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There are few changes that took place in Iraqin many fields during the past few years; the financial aspect is one of the fields that undergone this change. The change has positive impact because it increases the revenue inIraqfrom the oil exports. The National Insurance Company is one of many companies that belongs to the Ministry of Finance inIraqand has affected directly from this change in term of increasing the number of the insurers which we will discuss in this research. The aim of this research is to forecast the insurance premiums revenue of the National Insurance Company between the years 2012 to 2053 using Artificial Neural Network based on the actual annual data of the insurance premiums revenue between the years 1970 to 2011. The data analyses results of this research show that the growth indicator of the insurance premiums revenue for the next 41 years is approximately 120%, the Mean Squared Error is the average squared difference between outputs and targets. Lower values are better. Zero means no error and the regression values are very high. The estimations and forecasts of the insurance premiums revenue using Artificial Neural Network confirmed to be strong and useful to deploy it for forecasting the insurance premiums revenue.
This paper aims to demonstrate the importance and possible value of housing
predictive power which provides independent real estate market forecasts on home
prices by using data mining tasks. A (FFBP) network model and (CFBP) network
model are one of these tasks used in this research to compare results of them. We estimate the median value of owner occupied homes in Boston suburbs
given 13 neighborhood attributes. An estimator can be found by fitting the inputs and targets.
This data set has 506 samples. “ousing inputs” is a 13 × 506 matrix. The “housing
targets” is a 1 × 506 matrix of median values of owner-occupied homes in
result in this paper concludes that which one of the two networks
appears to be a better indicator of the output data to target data network
structure than maximizing predict. The CFBP network which is the best
result from the Output_network for all samples are found from the equation
output = 0.95 * Target + 1.2. The regression