Chatbot Ticketing System
Ayush Pratap Singh Department of Computer Science and Engineering (Artificial Intelligence and
Machine Learning),
Babu Banarasi Das Institute of Technology and Management, Lucknow
ayushpratap8707@gmail.com
Samridhi Jaiswal Department of Computer Science and Engineering (Artificial Intelligence and
Machine Learning),
Babu Banarasi Das Institute of Technology and Management, Lucknow
samridhi06jaiswal@gmail.com
Preety Pandey
Department of Computer Science and Engineering (Artificial Intelligence and MachineLearning), Babu Banarasi Das Institute of
Technology and Management, Lucknow
Abstract - – The Online Chatbot-Based Ticketing System aims to revolutionize the museum ticketing and visitor management process, making it more efficient, accessible, and user-friendly. Powered by advanced technologies like TensorFlow and large language models (LLMs), the chatbot enables human-like interactions that can handle visitor queries, provide dynamic pricing, and offer personalized recommendations based on past activities and preferences. The system incorporates QR-based ticketing for easy entry, real-time crowd updates through heatmaps to help manage visitor flow, and SMS-based booking options to ensure easy access for everyone.
One of the key features of the system is smart itinerary planning, which helps visitors optimize their schedules, reduce overcrowding, and enhance their overall experience. It also offers multilingual capabilities and personalized content delivery, catering to a diverse range of users. From a technical perspective, the system uses robust backend frameworks like Firebase and Go, along with a Flutter-based frontend for smooth and efficient integration. The solution not only improves operational efficiency but also supports sustainability by reducing the need for printed materials, such as maps and guides. It ensures data security and ethical usage while providing economic benefits, such as reduced staffing costs, increased ticket sales, and enhanced revenue streams. This chatbot-based system has the potential to set a new benchmark in museum ticketing and cultural tourism.
KEYWORDS: Deep Learning, Large language models(LLM), Convolutional Neural Network (CNN), Natural language Processing(NLP) , Tensorflow
