AI-Based Research Paper Analyzer And Generator Using NLP And Machine Learning

Abstract: In recent years, the exponential growth of academic publications has created a demand for intelligent systems that can analyze and generate research content efficiently. This study presents an AI-Based Research Paper Analyzer and Generator that leverages Natural Language Processing (NLP) and Machine Learning techniques to automate tasks such as grammar correction, plagiarism detection, keyword extraction, and research paper generation.

The proposed system integrates TF-IDF-based keyword extraction, transformer-based semantic similarity models for plagiarism detection, and a Flask-based web application for real-time interactions with users. Additionally, the system includes a recommendation engine that retrieves relevant research papers using external APIs.

Experimental evaluation demonstrates that the system significantly reduces manual effort, improves writing quality, and provides structured outputs suitable for IEEE/IJARCCE format. The proposed framework offers a scalable and efficient solution for both students and researchers.

Keywords: NLP; Machine Learning; Research Paper Analysis; Text Summarization; TF-IDF; Plagiarism Detection