Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text

Osman, Ahmed Hamza; Aljahdali, Hani Moaiteq

doi:10.3390/math10234613

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Osman et al [25] suggested Plagiarism is a high kind of academic rebellion that undermines the entire academic enterprise. In the past few years, several initiatives have been made to detect duplication in text documents.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Plagiarism Detection Through Advanced Natural Language Processing and E-BERT Framework of the Smith-Waterman Algorithm

Antonius,

Orosoo,

et al. 2023

IJACSA

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Effective detection has been extremely difficult due to plagiarism's pervasiveness throughout a variety of fields, including academia and research. Increasingly complex plagiarism detection strategies are being used by people, making traditional approaches ineffective. The assessment of plagiarism involves a comprehensive examination encompassing syntactic, lexical, semantic, and structural facets. In contrast to traditional string-matching techniques, this investigation adopts a sophisticated Natural Language Processing (NLP) framework. The preprocessing phase entails a series of intricate steps ultimately refining the raw text data. The crux of this methodology lies in the integration of two distinct metrics within the Encoder Representation from Transformers (E-BERT) approach, effectively facilitating a granular exploration of textual similarity. Within the realm of NLP, the amalgamation of Deep and Shallow approaches serves as a lens to delve into the intricate nuances of the text, uncovering underlying layers of meaning. The discerning outcomes of this research unveil the remarkable proficiency of Deep NLP in promptly identifying substantial revisions. Integral to this innovation is the novel utilization of the Waterman algorithm and an English-Spanish dictionary, which contribute to the selection of optimal attributes. Comparative evaluations against alternative models employing distinct encoding methodologies, along with logistic regression as a classifier underscore the potency of the proposed implementation. The culmination of extensive experimentation substantiates the system's prowess, boasting an impressive 99.5% accuracy rate in extracting instances of plagiarism. This research serves as a pivotal advancement in the domain of plagiarism detection, ushering in effective and sophisticated methods to combat the growing spectre of unoriginal content.

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Section: Related Workmentioning

confidence: 99%

Enhanced Plagiarism Detection Through Advanced Natural Language Processing and E-BERT Framework of the Smith-Waterman Algorithm

Antonius,

Orosoo,

et al. 2023

IJACSA

View full text Add to dashboard Cite

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Effective privacy preserving model based on adversarial CNN with IBOA in the social IoT systems for CEC

Shukla,

Pandit,

Gandhi

et al. 2024

Int J Communication

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SummaryIn the present scenario, the social Internet of Things (IoT) is one of the emerging technologies in combination with collaborative edge computing (CEC). The CEC solves the issue of storage and computing with the help of deep learning models that make full usage of the edge‐computing abilities. The robustness of the deep learning models is ineffective, and the edge devices in the CEC are threatened by the malicious attacks. Therefore, a new data protection framework is proposed in this manuscript to avoid the security crisis and privacy leakage of CEC. A new adversarial sample generation model is introduced in this manuscript on the basis of intensive butterfly optimization algorithm (IBOA) that effectively reduces the time complexity of the framework to linear. Here, IBOA is chosen because an extra intensive exploitation step is included that guides the proposed framework to converge quickly towards global optimum and to avoid local optima trap. Then, the adversarial training concept is incorporated with the convolutional neural network (CNN) model for sentence similarity analysis. On the other hand, the anti‐dropout layers are combined with the adversarial CNN model to reduce the overfitting concern. The proposed framework, adversarial CNN with IBOA, obtained higher results with f1‐score of 88.70%, accuracy of 89.40%, correct rate of 88.95%, recall of 89.48%, and precision of 89.41% on the Microsoft Research Paraphrase Corpus (MSRP) dataset. The obtained results are superiorly better than the conventional deep learning models.

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Applied Computing and Artificial Intelligence

Zhang

2023

Mathematics

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Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text

Cited by 3 publications

References 47 publications

Enhanced Plagiarism Detection Through Advanced Natural Language Processing and E-BERT Framework of the Smith-Waterman Algorithm

Enhanced Plagiarism Detection Through Advanced Natural Language Processing and E-BERT Framework of the Smith-Waterman Algorithm

Effective privacy preserving model based on adversarial CNN with IBOA in the social IoT systems for CEC

Applied Computing and Artificial Intelligence

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