Judicial Review: A Critical Study
DOI:
https://doi.org/10.61841/2y780h56Keywords:
DEMOCRACY, JUDICIAL REVIEW, NATURAL JUSTICE, WRITS.Abstract
The current research paper discusses the concept of judicial review and how it came to be originated with the case of Marbury v. Madison back in 1803. The paper also elucidates the various principles of natural justice before it talks about the various remedies that lie under the review such as certiorari, prohibition, habeas corpus, mandamus and quo warranto. The paper concludes with discussing the significance that judicial review holds in a democracy where the power can often go unfettered.
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References
[1] S. Gollapudi and S. Gollapudi, “Deep Learning for Computer Vision,” in Learn Computer Vision Using
OpenCV, 2019.
[2] G. Wang et al., “Interactive Medical Image Segmentation Using Deep Learning with Image-Specific Fine
Tuning,” IEEE Trans. Med. Imaging, 2018.
[3] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the
IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016.
[4] G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis. 2017.
[5] Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A
review,” Neurocomputing, 2016.
[6] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd
International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015.
[7] A. Janowczyk and A. Madabhushi, “Deep learning for digital pathology image analysis: A comprehensive
tutorial with selected use cases,” J. Pathol. Inform., 2016.
[8] J. Ker, L. Wang, J. Rao, and T. Lim, “Deep Learning Applications in Medical Image Analysis,” IEEE Access,
2017.
[9] B. Zhao, J. Feng, X. Wu, and S. Yan, “A survey on deep learning-based fine-grained object classification and
semantic segmentation,” International Journal of Automation and Computing. 2017.
[10] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015.
[11] K. Suzuki, “Overview of deep learning in medical imaging,” Radiological Physics and Technology. 2017.
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