h. mohammadzade

We introduce a novel Regularized Kernel Projection Pursuit Regression method which is a two-step nonlinearity encoding algorithm tailored for such very low dimensional problems as fatigue detection. This way, the data nonlinearity can be investigated from two different perspectives, first by transforming the data into a high dimensional intermediate space and then by using their spline estimations to the output variables which allows for a hierarchical unfolding of data. Experimental results on the SEED database shows an average RMSE value of 0.1080% and 0.1054% respectively for the temporal and posterior areas of the brain. Our method is also validated by conducting some experiments on Parkinson's disease prediction which further demonstrate the efficiency of our method for low-dimensional regression problems.Traditional off-the-shelf regression methods like SVR, KSVR, and GLM methods all require their link functions to be previously selected which limits their effectiveness for encoding the nonlinearity of a highly complex low dimensional data set. Moreover, conventional PPR does not deal with the very low dimensionality of data. This paper proposes a novel regression algorithm to address the encoding problem of a highly complex low dimensional data, which is usually encountered in bio-neurological prediction tasks like EEG based driving fatigue detection.

We studied the effect of privatization on financial and operating performance of Iranian governmental entities that were privatized during 2000-2007. The purpose of this study is the investigation on the success of privatization program in reaching performance improvements. In this study the performance of firms that privatized by initial public offering were tested by earnings quality, profitability, liquidity, operating efficiency, capital investment and employment indexes. The innovation of this study was the initial use of earnings quality index in Iranian privatization researches. Dechow and Dichev (2002) model was used to measure earnings quality. After testing of data normality, the comparemeans technique for paired samples test was used for each hypothesis. The earnings quality index and sales to employment ratio were significantly lower than before privatization period. The net income to employment ratio as other operating efficiency factor was significantly higher than before privatization period. Other indexes did not showsignificant differences. The general result of this research is that the privatizing of Iranian governmental entities by public offering method has not been successful.