Research Papers
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An Intelligent Framework for AI-Based Crime Prediction and Real-Time Emergency Response in Smart Policing Systems
The increasing complexity of urban environments and rising crime rates demand intelligent, data-driven solutions for effective law enforcement. This paper presents an AI-driven Crime Management System that integrates machine learning techniques — Random Forest for crime classification and K-Means clustering for hotspot detection — to analyze historical and real-time crime data. A real-time SOS emergency module powered by Socket.IO enables instant communication between citizens and law enforcement with live location tracking. Additionally, the system incorporates automated FIR processing with risk-based prioritization. Experimental evaluation demonstrates significant improvements in response time (from 8–10 min to 2–3 min), crime prediction accuracy (≈90%), and resource allocation efficiency, providing a scalable and intelligent framework for smart policing.
By Omkar Rathod · Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (VIMEET)
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
Precision Agriculture Using Machine Learning
Precision agriculture uses machine learning and technologies like IoT sensors, drones, and satellite data to improve farming efficiency. It helps farmers monitor soil, weather, and crop health in real time, enabling better decisions for irrigation, disease control, and yield prediction. This data-driven approach reduces resource waste, lowers costs, and increases productivity. Overall, it supports sustainable farming and improves food security.
By Keshav Kolekar · University of Mumbai
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