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An LSTM-Based Plagi...
An LSTM-Based Plagiarism Detection via Attention Mechanism and a Population-Based Approach for Pre-training Parameters with Imbalanced Classes
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- Moravvej, S. V. (author)
- Department of Computer Engineering, Isfahan University of Technology, Isfahan, Iran
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- Mousavirad, S. J. (author)
- Department of Computer Engineering, Hakim Sabzevari Univesity, Sabzevar, Iran
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- Helali Moghadam, Mahshid (author)
- RISE,Industriella system,Mälardalen University, Sweden,RISE Research Institutes of Sweden, Västerås, Sweden
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- Saadatmand, Mehrdad, 1980- (author)
- RISE,Industriella system,RISE Research Institutes of Sweden, Västerås, Sweden
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(creator_code:org_t)
- 2021-12-05
- 2021
- English.
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In: Lect. Notes Comput. Sci.. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030922375 ; , s. 690-701
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Abstract
Subject headings
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- 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.
Subject headings
- 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)
Keyword
- 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
Publication and Content Type
- ref (subject category)
- kon (subject category)
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