RPAT: Research Paper Analytic Tool

Project Logo
Project Facts
Area: Scientometrics, Data Science, NLP
Supervisor: Imran Ihsan
Team: Shahzaib Tariq, M.Umar Nawaz, Syed Muhammad Taqi
For: Air University, Islamabad
Status: In Progress
Tools & Technologies
Microsoft .NET Framework,
Microsoft SQL Server,
Python NLTK,
D3 JS Visulaization
Project Abstract
A Citation Based Tool To Assist Conferences & Journals To Evaluate the Quality of Submitted Paper
Scientific Research is being carried out across the globe in numerous fields. Evaluation of a scientific research in a scholarly big data is reliant on bibliometric indicators or citations. For these indicators to work, the scientific research has to be published and indexed. The conferences and journals do not have such bibliometric indicators to measure the quality of a submitted research paper. Therefore, there is a need of an analytic system that can easily and quickly measure the quality of an un-published research paper before it goes in review process. This research paper defines an analytic scheme to measure the quality of a research paper by measuring the quality of references used within the paper. References are a list of sources that represents the best documents selected by the author to layout the foundation of his/her research paper. Thus, an initial check on the quality of references, selected by the author, can provide a valid indication about the submitted paper. The scheme measures the quality of references using the bibliometric indicators or citations, collected from different corpora such as Google Scholar, of the referenced and published work.  For experiment, we have taken all the research papers from a local IEEE conference held in 2017 and evaluated each paper on the proposed analytic scheme and classifying them in high, medium and low impact papers.
Project Interface
Project Poster
  • Research & Development 90%
  • Project Management 80%
  • Client Communication 80%
  • Team Management 85%
  • Microsoft Project and Visio 80%
  • GUI Design 90%
  • Web & Mobile Application 95%
  • CMS Design & Development 85%
  • HTML 5 90%
  • CSS 3.0 90%
  • PHP / MySQL 90%
  • JS / JQuery 85%
  • JAVA 60%
  • C# / ASP.NET 50%
  • SQL 95%
  • C++ 90%
  • XML / XML Schema 90%
  • XPath / XQuery 90%
  • RDF / RDFs 90%
  • SPARQL 80%
  • OWL 80%