Research Papers
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AI-Powered Chatbot System for Tourism Ticket Booking
This research presents the design and comprehensive implementation of an intelligent, AI-powered chatbot-based ticketing ecosystem specifically engineered for the modern museum and cultural heritage sector. By synthesizing the capabilities of Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML), the proposed system transcends the limitations of traditional, menu-driven booking platforms. The core objective of this study is to replace static web forms with a dynamic, conversational interface that facilitates real-time reservations, automated multi-turn query handling, and a frictionless user journey. At the architectural heart of the system lies a sophisticated integration of high-performance technologies. We utilize the Gemini 1.5 Flash generative model for advanced intent recognition and semantic understanding, ensuring the chatbot can interpret complex human requests with high contextual accuracy. This is supported by a robust Flask-based backend that serves as the central orchestrator for processing asynchronous HTTP requests and managing session states. For data persistence and real-time synchronization, the system leverages Firebase, a NoSQL cloud database that ensures high availability and scalability for concurrent user interactions. Security and transaction integrity are prioritized through the implementation of the Razorpay API, which provides a PCI-compliant gateway for encrypted online payments. Upon successful transaction verification, the system autonomously generates a unique, QR-coded digital ticket using specialized Python libraries, thereby eliminating the need for physical paper and reducing the environmental footprint of the ticketing process. Beyond mere utility, the system incorporates personalized recommendation logic to suggest exhibits based on user preferences, further enhancing the "smart tourism" experience. Experimental evaluation and rigorous stress testing indicate that this integrated approach significantly reduces response latency, minimizes human intervention, and maximizes booking throughput compared to conventional systems. The results demonstrate that the synergy of generative AI and cloud-native infrastructure creates a highly resilient and user-centric platform. This project serves as a definitive proof-of-concept for the practical application of Large Language Models (LLMs) in the tourism industry, highlighting a scalable pathway toward fully autonomous, intelligent service solutions.
By Mehul Ramdas Pawade · Mumbai University
Suraksha Setu: AI-Driven Criminal Identification Using Facial Recognition Technology with Aadhaar Integration for Scalable Intelligent Public Safety in Urban India
Suraksha Setu is an AI-powered dual-platform public safety ecosystem designed to address the critical operational gaps in Indian law enforcement. The system integrates Facial Recognition Technology (FRT) using the DeepFace framework (VGGFace2 backbone), real-time multi-class object detection via YOLOv8 fine-tuned on a 14,200-image custom dataset, and biometric identity verification through the UIDAI Aadhaar Authentication API 2.5. The architecture comprises two primary subsystems: (1) a Citizen Safety Portal providing geofence-triggered crime alerts via Firebase Cloud Messaging, SOS GPS triangulation, and a community gamification layer; and (2) a Police Operational Dashboard delivering live AI-monitored CCTV analytics, LIDAR/UGS-based restricted-zone intrusion detection, crime heatmap visualization via MongoDB GeoJSON, and centralized Aadhaar-linked offender database access. Comprehensive evaluation demonstrates that DeepFace achieves 94.8% facial recognition accuracy under operational CCTV conditions, surpassing FaceNet (91.7%) and ArcFace (89.3%). YOLOv8 attains a mean Average Precision (mAP@0.5) of 91.3% across five threat categories, with weapon detection reaching 92.1% F1-score. System response latency averages 1.2 seconds for SOS dispatch and 2.8 seconds for FRT matching — a 75–80% reduction over conventional systems. An ablation study confirms that each architectural component contributes measurably to system performance. A security assessment across seven threat vectors demonstrates compliance with NIST SP 800-175B, UIDAI regulations, and OWASP Top-10 standards. Suraksha Setu represents a replicable, nationally scalable framework for AI-integrated, constitutionally compliant smart policing
By Sachin Chaurasiya · Vishwaniketan iMEET
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