Intelligent Chatbot Ticketing System Using RNN and CNN

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.

Publication Details

Journal
International Journal of Research and Development in Engineering Sciences
Published
2025-02-05
DOI
https://doi.org/10.63328/ijrdes-v7ri1p6
Primary Topic
Genomics and Chromatin Dynamics
Type
article
Field-Weighted Citation Impact
0.00
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article

Intelligent Chatbot Ticketing System Using RNN and CNN

International Journal of Research and Development in Engineering Sciences
Genomics and Chromatin Dynamics
article

Intelligent Chatbot Ticketing System Using RNN and CNN

article en

Abstract

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.

International Journal of Research and Development in Engineering SciencesVol. 7(1)
Openalex Percentile: Top 45%
Genomics and Chromatin Dynamics
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