CO2 emissions are known as carbon emissions and they are increasing globally, so measures must be taken to reduce their emissions and find solutions that mitigate the effects of high rates of emissions. CO2 emissions are currently increasing, and the construction sector is the largest source of these emissions, accounting for 39% of total emissions; therefore, CO2 emissions must be reduced as much as possible. The research aim is to study the effect of applying vegetated façades, in multistory residential buildings on the CO2 emissions under two different climates, cold semiarid and hot arid. The tool which is used is a DesignBuilder to evaluate the CO2 emissions. Thermal insulation contributes to reducing the carbon footprint of the building and consider as an additional layer applied to the bare wall façade as the same as the vegetation layer therefore in this research the comparison will be between the use of insulation materials and green façade with a bare wall façade of residential buildings to determine the effectiveness of using vegetated facades in reducing CO2 emissions. The study found that vegetated facades reduce CO2 emissions from 36.2 to 51.4 in cold semiarid climates and from 18% to 37.6% in hot arid climates.
Credit fraud modeling is an important topic covered by researchers. Overdue risk management is a critical business link in providing credit loan services. It directly impacts the rate of return and the bad debt percentage of lending organizations in this sector. Credit financial services have benefited the general public as a result of the development of the mobile Internet, and overdue risk control has evolved from the manual judgment that relied on rules in the past to a credit model built using a large amount of customer data to predict the likelihood of customers becoming delinquent. When creating a credit rating model, the emerging nature of the credit samples makes the minority class sample score very few; that is, when a large number of actual samples are obtained, this causes machine learning models to be biased towards the majority class when training. Traditional data balancing methods can reduce the bias of models to the majority category when the data is relatively unbalanced rather than excessive. Gradient boosting algorithms (XGBoost and CatBoost) are proposed in this paper to model highly unbalanced data to detect credit fraud. To find hyperparameters and determine the accuracy of the minority class as an optimization function of the model, Bayesian optimization is used to increase the model's accuracy for the minority class. The paper was tested with real European credit card fraud data. The results were compared to traditional machine learning (decision trees and logistic regression) and the performance of the bagging algorithm (random forest). For comparison, the traditional data balancing method (Oversample) is used
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