In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.

The aim of this research is to investigate the effect of economic, social and institutional factors on adoption within the national program for the propagation of high-rank seeds of wheat crop. 170 questionnaires were collected, 50% of them were participants in the program from Wasit and Babil governorates. Probabilistic regression models were used to know this effect, and the possibility of adopting Farmers of improved seeds produced from the national program for the multiplication of seeds of higher grades using the (ADOPT) program. The adoption rate was 0.12%, and the total number of adopters were 12 farmers, at a rate of 14.2%. Tobit model was estimated  to find out the impact of the profitability of the dunum, capital, farm si

Several previous investigations and studies utilized silica fume (SF) or (micro silica) particles as supplementary cementitious material added as a substitute to cement-based mortars and their effect on the overall properties, especially on physical properties, strength properties, and mechanical properties. This study investigated the impact of the inclusion of silica fume (SF) particles on the residual compressive strengths and microstructure properties of cement-based mortars exposed to severe conditions of elevated temperatures. The prepared specimens were tested and subjected to 25, 250, 450, 600, and 900 °C. Their residual compressive strengths and microstructure were evaluated and compared with control samples (C

In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen

Contents IJPAM: Volume 116, No. 3 (2017)

In this research, the efficiency of low-cost unmodified wool fibers were used to remove zinc ion from industrial wastewater. Removal of zinc ion was achieved at 99.52% by using simple wool column. The experiment was carried out under varying conditions of (2h) contact time, metal ion concentration (50mg/l), wool fibers quantity to treated water (70g/l), pH(7) & acid concentration (0.05M). The aim of this method is to use a high sensitive, available & cheep natural material which applied successfully for industrial wastewater& synthetic water, where zinc ion concentration was reduced from (14.6mg/l) to (0.07mg/l) & consequently the hazardous effect of contamination was minimized.

This paper shows an approach for Electromyography (ECG) signal processing based on linear and nonlinear adaptive filtering using Recursive Least Square (RLS) algorithm to remove two kinds of noise that affected the ECG signal. These are the High Frequency Noise (HFN) and Low Frequency Noise (LFN). Simulation is performed in Matlab. The ECG, HFN and LFN signals used in this study were downloaded from ftp://ftp.ieee.org/uploads/press/rangayyan/, and then the filtering process was obtained by using adaptive finite impulse response (FIR) that illustrated better results than infinite impulse response (IIR) filters did.

This work aims to provide a statistical analysis of metal removal during the Magnetic Abrasive Finishing process (MAF) and find out the mathematical model which describes the relationship between the process parameters and metal removal, also estimate the impact of the parameters on metal removal. In this study, the single point incremental forming was used to form the truncated cone made of low carbon steel (1008-AISI) based on the Z-level tool path. Then the finishing was accomplished using a magnetic abrasive process based on the Box-Behnken design of the experiment using Minitab 17 software was used to finish the surface of the formed truncated cone. The influences of different parameters (feed rate, machining step s