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International Peer reviewed Engineering Journal || Crossref DOI || Impact Factor 8.6 || ISSN: 2582-5003

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Research & review articles are invited for publication in September 2026 (Vol. 28, Issue 3) || Submission: up to 28th September || Editorial decision: within 48 hrs.

A Secure Web-Based Research Management Framework Integrating Automated Plagiarism Detection for Higher Education

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  • A Secure Web-Based Research Management Framework Integrating Automated Plagiarism Detection for Higher Education

Joseph Oluwaseyi Adewuyi, Ayomide Afolashade Lawal *, Abimbola Teniola Bello and Oluwatobiloba Peter Suleman

Department of Computer Science, School of Computing and Engineering Sciences, Babcock University, Ilishan-Remo, Ogun State, Nigeria.

Research Article

Global Journal of Engineering and Technology Advances, 2026, 27(03), 037-044

Article DOI: 10.30574/gjeta.2026.27.3.0135

DOI url: https://doi.org/10.30574/gjeta.2026.27.3.0135

Received on 22 April 2026; revised on 31 May 2026; accepted on 02 June 2026

The University Research Submission System (URSS) represents a web-based platform, engineered and deployed to mitigate documented inefficiencies inherent in the management and submission of academic research within higher education institutions. Traditional submission methodologies frequently present challenges, including fragmented workflows, suboptimal version management, inadequate plagiarism screening capabilities, and deficient document preservation protocols. Constructed upon a three-tier client-server architecture, the URSS leverages PHP, MySQL, HTML, CSS, and JavaScript as its foundational technologies. The system further integrates third-party APIs to facilitate robust plagiarism detection and comprehensive analysis of AI-generated content. The platform supports role-based access for students, supervisors, and administrators, while also offering structured submission workflows, incorporating automated similarity checking, and managing a centralized digital repository. Evaluations demonstrated the reliability of the plagiarism detection module across various document types. Similarity scores observed during testing ranged from 34% for submissions identified as original content to 84% for materials determined to be fully duplicated. Furthermore, the AI detection component successfully identified purely machine-generated text, yielding probability scores between 90% and 99%, while hybrid documents, containing a mix of human and AI-generated content, typically received scores in the 35–55% range. All primary modules, encompassing authentication protocols, submission processing, document retrieval functionalities, and encryption mechanisms, consistently operated in accordance with their established specifications. This system is projected to reduce manual processing time by approximately 85%, positioning it as a scalable and economically viable alternative to existing commercial submission tools. In summary, the URSS exhibits substantial improvements in the efficiency, transparency, and academic integrity associated with university research submission environments.

Research Submission System; Plagiarism Detection; Web Application; Academic Management; University System

https://gjeta.com/sites/default/files/fulltext_pdf/GJETA-2026-0135.pdf

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Joseph Oluwaseyi Adewuyi, Ayomide Afolashade Lawal, Abimbola Teniola Bello and Oluwatobiloba Peter Suleman. A Secure Web-Based Research Management Framework Integrating Automated Plagiarism Detection for Higher Education. Global Journal of Engineering and Technology Advances, 2026, 27(03), 037-044. Article DOI: https://doi.org/10.30574/gjeta.2026.27.3.0135.

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


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