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Cited by 28Academic integrity and plagiarismTopic ModelingSoftware Engineering Research

Reliable plagiarism detection system based on deep learning approaches

Open access
El-Rashidy, Mohamed A., Mohamed, Ramy G., El-Fishawy, Nawal A., Shouman, Marwa A.·2022·Neural Computing and Applications
DOI 10.1007/s00521-022-07486-w
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Abstract

Abstract The phenomenon of scientific burglary has seen a significant increase recently due to the technological development in software. Therefore, many types of research have been developed to address this phenomenon. However, detecting lexical, syntactic, and semantic text plagiarism remains to be a challenge. Thus, in this study, we have computed and recorded all the features that reflect different types of text similarities in a new database. The created database is proposed for intelligent learning to solve text plagiarism detection problems. Using the created database, a reliable plagiarism detection system is also proposed, which depends on intelligent deep learning. Different approaches to deep learning, such as convolution and recurrent neural network architectures, were considered during the construction of this system. A comparative study was implemented to evaluate the proposed intelligent system on the two benchmark datasets: PAN 2013 and PAN 2014 of the PAN Workshop series. The experimental results showed that the proposed system based on long short-term memory (LSTM) achieved the first rank compared to up-to-date ranking systems.

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  • Deep learning models such as different variants of convolutional and recurrent neural network architectures have also been used for plagiarism detection.

    “More recently, deep learning models such as different variants of convolutional and recurrent neural network architectures have also been used for plagiarism detection (El Mostafa Hambi, 2020; El-Rashidy et al., 2022).”

    From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as El-Rashidy et al., 2022

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Research works printed but not linked: 26

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Authors
El-Rashidy, M. A., Mohamed, R. G., El-Fishawy, N. A., & Shouman, M. A.
Affiliations
Menoufia University (EG)
Corresponding author
El-Rashidy, Mohamed A.
ORCID
Venue
Neural Computing and Applications
Publisher
Springer Science and Business Media LLC
Published
2022-06-24
Volume
34
Issue
21
Pages
18837-18858
ISSN
  • 0941-0643 (ISSN-L)
  • 1433-3058
DOI
10.1007/s00521-022-07486-w
OpenAlex
W4283381237
Cited by
28
References
22
Citations by year
2026: 7 · 2025: 13 · 2024: 3 · 2023: 5
FWCI
6.08
Type
journal article
Language
en
Open access
Licence
Locations
Neural Computing and Applications (publishedVersion, cc-by)
Funders
Minufiya University
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
Keywords
text plagiarism detection, convolutional neural networks, recurrent neural networks, long short-term memory

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