Whatever the designers of the advertisement in choosing the text and spoken phrases, those phrases cannot give or convey the full meaning to the recipient only if this spoken and written language is reinforced with another language based on the signals, movements, and symbols that are displayed using the body or other artistic elements of the advertisement such as pictures, colors, music, effects, and other elements used in the artistic construction of television advertising. All these artistic elements contribute to the completion of the advertising idea and make it ready to be displayed to the public.

Scientists and researchers, in the field of psychology, have relied a lot on this language (body language). And some of them put

The no parity problem causes determining is the most interesting case by doctors and researchers in this filed, because it helps them to pre-discovering of it, from this point the important of this paper is came, which tries to determine the priority causes and its fluency, thus it helps doctors and researchers to determine the problem and it’s fluency of increase or decrease the active sperm which fluencies of peregrinating. We use the censored regression (Tobit) model to analyze the data that contains 150 observations may by useful to whom it concern.         

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

     The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.

     Was conducted to compare the two methods above and it became clear by comparing the  logistic regression model best of a Linear Discriminant  function written

In this paper, we investigate the behavior of the bayes estimators, for the scale parameter of the Gompertz distribution under two different loss functions such as, the squared error loss function, the exponential loss function (proposed), based different double prior distributions represented as erlang with inverse levy prior, erlang with non-informative prior, inverse levy with non-informative prior and erlang with chi-square prior.

The simulation method was fulfilled to obtain the results, including the estimated values and the mean square error (MSE) for the scale parameter of the Gompertz distribution, for different cases for the scale parameter of the Gompertz distr

We have provided in this research model multi assignment  with  fuzzy function goal has been to build programming model is correct Integer Programming fogging  after removing the case from the objective function data and convert it to real data .Pascal triangular graded mean using Pascal way to the center of the triangular.

The data processing to get rid of the case fogging which is surrounded by using an Excel 2007 either model multi assignment  has been used program LNDO to reach the optimal solution, which represents less than what can be from time to accomplish a number of tasks by the number of employees on the specific amount of the Internet, also included a search on some of the

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Nutlets of 22 taxa of Stachys (13 species and seven subspecies and two varieties), representing seven of the currently recognized sections distributed in northern Iraq were examined by light microscope. The basic shape of nutlets in most taxa studied is Obovoid, but Oblong also found in S.megalodanta Hausskn.& Bornm. ex P.H.Davis, S.setirefa C.A.Mey. subsp daenensis (Gandog.) Rech.f.and S. kurdica Boiss.& Hohen. var.kurdica, while the Subgloboid shape found in S. iberica M.Bieb. and S. inflata Benth., more over the Broad triangular shape was found in S. nephrophylla Rech.f. and S.lanigera (Bornm.) Rech.f.., the biggest size of nutlets was found in S.inflata L. and the smallest was in S.melampyroides Hand.-Mzt. Regarding sculpturing pa