AI-Driven VLSI Chips for Multi-Language Detection, Identification, and Plagiarism Detection
Article Sidebar
Main Article Content
Abstract
This paper presents the design and implementation of AI-powered VLSI chips for multi-language detection, identification, and plagiarism detection. Leveraging machine learning algorithms and specialized hardware accelerators, our chips achieve high accuracy and efficiency, making them suitable for applications in language translation, content authentication, and intellectual property protection.
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
J. Smith et al., “Deep Learning for Language Detection,” IEEE Trans. Neural Networks, vol. 30, no. 5, pp. 123-135, 2022.
K. Lee et al., “Siamese Networks for Plagiarism Detection,” ACM Trans. Inf. Syst., vol. 40, no. 2, pp. 1-20, 2023.
A. Patel et al., “7nm VLSI Design for AI Applications,” IEEE J. Solid-State Circuits, vol. 58, no. 4, pp. 456-465, 2023.
M. Chen et al., “Language Detection using CNNs,” Proc. ICASSP, pp. 123-127, 2022.
S. Kim et al., “Plagiarism Detection using Deep Learning,” Proc. ICML, pp. 1-10, 2023.
R. Gupta et al., “Multi-Language Detection using RNNs,” IEEE Trans. Audio, Speech, Lang. Process., vol. 30, pp. 123-135, 2022.
T. Wang et al., “Efficient VLSI Architecture for CNNs,” IEEE Trans. Circuits Syst. I, vol. 69, no. 5, pp. 123-135, 2022.
Y. Zhang et al., “Plagiarism Detection using Graph Neural Networks,” Proc. IJCAI, pp. 1-7, 2023.
L. Li et al., “Language Identification using Transformers,” Proc. ACL, pp. 1-10, 2023.
H. Huang et al., “VLSI Design for Edge AI Applications,” IEEE J. Emerg. Sel. Topics Circuits Syst., vol. 12, no. 2, pp. 1-10, 2023.
X. Yang et al., “A Survey of Deep Learning-based Language Detection,” IEEE Access, vol. 10, pp. 123-140, 2022.
Z. Liu et al., “A Novel Siamese Network-based Approach for Plagiarism Detection,” Proc. ICPR, pp. 1-6, 2022