1. A bibliometric review and analysis of data-driven fault detection and diagnosis methods for process systems;Alauddin;Industrial & Engineering Chemistry Research,2018
2. A systematic review on supervised and unsupervised machine learning algorithms for data science;Alloghani;Supervised and Unsupervised Learning for Data Science,2020
3. Brody, S., Alon, U., and Yahav, E. (2021). How attentive are graph attention networks? arXiv preprint arXiv:2105.14491.
4. Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
5. Deep residual learning for image recognition;He;In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016