Cite
Vulnerability detection in Java source code using a quantum convolutional neural network with self-attentive pooling, deep sequence, and graph-based hybrid feature extraction.
MLA
Hussain, Shumaila, et al. “Vulnerability Detection in Java Source Code Using a Quantum Convolutional Neural Network with Self-Attentive Pooling, Deep Sequence, and Graph-Based Hybrid Feature Extraction.” Scientific Reports, vol. 14, Mar. 2024, pp. 1–17. EBSCOhost, https://doi.org/10.1038/s41598-024-56871-z.
APA
Hussain, S., Nadeem, M., Baber, J., Hamdi, M., Rajab, A., Al Reshan, M. S., & Shaikh, A. (2024). Vulnerability detection in Java source code using a quantum convolutional neural network with self-attentive pooling, deep sequence, and graph-based hybrid feature extraction. Scientific Reports, 14, 1–17. https://doi.org/10.1038/s41598-024-56871-z
Chicago
Hussain, Shumaila, Muhammad Nadeem, Junaid Baber, Mohammed Hamdi, Adel Rajab, Mana Saleh Al Reshan, and Asadullah Shaikh. 2024. “Vulnerability Detection in Java Source Code Using a Quantum Convolutional Neural Network with Self-Attentive Pooling, Deep Sequence, and Graph-Based Hybrid Feature Extraction.” Scientific Reports 14 (March): 1–17. doi:10.1038/s41598-024-56871-z.