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An LSTM-Based Plagiarism Detection via Attention Mechanism and a Population-Based Approach for Pre-training Parameters with Imbalanced Classes
Isfahan University of Technology, Iran.
Hakim Sabzevari Univesity, Iran.
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.ORCID iD: 0000-0003-3354-1463
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-1512-0844
2021 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Open AccessVolume 13110 LNCS, Pages 690-701, Springer Science and Business Media Deutschland GmbH , 2021, p. 690-701Conference paper, Published paper (Refereed)
Abstract [en]

Plagiarism is one of the leading problems in academic and industrial environments, which its goal is to find the similar items in a typical document or source code. This paper proposes an architecture based on a Long Short-Term Memory (LSTM) and attention mechanism called LSTM-AM-ABC boosted by a population-based approach for parameter initialization. Gradient-based optimization algorithms such as back-propagation (BP) are widely used in the literature for learning process in LSTM, attention mechanism, and feed-forward neural network, while they suffer from some problems such as getting stuck in local optima. To tackle this problem, population-based metaheuristic (PBMH) algorithms can be used. To this end, this paper employs a PBMH algorithm, artificial bee colony (ABC), to moderate the problem. Our proposed algorithm can find the initial values for model learning in all LSTM, attention mechanism, and feed-forward neural network, simultaneously. In other words, ABC algorithm finds a promising point for starting BP algorithm. For evaluation, we compare our proposed algorithm with both conventional and population-based methods. The results clearly show that the proposed method can provide competitive performance. 

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2021. p. 690-701
Keywords [en]
Artificial bee colony, Attention mechanism, Back-propagation, LSTM, Plagiarism, Feedforward neural networks, Intellectual property, Learning algorithms, Optimization, Academic environment, Attention mechanisms, Back Propagation, Feed forward neural net works, Imbalanced class, Industrial environments, Meta-heuristics algorithms, Plagiarism detection, Pre-training, Training parameters, Long short-term memory
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-57902DOI: 10.1007/978-3-030-92238-2_57Scopus ID: 2-s2.0-85121899875ISBN: 9783030922375 (print)OAI: oai:DiVA.org:ri-57902DiVA, id: diva2:1626084
Conference
28th International Conference on Neural Information Processing, ICONIP 2021Virtual, Online. 8 December 2021 through 12 December 2021
Available from: 2022-01-10 Created: 2022-01-10 Last updated: 2023-10-04Bibliographically approved

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Helali Moghadam, MahshidSaadatmand, Mehrdad

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