Research Paper Quality Recognition

Research Paper Quality Recognition

    Presenter(s)

    Sadwik Gummadavelli

    Comments

    11:40-12:00, LTC Studio

    Description

    Knowledge and innovations are shaped by using the quality and credibility of the scientific research. There always remains a challenge how to distinguish between impactful high-quality research and flawed. This project proposes a very systematic approach to classifying the research papers into good and bad categories where bad papers are those retracted from journals or conference proceedings and good papers are characterized by high citation counts. We explore the underlying factors that contribute to a papers scholarly influence or its eventual rejection by analyzing by citation patterns publication meta data and retraction records. We used machine learning models and feature extraction techniques to identify anomalies, trends and potential predictors of research quality. The findings of our study insights into highlighting the importance of citation behavior, the dynamics of academic publishing and scientific accountability. This study adds to the larger conversation about academic impact evaluation and lays the groundwork for automated tools that can help assess the reliability of research papers.

    Publication Date

    4-23-2025

    Project Designation

    Course Project - CPS 596 P3

    Primary Advisor

    Tam Nguyen

    Primary Advisor's Department

    Computer Science

    Keywords

    Stander Symposium, College of Arts and Sciences

    Institutional Learning Goals

    Scholarship; Community; Vocation

    Research Paper Quality Recognition

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