techniques, particularly Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), to automate and enhance the process of managing and responding to customer service tickets. This system aims to streamline the ticketing process by efficiently understanding and categorizing user inquiries, providing quick resolutions, and improving overall user experience. The RNN is employed to capture sequential patterns in user interactions, enabling the system to understand context and provide coherent, context-aware responses. Meanwhile, CNN is used for feature extraction from text inputs, helping the system identify important keywords and topics related to customer queries. This combination of RNN and CNN allows the system to not only classify and prioritize tickets effectively but also learn from past interactions to continuously improve its performance. The integration of these deep learning models ensures an intelligent, adaptive, and user-friendly ticketing system that reduces the manual effort required from customer service agents and provides timely assistance to users.
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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Tarollu prasad, tarolluprasad@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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