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Updated: May 13, 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

Automated authorship attribution using advanced signal classification techniques.

Maryam Ebrahimpour1, Tālis J Putniņš, Matthew J Berryman

  • 1School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, South Australia, Australia.

Plos One
|February 26, 2013
PubMed
Summary
This summary is machine-generated.

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This study introduces automated authorship attribution methods using word frequencies, achieving over 90% accuracy on English texts. The techniques were applied to disputed historical documents, including the Federalist Papers and the Letter to the Hebrews.

Area of Science:

  • Computational Linguistics
  • Digital Humanities
  • Forensic Linguistics

Background:

  • Authorship attribution is crucial for historical and legal text analysis.
  • Traditional methods can be labor-intensive and subjective.
  • Automated approaches offer scalable and objective solutions.

Purpose of the Study:

  • To develop and evaluate automated authorship attribution schemes.
  • To apply these schemes to historically significant texts with disputed authorship.
  • To assess the performance and limitations of word-frequency-based classification.

Main Methods:

  • Development of two automated authorship attribution models: Multiple Discriminant Analysis (MDA) and Support Vector Machine (SVM).
  • Text preprocessing involved retaining only a-z characters and spaces for enhanced portability.

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Last Updated: May 13, 2026

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

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Published on: December 15, 2023

  • Classification based on word frequencies within texts.
  • Leave-one-out cross-validation for performance evaluation.

    • Main Results:

    • Achieved classification accuracies exceeding 90% on a corpus of undisputed English texts.
    • Applied methods to the Federalist Papers, yielding insights into authorship.
    • Evaluated authorship of the Letter to the Hebrews against known Greek texts, identifying limitations.

    Conclusions:

    • Automated authorship attribution using word frequencies is highly accurate and effective.
    • The developed methods provide valuable tools for literary and historical scholarship.
    • Further research is needed to address identified limitations and expand applicability.