Decriminalisation of adultery: A socio legal analysis
DOI:
https://doi.org/10.61841/be3azt56Keywords:
Adultery, adulterium, adultererAbstract
The current status of women in Indian was not as same during Pre-independence period also it can not deny that women have always been the victim of offences in society, Keeping in mind the adultery is one of such offence provided under section 497 of IPC which women can not be held responsible for guilty though she may seducer of offence. Second Women should not be treated as commodity or tool of the men to be used as and when required and the husband can not be a master of his wife as husband will give the consent in the matter of adultery. By taking on account of these facts the section 497 was itself a gender discrimination which is legally and ethically wrong. In other hand the present judgment of Supreme Court is not satisfactory in response to decriminalization of the offence instead of making gender neutral provision.
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References
1. H. He and E. A. Garcia, “Learning fromimbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009.
2. A. Estabrooks, T. Jo, and N. Japkowicz, “Amultiple resampling method for learning fromimbalanced data sets,” Computational Intelligence, vol. 20, no. 1, pp. 18–36, 2004.
3. H. Han, W.-Y. Wang, and B.-H. Mao, “Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning,” in Advances in Intelligent Computing, pp. 878–887, Springer, 2005.
4. N. V. Chawla, N. Japkowicz, and A. Kotcz, “Editorial: special issue on learning from imbalanced data sets,” ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 1–6, 2004.
5. U. Bhowan, M. Johnston, and M. Zhang, “Developing new fitness functions in genetic programming for classification with unbalanced data,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42,no. 2, pp.406–421, 2012.
6. J.-H. Xue and P. Hall, “Why does rebalancing class-unbalanced data improve AUC for linear discriminant analysis?” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 5, pp. 1109– 1112, 2015.
7. R. Batuwita and V. Palade, “Class imbalance learning methods for support vector machines,” in Imbalanced Learning: Foundations, Algorithms, and Applications, pp. 83–99, John Wiley & Sons, Berlin, Germany, 2013.
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