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Updated: Jun 8, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
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Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Automatic figure ranking and user interfacing for intelligent figure search.

Hong Yu1, Feifan Liu, Balaji Polepalli Ramesh

  • 1Department of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America. hongyu@uwm.edu

Plos One
|October 16, 2010
PubMed
Summary

Researchers developed an automatic figure ranking system for bioscience literature. This system efficiently identifies and prioritizes important figures, improving scientific information access for researchers.

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Area of Science:

  • Biomedical Informatics
  • Scientific Literature Analysis

Background:

  • Figures are crucial experimental results in bioscience articles, essential for research validation and hypothesis testing.
  • The increasing volume of bioscience literature makes accessing figures challenging.
  • A novel figure ranking concept is introduced to prioritize figures by their contribution to knowledge discovery.

Purpose of the Study:

  • To develop and validate an automatic figure ranking system for bioscience literature.
  • To improve the efficiency of accessing important figures in scientific articles.
  • To integrate figure ranking into a user interface for enhanced scientific information retrieval.

Main Methods:

  • Empirical validation of the figure ranking hypothesis with over 100 bioscience researchers.

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Last Updated: Jun 8, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Development of unsupervised natural language processing (NLP) approaches for automatic figure ranking.
  • Creation of novel user interfaces (UIs) incorporating figure ranking.

    • Main Results:

    • The best automatic figure ranking system achieved a weighted error rate of 0.2, outperforming baseline systems.
    • 92% of researchers preferred UIs that enlarged the most important figures.
    • Researchers showed no statistical preference difference between UIs with automatic and human-ranked figures.

    Conclusions:

    • Automatic figure ranking and user interfacing are implementable in online publishing.
    • The integrated system offers a more efficient and robust method for accessing biomedical information.
    • The enhanced figure search engine will better facilitate bioscientists' access to figures of interest.