Abstract The Design Science Research method (DSR) is an approach to provide practical solutions based on scientific principles in order to produce substantiated and inferred results and products, and at the same time, the results can be scientifically evaluated in the form of primary artifacts and practical use in four main stages which ultimately results in their practical efficiency and effectiveness in the outside world. By designing and creating an archetype in the prototyping stage, DSR evaluates real scenarios and then examines the solution in practical cases. From this point of view, in this research, it has been tried to use the DSR method to provide an innovative solution for predicting bank liquidity risk and upcoming scenarios. This study uses semtiment analysis and deep learning algorithm such as deep convolutional network in predicting liquidity risk and presents a simple and effective method to identify dynamic qualitative variables from recent news about a domestic bank in the country. Predicted scenarios are available to banking experts in the real world to facilitate decision-making in risk measures. According to the guidelines of the Basel Committee and other European banking regulatory frameworks, comparing these scenarios with the scenarios occurring in the bank indicates a relatively high accuracy of the proposed method. In the scenarios derived from the Basel Committee and derived from the European Banking Authority, the forecasting accuracy is about 91% and 82%, respectively

Abstract Mathematical robust modeling, a modern approach in IRAN public budgeting Abstract: Nowadays most of resource allocation is mental, empirical and based on the old methods. So it does not have generalization ability and mathematical confirm, Hence using from quantity theory for nearing of quantitative and qualitative human resource is essential objective. Budgeting lows based on forecasting which always face with errors and uncertainty. These errors are Prediction errors, Measurement Errors, Implementation Errors, so mirror change cause to hesitation on optimally and feasibility for budgeting lows. Goal of this article is designing of a budgeting model in the public sector in IRAN to be able for glancing uncertainty factor which with preserving optimally and feasibility allocation area in the budget low in contorting with changing, guaranty his flexibility in the also implementation. There are different approaches for glancing of effect of uncertainty which from these approaches, we selected robust optimization approach And the model developed by using of Bertsimas & Sym model. Until on foundation of measurement convex, decision makers from situation of now and future of country, be able use from every one these robust models.