Download pdf
Enhancing plagiarism detection methodology with the DQN algorithm on an improved differential evolution foundation
- 1 Hebei University of Engineering
- 2 University of Leeds
* Author to whom correspondence should be addressed.
Abstract
Plagiarism detection has become increasingly crucial in real-world applications, demanding precise identification of content similarity. This paper introduces a novel plagiarism detection approach. Building upon LSTM as the foundation, it employs an enhanced DE (Differential Evolution) algorithm and reinforces learning with the DQN algorithm for sample classification and training. Throughout the training process, gradual parameter adjustments are made with the aim of improving the model's efficiency and accuracy.
Keywords
LSTM, differential evolution, plagiarism detection, DQN
[1]. Hu, Runqiao, Shaofan Wang, and Ke Li. "Visual Active Tracking Algorithm for UAV Cluster Based on Deep Reinforcement Learning." Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). Singapore: Springer Nature Singapore, 2023.
[2]. Hochreiter, S. (1997). Long Short-Term Memory. Technical Report, Institute for Computer Science, Austrian Federal Institute of Technology (ETH), Zurich, Switzerland
[3]. Larry R Medsker and LC Jain. Recurrent neural networks. Design and Applications, 5:64–67, 2001.
[4]. Navin Kumar Manaswi. RNN and LSTM. In Deep Learning with Applications Using Python, pages 115– 126. Springer, 2018.
[5]. Sepp Hochreiter and Jurgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735– 1780, 1997.
[6]. Storn, R., & Price, K. M. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.https://doi.org/10.1023/a:1008202821328
[7]. Swagatam Das and Amit Konar. Automatic image pixel clustering with an improved differential evolution. Applied Soft Computing, 9(1):226–236, 2009.
[8]. Iztok Fister, Dusan Fister, Suash Deb, Uros Mlakar, and Janez Brest. Posthoc analysis of sport performance with difffferential evolution. Neural Computing and Applications, 32(15):10799– 10808, 2020.
[9]. Li, J., Monroe, W. S., Ritter, A., Jurafsky, D., Galley, M., & Gao, J. (2016). Deep Reinforcement Learning for Dialogue Generation. https://doi.org/10.18653/v1/d16- 1127
[10]. Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state- of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 4-31.
[11]. Vesterstrom, J., & Thomsen, R. (2004). A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. Proceedings of the 2004 Congress on Evolutionary Computation, 1980- 1987.
[12]. Tasgetiren, M. F., Sevkli, M., Liang, Y. C., & Gencyilmaz, G. (2004). Particle swarm optimization algorithm for single machine total weighted tardiness problem. Proceedings of the 2004 Congress on Evolutionary Computation, 1412- 1419.
[13]. Zhang, J., & Sanderson,A. C. (2009). JADE: Adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation, 13(5), 945-958.
[14]. Qin,A. K., Huang, V. L., & Suganthan, P. N. (2008). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation, 13(2), 398-417.
[15]. Liu, J., & Lampinen, J. (2005). A fuzzy adaptive differential evolution algorithm. Soft Computing, 9(6), 448-462.
[16]. Porter, M. F. (1997). An algorithm for suffix stripping. Program: Electronic Library and Information Systems, 14(3), 130– 137.https://doi.org/10.1108/eb046814
[17]. Word2Vec Tutorial - The Skip-Gram Model · Chris McCormick. (2016, April 19).http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model
[18]. Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global Vectors for Word Representation.https://doi.org/10.3115/v1/d14-1162
[19]. Hessel, Matteo, et al. "Rainbow: Combining improvements in deep reinforcement learning." Proceedings of the AAAI conference on artificial intelligence. Vol. 32. No. 1. 2018.
[20]. Gokcesu, Kaan, and Hakan Gokcesu. "Generalized huber loss for robust learning and its efficient minimization for a robust statistics." arXiv preprint arXiv:2108.12627 (2021).
Cite this article
Feng,Y.;Guo,P. (2024). Enhancing plagiarism detection methodology with the DQN algorithm on an improved differential evolution foundation. Applied and Computational Engineering,44,193-201.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).