Comparison Of Dt& Gbdt Algorithms For Predictive Modeling Of Currency Exchange Rates

Recently, many uses of artificial intelligence have appeared in the commercial field. Artificial intelligence allows computers to analyze very large amounts of information and data, reach logical conclusions on many important topics, and make difficult decisions, this will help consumers and businesses make better decisions to improve their lives, and it will also help startups and small companies achieve great long-term success. Currency exchange rates are important matters for both governments, companies, banks and consumers. The decision tree is one of the most widely artificial intelligence tools used in data mining. With the development of this field the decision tree and Gradient boosting decision tree are used to predicate through constructed intelligent predictive system based on it. These algorithms have been used in many stock market forecasting systems based on global market data. The Iraqi dinar exchange rates for the US dollar are affected in local markets, depending on the exchange rate of the Central Bank of Iraq and the features of that auction. The proposed system is used to predict the dollar exchange rates in the Iraq markets Depending on the daily auction data of the Central Bank of Iraq (CBI). The decision tree and Gradient boosting decision tree was trained and testing using dataset of three-year issued by the CBI and compare the performance of both algorithms and find the correlation between the data. (Runtime, accuracy and correlation) criteria are adopted to select the best methods. In system, the characteristic of artificial intelligence have been integrated with the characteristic of data mining to solve problems facing organization to use available data for decision making and multi-source data linking, to provide a unified and integrated view of organization data

Model of Dynamics of the Grouping States of Radio Electronic Means in the Problems of Ensuring Electromagnetic Compatibility

A dynamic model of multiple interactions of n-elements of a complex mobile communication system is developed, which takes into account the nature of inter-element communications and phase states of the grouping of electronic devices. The model describes the electromagnetic environment of the grouping of electronic equipment in the state space during group use of a frequency resource.The simulation of the dynamics of interaction and phase states of the grouping of electronic equipment is done with the group use of the frequency resource.It is shown that at sufficiently large values of the growth coefficient of the number of electronic devices, both a sharp increase in the level of interaction intensity and a sharp decrease characteristic of those situations that occur in mobile communication systems during the busy hours in places of high density of mobile users can occur.The analysis of the dynamics of the grouping of radio-electronic means of a mobile communication system at different intensities of linear and non-linear multiple interactions, the combined nature of which is displayed by the normalized value of the signal/(jam+noise) ratio, is carried out. The dynamics of non-equilibrium states of the groups of 2 mobile networks at various values of the intensity of interactions is considered.It is established that the non-equilibrium state of the mobile communication system occurs when the total level of the group influence of the emitting devices on the receiving devices increases with a normalized value of the interaction intensity of more than 1.4.The conditions are identified under which the grouping of radio-electronic means of a mobile communication system can function without deterioration of quality indicators, characterized by the total level of group influence of radiating devices on receiving devices, under conditions of optimal frequency resource distribution.It is shown how, using the non-linear Volterra system, which simulates the dynamics of the interactions of a grouping of electronic equipment, it is possible to analyze its state in the future. This model allows to analyze the grouping of electronic equipment with various specific parameters of individual types of electronic equipment, the nature and intensity of their interaction in the group with the current distribution of resource