[1] M. R. K. H. Saberi and M. R. Hasani Ahangar, “Providing an Agent-Based Architecture for Semantic Mining From Large-Scale Data in Distributed Environments,” Journal of Electronical & Cyber Defence, vol. 8, no. 3, 2020.##
[2] S. Krig, “Feature learning and deep learning architecture survey,” in Computer Vision Metrics: Springer, pp. 375-514. 2016.##
[3] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, MIT press, 2016.##
[4] S. P. M. Zakeri Nasrabadi1, “Automatic Test Data Generation in File Format Fuzzers,” Journal of Electronical & Cyber Defence, vol. 8, no. 29, 2020.##
[5] D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, “Flexible, high performance convolutional neural networks for image classification,” in Twenty-Second International Joint Conference on Artificial Intelligence, 2011.##
[8] Y. Sun, A. K. Wong, and M. S. Kamel, “Classification of imbalanced data: A review,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 23, no. 04, pp. 687-719,2009.##
[9] M. Buda, A. Maki, and M. A. Mazurowski, “A systematic study of the class imbalance problem in convolutional neural networks,” Neural Networks, vol. 106, pp. 249-259, 2018.##
[10] J. v. Neumann, “Zur theorie der gesellschaftsspiele,” Mathematische annalen, vol. 100, no. 1, pp. 295-320, 1928.##
[11] A. W. Tucker and R. D. Luce, Contributions to the Theory of Games (no. 40), Princeton University Press, 1959.##
[12] A. A. M. Forooghy and M. Bagheri, “A Decision-Making Model in a Cyber Conflicts Acted Upon Vulnerability, Based on Game Theoretic Analysis,” Journal of Electronical & Cyber Defence, vol. 6, no. 22, 2018.##
[13] I. Goodfellow et al., “Generative adversarial nets,” in Advances in neural information processing systems, pp. 2672-2680, 2014.##
[14] E. L. Denton, S. Chintala, and R. Fergus, “Deep generative image models using a laplacian pyramid of adversarial networks,” in Advances in neural information processing systems, pp. 1486-1494, 2015.##
[15] I. Goodfellow, “NIPS 2016 tutorial: Generative adversarial networks,” arXiv preprint arXiv:1701.00160, 2016. [Online]. Available:
http://arxiv.org/abs/1701.00160.##
[16] A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434, 2015.##
[17] M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv preprint arXiv:1411.1784, 2014.##
[18] J. An and S. Cho, “Variational autoencoder based anomaly detection using reconstruction probability,” pecial Lecture on IE, vol. 2, no. 1, 2015.##
[19] S. Nowozin, B. Cseke, and R. Tomioka, “f-gan: Training generative neural samplers using variational divergence minimization,” in Advances in neural information processing systems, pp. 271-279, 2016.##
[20] A. Kadurin, S. Nikolenko, K. Khrabrov, A. Aliper, and A. Zhavoronkov, “druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico,” Molecular pharmaceutics, vol. 14, no. 9, pp. 3098-3104, 2017.##
[21] T. G. Dietterich, “Ensemble learning,” The handbook of brain theory and neural networks, vol. 2, pp. 110-125, 2002.##
[22] L. Torrey and J. Shavlik, “Transfer learning,” in Handbook of research on machine learning applications and trends: algorithms, methods, and techniques: IGI global, pp. 242-264, 2010.##
[23] F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size,” arXiv preprint arXiv:1602.07360, 2016.##
[24] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, Springer, pp. 234-241, 2015.##
[25] H. Qassim, A. Verma, and D. Feinzimer, “Compressed residual-VGG16 CNN model for big data places image recognition,” in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, pp. 169-175, 2018.##
[27] M. Abadi et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467, 2016.##
[29] Y. Bengio, “Practical recommendations for gradient-based training of deep architectures,” in Neural networks: Tricks of the trade, Springer, pp. 437-478, 2012.##
[31] D. Masters and C. Luschi, “Revisiting Small Batch Training for Deep Neural Networks. arXiv 2018,” arXiv preprint arXiv:1804.07612.##
[32] N. S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, and P. T. P. Tang, “On large-batch training for deep learning: Generalization gap and sharp minima,” arXiv preprint arXiv:1609.04836, 2016. [Online]. Available:
http://arxiv.org/abs/1609.04836.##
[33] B. Graham, “Kaggle diabetic retinopathy detection competition report,” University of Warwick, 2015.##
[34] M. Antony and S. Brüggemann, “Kaggle Diabetic Retinopathy Detection; Team o_O solution,” ed: Competition Report Github. url:
https://github. com/sveitser/kaggle_diabetic …, 2015.##
[35] S. Qummar et al., “A deep learning ensemble approach for diabetic retinopathy detection,” IEEE Access, vol. 7, pp. 150530-150539, 2019.##
[36] H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding, and Y. Zheng, “Convolutional neural networks for diabetic retinopathy,” Procedia Computer Science, vol. 90, pp. 200-205, 2016.##
)