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

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

Mousavirad, S. J. (författare): Department of Computer Engineering, Hakim Sabzevari Univesity, Sabzevar, Iran

Helali Moghadam, Mahshid (författare): RISE,Industriella system,Mälardalen University, Sweden,RISE Research Institutes of Sweden, Västerås, Sweden

Saadatmand, Mehrdad, 1980- (författare): RISE,Industriella system,RISE Research Institutes of Sweden, Västerås, Sweden

(creator_code:org_t)

2021-12-05
2021
Engelska.
Ingår i: Lect. Notes Comput. Sci.. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030922375 ; , s. 690-701

Relaterad länk:: https://doi.org/10.1...; visa fler...; http://arxiv.org/pdf...; https://urn.kb.se/re...; https://doi.org/10.1...; https://urn.kb.se/re...; visa färre...

Abstract Ämnesord

Stäng

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.

TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Av författaren/redakt...: Moravvej, S. V.; Mousavirad, S. J ...; Helali Moghadam, ...; Saadatmand, Mehr ...

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Maskinteknik; och Energiteknik

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.