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EN
This work aims to develop defect severity level prediction models that have the ability to assign severity level of defects based on bugs report. In this work, seven different word embedding techniques are applied to defect description to represent the word, not just as a number but as a vector in n-dimensional space. Further, three feature selection techniques have been applied to find the right set of relevant vectors. The effectiveness of these word embedding techniques and different sets of vectors are evaluated using different classification techniques with SMOTE to overcome the class imbalance problem.
EN
The economy of Pakistan had always been plunged due to its severe electricity shortages over the last two decades and persistently faces challenges in revamping its electricity supply network. The China Pakistan Economic Corridor (CPEC) is considered as a productive shock which has opened up new avenues for the energy sector in Pakistan. This study is an endeavor to incorporate the impact of such shock in the production function and to revisit the dynamics between electricity consumption and economic growth (ECEG) in the new economy of Pakistan for the time span of 1971-2018. The study has employed Vector Autoregression (VAR) model, including capital formation, labor participation, openness of the economy and financial development. The findings of the study affirmed the neutrality hypothesis while cointegration estimates jagged long run effectiveness for ECEG nexus. Keeping in view the internal and external bottlenecks, it is thus recommended to revise the ECEG model for the new economy of Pakistan keeping in view the revival of industrial sector removing the inefficiencies of the power sector.
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