Sovereign credit ratings have been a controversial issue since the outbreak of the 2008 financial crisis. Among the debates the inaccuracies stay at the centre. By employing classification and regression trees, multilayer perceptron, support vector machines (SVM), Bayes net, and naïve Bayes; we compare the ability of various learning techniques with the conventional statistical method in predicting sovereign credit ratings. Experimental results suggest that all the techniques excluding SVM have over 90 % accurate prediction. According to within one and two notch accurate prediction measure, the prediction performance of SVM also increases above 90 %. These findings indicate a clear outperformance of AI methods over the conventional statistical method. The results have many implications for the practitioners in credit scoring industry. Amidst the regulatory measures that encourage individual credit scoring for international financial institutions, these findings suggest that up-to-date AI methods serve quite reliable technical tools to predict sovereign credit ratings.
Building Information Modeling/Management (BIM) technology has attracted the attention of many researchers and academicians as a new concept that has increased rapidly in the construction sector in recent years. The time-dependent changing society has affected the expectations and demands of the construction industry and triggered/resulted the production of more complex and original projects accordingly. Building Information Modeling/Management (BIM) concept has become a necessity in today's construction industry in terms of providing integration between project stakeholders and providing the possibility of processing and storing the project data in a common point. Examining international studies, it is seen that the implementation of the Building Information Modeling/Management (BIM) approach is a mandatory job and/or task in the construction industries of the developed countries. It is observed that they follow this new trend in their rapidly developing countries. This study proposes a procurement framework based on the implementation of Building Information/Management (BIM) to achieve 'best results' in construction projects. A case study presented in this study proves the applicability and usefulness of the proposed Building Information Modeling/Management (BIM) approach in a complex construction project funded by the private sector. Contractual arrangements recommended for the project with an effective resource management approach, one of the basic principles of Building Information Modeling/Management (BIM); benefits such as improved productivity, better coordination and minimized errors and repetition of works.
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