Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)

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Binary-Class AI-Generated Content Detection Through Comprehensive Feature Engineering

Authors
Afifa Hoque Tisha1, *, Ayesha Banu2, Fatema-Tuj-Johora3, Riad Hossain4
1Department of Computer Science and Engineering, Premier University, Chittagong, 4000, Bangladesh
2Department of Computer Science and Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, 4349, Bangladesh
3Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, 1216, Bangladesh
4Department of Computer Science and Engineering, East Delta University, Chittagong, 4209, Bangladesh
*Corresponding author. Email: afifahoque57@gmail.com
Corresponding Author
Afifa Hoque Tisha
Available Online 8 June 2026.
DOI
10.2991/978-94-6239-664-7_74How to use a DOI?
Keywords
AI content detection; feature engineering; machine learning; deep learning; transformers; text classification; content authenticity
Abstract

The proliferation of AI-generated content necessitates robust detection systems across academia, journalism, and digital media. This study presents a comprehensive feature engineering framework combining 2,537 linguistic, stylometric, and semantic features for automated discrimination of AI-generated versus human-written text across four content categories. We systematically evaluate nine classification models spanning traditional machine learning, deep neural networks, and transformer architectures on a balanced dataset of 1,367 samples. Our feature engineering approach, refined to 800 optimal discriminators through chisquared selection, demonstrates that carefully engineered features with classical classifiers can surpass complex neural architectures while maintaining computational efficiency. This work provides empirical evidence for feature-centric model design and practical deployment insights for production environments.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
8 June 2026
ISBN
978-94-6239-664-7
ISSN
1951-6851
DOI
10.2991/978-94-6239-664-7_74How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Afifa Hoque Tisha
AU  - Ayesha Banu
AU  - Fatema-Tuj-Johora
AU  - Riad Hossain
PY  - 2026
DA  - 2026/06/08
TI  - Binary-Class AI-Generated Content Detection Through Comprehensive Feature Engineering
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 1079
EP  - 1095
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_74
DO  - 10.2991/978-94-6239-664-7_74
ID  - Tisha2026
ER  -