The successful key of trading in the forex market is the selection of correct exchange in proper time based on an exact prediction of future exchange rate. Foreign exchange rates are affected by many correlated economic, political and even psychological factors. Therefore, in order to achieve a profitable trade these factors should be considered. The application of intelligent techniques for forecasting has been proved extremely successful in recent years. Previous studies have mainly focused on the historical prices and the trading volume of one market only. In this paper, we have used Artificial Neural Networks (ANN) to predict the exchange rate with respect to three external factors including gold, petroleum prices and FTSE 100 index. The result of forecasts is compared with the ANNs without external factors. The empirical results demonstrate that the proposed model can be an effective way of forecasting. For the experimental analysis phase, the data of exchange rate of GBP/USD is used. تفاصيل المقالة

As networking and communication technology becomes more widespread, the quantity and impact of system attackers have been increased rapidly. The methodology of intrusion detection (IDS) is generally classified into two broad categories according to the detection approaches: misuse detection and anomaly detection. In misuse detection approach, abnormal system behavior is defined at first, and then any other behavior is defined as normal behavior. The main goal of the anomaly detection approach is to construct a model representing normal activities. Then, any deviation from this model can be considered as an anomaly, and recognized to be an attack. Recently much more attention is paid to the application of lattice theory in different fields. In this work we propose a lattice based nearest neighbor classifier capable of distinguishing between bad connections, called attacks, and good normal connections. A new nonlinear valuation function is introduced to tune the performance of the proposed model. The performance of the algorithm was evaluated by using KDD Cup 99 Data Set, the benchmark dataset used by Intrusion detection Systems researchers. Simulation results confirm the effectiveness of the proposed method. تفاصيل المقالة