Review of Recent Plagiarism Detection Techniques and Their Performance Comparison

Kaur, Manpreet; Gupta, Vishal; Kaur, Ravreet

doi:10.1007/978-981-15-7234-0_13

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…Collaboration among countries in the field of DT technology for education shows great promise in transforming teaching and learning practices, improving educational outcomes, and fostering innovation in the global education sector. In education, DT requires the creation of virtual replicas of learning environments, curriculum materials, and student interactions, enabling personalized, immersive, and interactive educational experiences [42]. When countries collaborate on digital twin initiatives in education, they can leverage their respective strengths, resources, and educational practices to address common challenges and promote inclusive, equitable, and quality education for all.…”

Section: Country Collaboration Analysis Of Digital Twin For Educationmentioning

confidence: 99%

“…The major citing article in this cluster is authored by De, benedictis A (2023.0-JAN) and is titled "Digital twins in healthcare: an architectural proposal and its application in a social distancing case study" published in IEEE Journal of Biomedical and Health Informatics, V27, P11. The most cited topics within this cluster include "human" (68), "article" (46), and "data acquisition" (42). The fifth largest cluster #4 comprises 50 members with a silhouette value of 0.682 and is labeled as "digital twin" by LLR.…”

Section: Keyword Analysis Of Digital Twin For Educationmentioning

confidence: 99%

“…The major citing article of the cluster is authored by Wang, Z (2023.0-JAN) and is titled "Self-attentive local aggregation learning with prototype guided regularization for point cloud semantic segmentation of high-speed railways" published in IEEE Transactions on Intelligent Transportation Systems, V24, P13. The most cited topics within this cluster are "deep learning" (396), "deep neural networks" (42), and "semantics" (41). The sixth largest cluster #5 consists of 18 members with a silhouette value of 0.986 and is labeled as "data science" by LLR.…”

Section: Keyword Analysis Of Digital Twin For Educationmentioning

confidence: 99%

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Scientometric Insights of Digital Twins in Industry 4.0

Kaur,

Bhatia

2024

Preprint

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Industry 4.0 is a new era of digitization that integrates digital technologies to create smart, interconnected production systems, marking a paradigm shift in different domains. One of the key technologies in this era is 'Digital Twin', which offers virtual representations of physical assets and processes. It is crucial to analyze the role of digital twin technology in the context of Industry 4.0 using scientometric analysis with citeSpace, a powerful tool for visualizing and analyzing citation networks. This research focuses on the growth trajectory of digital twin literature, identifies influential authors, institutions, and journals, and analyzes the geographical distribution of research contributions from the Scopus database for 2018-2024. The findings reveal insights into the current state of digital twin research, its impact on Industry 4.0, and opportunities for future exploration and collaboration in this rapidly evolving field.

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Section: Country Collaboration Analysis Of Digital Twin For Educationmentioning

confidence: 99%

Section: Keyword Analysis Of Digital Twin For Educationmentioning

confidence: 99%

Section: Keyword Analysis Of Digital Twin For Educationmentioning

confidence: 99%

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Scientometric Insights of Digital Twins in Industry 4.0

Kaur,

Bhatia

2024

Preprint

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“…One potential problem with the adaptable layout described above is that WordNet’s’ meaning and scope might quickly diverge from one another. We cannot be sure that we will be encoding the same relationships or that we will be covering the same conceptual ground [ 34 , 35 ].…”

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

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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.

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“…Since the beginning of the 21st century, the use of computer programmes to detect academic plagiarism in students' work has been spreading, and a large number are available (Foltýnek et al, 2020;Kaur et al, 2021;Naik et al, 2015). These programmes have been useful in reducing the incidence of plagiarism in universities (Ledwith and Rísquez, 2008), but numerous studies have also highlighted their limitations.…”

Section: Technological Mechanisms For Detecting Plagiarism and Identi...mentioning

confidence: 99%

Academic Integrity Policies Against Assessment Fraud in Postgraduate Studies: An Analysis of the Situation in Spanish Universities

et al. 2021

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Review of Recent Plagiarism Detection Techniques and Their Performance Comparison

Cited by 6 publications

References 21 publications

Scientometric Insights of Digital Twins in Industry 4.0

Scientometric Insights of Digital Twins in Industry 4.0

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

Academic Integrity Policies Against Assessment Fraud in Postgraduate Studies: An Analysis of the Situation in Spanish Universities

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