The Local Government System of Pakistan: Participation, Representation and Empowerment of Women

Politics is the field where women have been ignored since ancient times, due to the political notions determined by ideological beliefs and cultural norms. Among the United Nations’ measures remove gender inequalities, includes recommendation for reserving 30 per cent seats for women in legislatures. In Pakistan, this remarkable change was effected in 2000, as it reserved 33 percent seats for women at the local government and 17 per cent at the Provincial/National Assemblies and the Senate level, so about 40,000 women entered in local bodies throughout the country in 2000-01, and almost 55,000 women contested local government in 2005. Despite these developments, women are still facing challenges that deter the full realization of their roles as active interlocutors of policy-making. Keeping in view these aspects, this paper has analyzed the local government, focusing on women, exploring whether the increasing number of women in legislatures is merely a numerical strength, or is empowering them, too, Whether they are the ‘subjects of change’ or the ‘agents of change.’ It is calculated that administrative structures of local government is male-dominated, whereas women are trying to ensure their position, however, gendered barriers remain unbroken

Software Defect Prediction Via Machine Learning Classifiers

To improve software reliability, software should be developed without defects. SDP models can be employed to identify defective code sections at initial stage during the software development. When defects are ascertained early, it helps the practitioners to prioritize the efforts for testing and allocating more resources to defective modules. This results in improved software quality, reliability, and efficiency. Despite the fact that the numbers of approaches have been used in the past for SDP but most of them are not practically applicable. Manual feature selection is mostly performed by majority of feature selection methods. Core aim of this research is to propose iterative feature selection technique using Boruta (random forest) for SDP model. This research proposes Two?step preprocessing using SMOTE and BORUTA. Support Vector Machine (SVM), Neural Networks and XGboostclassifiers are used by MLC. Furthermore to confirm the accuracy, performance and capability of each classifier on PROMISE dataset evaluationmeasures AUC,recall, F1-measure, andaccuracy are used