computational intelligence

Financial econometrics and computational intelligence are two key fields used independently for financial data analysis and risk modeling. Financial econometrics employs statistical and mathematical models to analyze relationships between economic and financial variables, while computational intelligence focuses on complex and nonlinear data analysis for predicting financial risks and volatility. The synergy between these two approaches can enhance the accuracy of modeling and financial decision-making. This paper aims to examine the synergy between financial econometrics and computational intelligence in risk modeling and presents successful examples of its application in finance. This study employs a descriptive and narrative review of the existing literature. Relevant academic sources related to the applications of financial econometrics and computational intelligence in predicting volatility and financial risks were reviewed. Additionally, combined models of these two fields were analyzed, identifying their synergies and evaluating the challenges involved. The results demonstrate that combining financial econometrics and computational intelligence significantly improves the accuracy of financial forecasts, reduces systemic risks, and identifies complex patterns in financial data. Combined models, especially in volatile and unstable financial markets, outperform individual approaches. The combination of financial econometrics and computational intelligence can lead to enhanced risk modeling and financial decision-making. Despite challenges such as the need for large datasets and computational complexity, this synergistic approach holds significant potential for developing more accurate predictive models and improving risk management.

In all human behavioral spectrums, Politics is the most difficult behavior that can be automated. Politics is inherently complex, reflecting the complexity of human behavior both as an individual and in the social dimension. This complexity seems much more real at the level of international relations. Artificial intelligence is an influential and pervasive field that scope is not only related to technical and engineering issues, but also includes the fields of humanities, especially international peace and security. Using game theory as part of artificial intelligence in the field of political science and international relations is concentrated in common areas such as equitable distribution, political economy, public choice, war bargaining, positive policy theory, and public choice theory. In each of these areas, researchers have developed game theory models in which player models are often represented in the form of voters, governments, special interest groups, and politicians. Therefore, in this research the role of game theory in international relations will be investigated.

Suspended sediment load is one of the most important river elements that, in addition to its impact on water quality, has an effective role in managing water resources and structures on these resources. Therefore, estimating the suspended load can be a great help in increasing the productivity of water resources and improving the efficiency of aquifers. Determining the exact and proper parameters that have an effective factor in the sedimentation rate of the catchment area has an effective role in increasing the accuracy of data mining models. Physiographic parameters that indicate the physical properties of the watershed can be considered as a determining factor in the extent of sedimentation of the catchment area. There are several methods to estimate the suspended load of rivers. One of these methods is the use of data mining models that are very useful in solving hydrologic problems. Therefore, in this research, by combining data mining models and sediment load physiographic parameters of 30 watersheds in Lorestan province with a statistical period of 33 years, in order to study the effect of different physiographic indices on sediment estimation in the first step of flow flow as the only input of models and in step Next, various field physiographic indices were selected as inputs of different data mining models. In this study, five data mining models including artificial neural network, evolutionary support vector machine, decision tree, Gaussian process and regression were used. All models have been well received.

In this paper، a queuing-inventory system with continuous review inventory control policy and batch arrival queuing approach is proposed. To best of our knowledge، (I) demand function is stochastic and price dependent; (II) due to the uncertainty in real-world situations، a fuzzy programming approach is applied. Therefore، the presented model with goal of maximizing total profit of system analyzes the price and order quantity decision variables. Since the proposed model belongs to NP-hard problems، two Pareto-based approaches based on genetic algorithm is applied to solve the model. At end، several numerical illustrations are generated to demonstrate the model validity and algorithms performance.