Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

The rapid developmemt of information technology and its use in all fields has a bositive influence on all fields , and financial markets have a share of this development through the use of an electronic trading system to settle transactions and enhance transparency and disclosure in all activities of these markets and stimulate their performance .

It is worth nothing that these revolutions remove the necessity for nonstop connection with persons through the internet or phone networks , novel knowledge decreases the charges of structure original transaction system and reducing the fences of new participants entry .

The development in transportations expertise allows for quicker or

This research analyzes the level of the short circuit effect of the Iraqi super network and decides the suitable location for the High Voltage Direct Current (HVDC) connections in order to obtain the best short circuit reduction of the total currents of the buses in the network. The proposed method depends on choosing the transmission lines for Alternating current (AC) system that suffers from high Short Circuit Levels (SCLs) in order to reduce its impact on the transmission system and on the lines adjacent to it and this after replacing the alternating current (AC) line by direct current (DC) line. In this paper, Power System Simulator for Engineering (PSS/E) is used to model two types of HVDC lines in an effective regi

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

In order to achieve overall balance in the economy to be achieved in different markets and at one time (market commodity, monetary and labor market and the balance of payments and public budget), did not provide yet a model from which to determine the overall balance in the economy and the difficulty of finding the inter-relationship between all these markets and put them applied in the form of allowing the identification of balance in all markets at once.

One of the best models that have dealt with this subject is a model
(LM-BP-IS), who teaches balance in the commodity market and money market and balance of payments and the importance of this issue This research tries to shed light on the reality

Metoclopramide (MCP) ion selective electrodes based on metoclopramide-phosphotungstic acid (MCP-PT) ion pair complex in PVC matrix membrane were constructed. The plasticizers used were tri-butyl phosphate (TBP), di-octyl phenyl phosphonate (DOPP), di-butyl phthalate (DBPH), di-octyl phthalate (DOP), di-butyl phosphate (DBP), bis 2-ethyl hexyl phosphate (BEHP). The sensors based on TBP, DOPP, DBPH and DOP display a fast, stable and linear response with slopes 59.9, 57.7, 57.4, 55.3 mV/decade respectively at pH ranged 2-6. The linear concentration range between 1.0×10^-5 – 1.0×10^-2 M with detection limit 3.0×10^-6 and 4.0×10^-6 M for electrodes using TBP, DOPP and DBPH while e

 In this paper we study the effect of the number of training samples for  Artificial neural networks ( ANN ) which is necessary for training process of feed forward neural network  .Also we design 5 Ann's and train 41 Ann's which illustrate how good the training samples that represent the actual function for Ann's.

The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con