Our Recent Publications
A look at the latest peer-reviewed, open-access research papers published on PaperNova by students and scholars from over 60 countries. Every paper is citable, downloadable as PDF, and issued with a certificate of publication.
Looking to publish your own work? Submit your research paper and go live in minutes.
Enhancing Manufacturing Supply Chains through Improved Visibility and Collaboration Using Artificial Intelligence within the Industry 5.0 Paradigm
This paper seeks to explore and transition Industry 4.0 to Industry 5.0 concept among Indian manufacturing industries and their supply chains. Many industries have forecast that the future of operations in supply chain management (SCM) may change steadily dramatically, from planning, scheduling, optimisation, to transportation, with the presence and integration of artificial intelligence (AI). Although the integration of Artificial Intelligence (AI) into supply chain management has emerged a pivotal role in enhancing efficiency and resilience in industries operations. The paper delves into integrating of AI in SCM processes strengthening supply chain resilience including improving data sharing, enhancing real time visibility and ensuring smooth communication among partners. With the technological advancement we tried to build a AI interface which uses real time analytics and secures collaboration to fix poor visibility and drive efficiency across complex manufacturing supply chain. It ensures the interface is User Centric and Effective in solving the core visibility and collaboration problem.
By Keshav Arun Tyagi · Vishwaniketan's Institute of Management Entrepreneurship and Engineering Technology
Expansion.AI: A Globally Scalable AI-Driven Decision-Support Framework for Autonomous International Market
International business expansion is a high-stakes strategic process requiring the synthesis of fragmented data across regulatory, economic, and competitive domains. Traditional expansion strategies rely on human-centric consultancy, which is often cost-prohibitive for Small and Medium Enterprises (SMEs) and prone to cognitive bias. This paper introduces Expansion AI, an integrated decision-support framework that leverages Multi-Criteria Decision Making (MCDM) and Explainable Artificial Intelligence (XAI) to automate global market evaluation. Utilizing real-time data from the World Bank, WTO, and UN Comtrade, Expansion.AI generates transparent, data-driven recommendations that reduce the time-to-insight for strategic planning by over 70%.
By Shivani Mangesh Bhosale · University of Mumbai
VITA: Visual Intelligent Teaching Assistant
This paper introduces VITA (Visual Intelligent Teaching Assistant), an artificial intelligence–driven system for the automatic generation of animated mathematics explanations. Conventional methods for creating visual educational content are often time-intensive and require specialized skills, limiting their scalability and accessibility. VITA addresses this limitation by enabling users to submit mathematical queries through a simple interface, which are then interpreted using a Large Language Model (LLM). The system converts these queries into executable animation scripts using the Manim library, producing step-by-step visual explanations that may be supplemented with narration. By transforming textual queries into dynamic visual content, the proposed approach enhances conceptual understanding and supports personalized learning experiences. Despite challenges related to computational cost and script accuracy, the system demonstrates strong applicability in digital learning environments. VITA offers a scalable and efficient solution for automated visual instruction in mathematics.
By Manan Patil · Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (iMEET), Khalapur, Raigad, Maharashtra 410202, India.
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
Academic Assistant AI Platform Integrated Using Chatbot.
The Academic Assistance AI Platform is designed to revolutionize student support through the use of intelligent conversational artificial intelligence. This platform leverages advanced natural language processing and large language models (LLMs) to provide real-time, context-aware academic guidance. Students can interact with the AI assistant to resolve academic queries, access learning materials, check schedules, and receive personalized assistance. Developed using Python, Flask, MongoDB, and DeepSeek LLM API, the system seamlessly integrates AI with college data systems to deliver fast, reliable, and efficient responses. This report outlines the motivation, design, implementation, and future scope of the project, emphasizing its potential to enhance academic engagement and accessibility. This paper details the research, design, implementation and evaluation of the platform, demonstrating its potential to revolutionize student support service.
By Soham Sanjay Kank · Vishwaniketan's Institute of Management Entrepreneurship and Engineering Technology.
About PaperNova's research archive
PaperNova is an international platform where students and researchers can publish research papers online instantly and receive a verifiable certificate of publication. Our open-access archive covers engineering, computer science, artificial intelligence, biotechnology, management, finance, and more.
Every paper on PaperNova is peer-reviewed, citable, and indexed on the open web so it can be discovered by future researchers. If you are looking for the fastest way to publish a journal paper internationally, start by browsing our AI research papers, machine learning papers, or computer science archive.