An LSTM-Based Plagiarism Detection via Attention Mechanism and a Population-Based Approach for Pre-training Parameters with Imbalanced Classes

Moravvej, S. V.Department of Computer Engineering, Isfahan University of Technology, Isfahan, Iran (author)

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.

Mousavirad, S. J.Department of Computer Engineering, Hakim Sabzevari Univesity, Sabzevar, Iran (author)
Helali Moghadam, MahshidRISE,Industriella system,Mälardalen University, Sweden,RISE Research Institutes of Sweden, Västerås, Sweden(Swepub:ri)mahshid.helali.moghadam@ri.se (author)
Saadatmand, Mehrdad,1980-RISE,Industriella system,RISE Research Institutes of Sweden, Västerås, Sweden(Swepub:ri)MehrdadSaa@ri.se (author)
Department of Computer Engineering, Isfahan University of Technology, Isfahan, IranDepartment of Computer Engineering, Hakim Sabzevari Univesity, Sabzevar, Iran (creator_code:org_t)

In:Lect. Notes Comput. Sci.Cham : Springer Science and Business Media Deutschland GmbH, s. 690-7019783030922375

By the author/editor: Moravvej, S. V.; Mousavirad, S. J ...; Helali Moghadam, ...; Saadatmand, Mehr ...

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