Reliable plagiarism detection system based on deep learning approaches

El-Rashidy, Mohamed A.; Mohamed, Ramy G.; El-Fishawy, Nawal; Shouman, Marwa A.

doi:10.1007/s00521-022-07486-w

Cited by 7 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Foltỳnek et al ( 2019) also provides a comprehensive summary of how classical ML algorithms such as tree-based methods, SVMs and neural networks have been successfully used to combine more than one type of detection method to create the bestperforming meta-system. 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).…”

Section: Performance Assessment and Monitoringmentioning

confidence: 99%

Proactive and reactive engagement of artificial intelligence methods for education: a review

Mallik

Gangopadhyay

2023

Front. Artif. Intell.

View full text Add to dashboard Cite

The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety—from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003–2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education.

show abstract

Section: Performance Assessment and Monitoringmentioning

confidence: 99%

Proactive and reactive engagement of artificial intelligence methods for education: a review

Mallik

Gangopadhyay

2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…In Ref. [ 36 ], the authors created a new database that contains all the characteristics that indicate various linguistic similarities. As a solution to textual plagiarism issues, the developed database is offered for use in intelligent learning.…”

Section: The State Of the Artmentioning

confidence: 99%

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

Darwish,

Mhaimeed,

Elzoghabi

2023

Entropy

View full text Add to dashboard Cite

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document’s idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document’s main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model’s ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10–15% for each increase in the number of samples in the dataset.

show abstract

“…This paper presents a systematic review of the work done in the field of plagiarism detection systems (PDS) performance indicators and in order to get a broad view, various papers and journals have been searched and selected the publications that related to this study within the time span 2013 to 2023. After selecting the publications related to the study within this period, 30 articles have been found that very closely to the plagiarism systems performance indicators, The search strings that Table (2) are found among academic databases to locate articles having these strings in their abstracts, titles, and keywords. Accordingly, famous online academic databases like ACM, IEEE, Science Direct, Springer, Google Scholar, Taylor & Francis, and Wiley are used.…”

Section: Selected Primary Studiesmentioning

confidence: 99%

“…The overall objective is defined in these research questions. The indicators of evaluating the performance of (PDS) were presented in Table 1, these indicators were collected after a comprehensive study of the previous researches stated in table (2), that presents a group of previous research specialized in evaluating the performance of (PDS) within the period from 2013 to 2023 where the metrics used in each research were determined .…”

Section: Research Questionsmentioning

confidence: 99%

“…These suicide systems will not have the same degree of performance and efficiency. If the plagiarism system performs poorly, the researcher simply leaves this system and uses another plagiarism system, and there is no chance of bringing this researcher back to using a poorly performing system again [2] - [6]. Therefore, in order to improve the performance and efficiency of these systems, it is important to design impersonation systems with some new characteristics, such as: Accessibility, Accuracy of Results, Clarity Report, Diversity Of Text Format, Download Time, Expandability, Open Source, Plagiarism Security, Supported Languages, and Validation Time [7] - [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Evaluation of the Plagiarism Systems A Systematic Review

Qazan,

Mustafa Al- Mashhadany

2023

ijeeic

View full text Add to dashboard Cite

Paraphrase plagiarism occurs when text is deliberately obfuscated to evade detection, deliberate alteration increases the complexity of plagiarism and the difficulty in detecting paraphrase plagiarism., Plagiarism systems play an important role in educational and research institutions. In the field of education, the use of these systems has increased in universities to help researchers select new research, which is why it has been important to ensure that universities have effective citation screening systems. Different metrics were used to evaluate the performance of these systems. All of these metrics affect the performance and usability of the impersonation system. This paper present a systematic literature review (SLR) to obtain an overview of the existing studies of distinct criteria for evaluating the performance of textual plagiarism systems and each criterion is briefly explained to give the basic idea behind it. Overall, the aim of this paper is to provide a modern introduction and short review of these criteria. A systematic literature review is performed according to the guidelines for conducting SLR in software engineering. The study relied on a comprehensive set of research papers collected from electronic libraries published during a time period from 2013 to 2023. The results of this SLR include information about metrics for evaluating the performance of plagiarism systems according to previous research. During a specific period it can assist researchers in this field by providing an overview of current researches in this field. Moreover, it may serve as a first step towards a great SLR-assisted explanation of the subject.

show abstract

Reliable plagiarism detection system based on deep learning approaches

Cited by 7 publications

References 23 publications

Proactive and reactive engagement of artificial intelligence methods for education: a review

Proactive and reactive engagement of artificial intelligence methods for education: a review

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

Performance Evaluation of the Plagiarism Systems A Systematic Review

Contact Info

Product

Resources

About