Abstract
Plagiarism in colleges is a significant issue, staying as a point for logical works for a considerable length of time. We can watch plagiarism happen in different fields like writing, scholastic, science, and music inconceivably. It very well may be likewise conceivable that one day we will get our task work in another production without legitimate reference. Plagiarism discovery systems are there, which are ordered into character-based strategy, basic based technique, characterization or group-based strategy, cross language-based methods, citation-based methods, semantic-based methods, and syntax-based methods. Different devices are accessible utilizing the above plagiarism strategies. Our tests show the viability of “deep features” in the undertaking of grouping task program entries as copy, partial-copy, and non-copy by bunching systems. Here, we have created a database containing sets of synonyms in the tabular form; it covers a variety of words containing a total of 100,000 words. This dataset helps to create an instantaneous feature for the specific dataset.
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Notes
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Database containing sets of synonyms in tabular form, having a total of 100,000 words.
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PAN is a series of scientific events and shared tasks on digital text forensics and stylometry.
References
Blum SD (2011) My word! Plagiarism and college culture. Cornell University Press
Kalleberg RB (2015) Towards detecting textual plagiarism using machine learning methods (Master’s thesis, Universitetet i Agder; University of Agder)
Bengio Y, Courville A, Vincent P (2013) Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828
Niwattanakul S, Singthongchai J, Naenudorn E, Wanapu S (2013) Using of Jaccard coefficient for keywords similarity. Proc Int Multi-Conf Eng Comput Sci 1(6):380–384
Nivre J (2005) Dependency grammar and dependency parsing. MSI Rep 5133(1959):1–32
Younes N, Reips UD (2019) Guideline for improving the reliability of Google Ngram studies: Evidence from religious terms. PloS One 14(3)
Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys D Nonlinear Phenom 132306
Jaderberg M, Vedaldi A, Zisserman A (2014) Deep features for text spotting. In: European conference on computer vision. Springer, Cham, pp 512–528
Sokolova M, Japkowicz N, Szpakowicz S (2006) Beyond accuracy, Fscore and ROC: a family of discriminant measures for performance evaluation. In: Australasian joint conference on artificial intelligence. Springer, Berlin, Heidelberg, pp 1015–1021
Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. arXiv:1503.00075
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Muttumana, A.V., Goel, H., Teotia, Y., Bhardwaj, P. (2021). Plagiarism Detection Using Deep Based Feature Combined with SynmDict. In: Abraham, A., Castillo, O., Virmani, D. (eds) Proceedings of 3rd International Conference on Computing Informatics and Networks. Lecture Notes in Networks and Systems, vol 167. Springer, Singapore. https://doi.org/10.1007/978-981-15-9712-1_5
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