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Thaweesak Yingthawornsuk. Intelligent flagged content detection with transformer-based models for secure online environments. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2025.
Intelligent flagged content detection with transformer-based models for secure online environments
Name: Balaji, R. S. Lakshmi
Organization :
Academy of Maritime Education and Training University (AMET). Department of Advanced Computing Sciences
Email :
rslbalaji@protonmail.com
Name: Abirami, G.
Organization :
Academy of Maritime Education and Training University (AMET). Computer Science and Engineering
Email :
gabirami08@gmail.com
Name: Sirimonpak Suwannakhun
Organization :
King Mongkut's University of Technology Thonburi. Faculty of Industrial Education and Technology
Email :
sirimonpak.sut@kmutt.ac.th
Organization :
Govt of India. National Cadet Corps
Email :
cstswamy@proton.me
Name: Thaweesak Yingthawornsuk
Organization :
King Mongkut's University of Technology Thonburi. Department of Media Technology
Email :
thaweesak.yin@kmutt.ac.th
keyword:
Content filtering (Internet)
LCSH:
Online social networks
-- Security measures
LCSH:
Artificial intelligence
-- Data processing
LCSH:
Computer security
Abstract:
The increasing volume of user-generated content
on global online platforms has made it very crucial to detect and
moderate harmful content like criminal activities, illegal
substances, extremist codewords, and other prohibited
materials across multiple languages. In this study, we present an
intelligent system for detecting flagged content using advanced
transformer-based models such as BERT, RoBERTa, XLNet,
and T5. These models have been further trained to very
accurately and effectively detect from illegal activities to
extreme language and drug related content. Our method
surpasses previous methods of content moderation achieving
great improvements in classification accuracy, precision, recall
and F1-score. Furthermore, the implemented system enables
offline or online real-time process and therefore large-scale
throughput. It also gives a detailed overview of the architectural
designs of the model, training processes and other problems
faced during the model development stage. The results illustrate
the power of transformer-based models in offering a robust
strategy for automated detection of flagged content across
languages to help in cultivating better online safety. This
research also contributes to SDG 16 by enhancing online
security and SDG 10 by promoting fairer digital spaces. The
study concludes with a discussion on potential applications in
social media moderation, law enforcement, and global security
agencies and future research aimed at improving detection
capabilities
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2025
Modified:
2025-06-24
Issued:
2025-06-24
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering Academic Association (Thailand) and King Mongkut's University of Technology North Bangkok. Department of Electrical and Computer Engineering. 13th International Electrical Engineering Congress (iEECON 2025) (P06427). Bangkok : Electrical Engineering Academic Association (Thailand), 2025
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.021249 วินาที
Balaji, R. S. Lakshmi
| Title | Contributor | Type |
|---|---|---|
| Intelligent flagged content detection with transformer-based models for secure online environments
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, R. S. Lakshmi;Abirami, G.;Sirimonpak Suwannakhun;Thiruvenkataswamy, C . S.;Thaweesak Yingthawornsuk | บทความ/Article |
Abirami, G.
| Title | Contributor | Type |
|---|---|---|
| Intelligent flagged content detection with transformer-based models for secure online environments
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, R. S. Lakshmi;Abirami, G.;Sirimonpak Suwannakhun;Thiruvenkataswamy, C . S.;Thaweesak Yingthawornsuk | บทความ/Article |
Sirimonpak Suwannakhun
| Title | Contributor | Type |
|---|---|---|
| Deep learning-based predictive modeling for male depression detection
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, R. S. Lakshmi;Sumetee Jirapattarasakul;Kantapat Kwansomkid;Sirimonpak Suwannakhun;Thaweesak Yingthawornsuk | บทความ/Article | |
| Intelligent flagged content detection with transformer-based models for secure online environments
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, R. S. Lakshmi;Abirami, G.;Sirimonpak Suwannakhun;Thiruvenkataswamy, C . S.;Thaweesak Yingthawornsuk | บทความ/Article | |
| A study of deep learning models for identifying and estimating psychological stress and disorders using electroencephalogram signals
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, Lakshmi R. S.;Batumalay, M.;Thaweesak Yingthawornsuk;Avanthika Swamy;Sirimonpak Suwannakhun | บทความ/Article |
Thiruvenkataswamy, C . S.
| Title | Contributor | Type |
|---|---|---|
| Intelligent flagged content detection with transformer-based models for secure online environments
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, R. S. Lakshmi;Abirami, G.;Sirimonpak Suwannakhun;Thiruvenkataswamy, C . S.;Thaweesak Yingthawornsuk | บทความ/Article |
Thaweesak Yingthawornsuk
| Title | Contributor | Type |
|---|---|---|
| Deep learning-based predictive modeling for male depression detection
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, R. S. Lakshmi;Sumetee Jirapattarasakul;Kantapat Kwansomkid;Sirimonpak Suwannakhun;Thaweesak Yingthawornsuk | บทความ/Article | |
| Empirical evaluation of machine learning techniques for autism spectrum disorder
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| Sleep stages classification with multi-modal signals using deep learning
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| Intelligent flagged content detection with transformer-based models for secure online environments
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Balaji, R. S. Lakshmi;Abirami, G.;Sirimonpak Suwannakhun;Thiruvenkataswamy, C . S.;Thaweesak Yingthawornsuk | บทความ/Article | |
| A study of deep learning models for identifying and estimating psychological stress and disorders using electroencephalogram signals
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