درخت حوزه‌های تخصصی

Banks as the largest financial intermediaries play a vital role in collecting savings and directing them toward manufacturing activities. Hence, assets and liabilities management is crucial for them and their depositors. In this study, through system dynamics approach, a dynamic model was proposed to manage assets and liabili-ties optimally. After building the model and conducting validation and sensitivity analysis, three scenarios were devised and were simulated and analyzed via the proposed model. The results revealed that taking effective decisions and actions to collect more deposits, especially time deposits as well as increasing the quality of credit assets (if allocated optimally) can be effective in improving the performance of banks. Therefore, using systems thinking and system dynamics can aid banks in managing their assets and liabilities optimally. mary of the significant items in the paper, including the results and conclusions.

K nearest neighbor algorithm is one of the most frequently used techniques in data mining for its integrity and performance. Though the KNN algorithm is highly effective in many cases, it has some essential deficiencies, which affects the classification accuracy of the algorithm. First, the effectiveness of the algorithm is affected by redundant and irrelevant features. Furthermore, this algorithm does not consider the differences between samples, which led the algorithm to have inaccurate predictions. In this paper, we proposed a novel scheme for improving the accuracy of the KNN classification algorithm based on the new weighting technique and stepwise feature selection. First, we used a stepwise feature selection method to eliminate irrelevant features and select highly correlated features with the class category. Then a new weighting method was proposed to give authority value to each sample in train dataset based on neighbor categories and Euclidean distances. This weighting approach gives a higher preference to samples that have neighbors with close Euclidean distance while they are in the same category, which can effectively increase the classification accuracy of the algorithm. We evaluated the accuracy rate of the proposed method and analyzed it with the traditional KNN algorithm and some similar works with the use of five real-world UCI datasets. The experiment results determined that the proposed scheme (denoted by WAD-KNN) performed better than the traditional KNN algorithm and considered approaches with the improvement of approximately 10% accuracy.

The study is aimed at assessing the strategic importance of a business processes for achieving sustainable competitive advantage, therefore, in this article the theoretical approach for measuring the strategic contribution of a business process to an enterprise’s business system is presented.  For evaluating of a business process strategic importance the study proposes the system of economic and managerial indicators, which includes the process’ contribution to the added value, its compliance to critical success factors, and its organizational involvement. Сombining these three indicators into one integral allows it to be used in different types of matrix analysis to make decisions on improving of a company’s business system.