‎ This research aims to estimate stock returns, according to the ‎Rough Set Theory ‎approach, ‎test ‎its effectiveness and accuracy in predicting stock returns and their potential in the ‎field of ‎financial ‎markets, and rationalize investor decisions. The research sample is totaling (10) ‎companies traded at Iraq Stock Exchange. The results showed a remarkable ‎ ‎Rough Set Theory application in data reduction, contributing to the rationalization of ‎investment ‎decisions. The most prominent conclusions are the capability of rough set theory ‎in ‎dealing with financial data and applying it for forecasting stock ‎returns.‎The ‎research provides those interested in investing stocks in financial

The study aims to demonstrate the importance of instructional methods in teaching Arabic language as a second language or teaching the Arabic language to non-native speakers. The study is in line with the tremendous development in the field of knowledge, especially in the field of technology and communication, and the emergence of many electronic media in education in general and language teaching in particular. It employs an image in teaching vocabulary and presenting the experience of the Arabic Language Institute for Non-Speakers-King Abdul-Aziz University. The study follows the descriptive approach to solve the problem represented by the lack of interest in the educational methods when teaching Arabic as a second language. Accordingl

The research aims to apply a modified SERVQUAL model to evaluate the quality of the educational services via conducting exploratory research for students from the College of Administration and Economics- Department of Business Administration- Evening studies at the University of Baghdad. Questionnaire of two parts was distributed to a sample of (72) students out of (720) students of the 2^nd.,3^rd. and 4^th. year in the beginning of the second semester of the year 2008-2009 to measure the expectations and perceptions to the quality of the educational services. Five major dimensions were analyzed to see the gaps for (22) variables. The study concluded that there were (13) variables confirmed that the

This research deals with unusual approach for analyzing the Simple Linear Regression via Linear Programming by Two - phase method, which is known in Operations Research: “O.R.”. The estimation here is found by solving optimization problem when adding artificial variables: Ri. Another method to analyze the Simple Linear Regression is introduced in this research, where the conditional Median of (y) was taken under consideration by minimizing the Sum of Absolute Residuals instead of finding the conditional Mean of (y) which depends on minimizing the Sum of Squared Residuals, that is called: “Median Regression”. Also, an Iterative Reweighted Least Squared based on the Absolute Residuals as weights is performed here as another method to

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

Pyogenic granuloma is one of the inflammatory hyperplasia seen in the oral cavity. The
present study included 10 patients with pyogenic granuloma, involving 4 males and 6 females with 1:1.5
male to female ratio. Patient ages ranged from 5 to 85 years (mean, 30 years) and half of the lesions had
pedunculated base, with surface ulceration in 10% of cases. Treatment consisted of resection, using 810
nm diode lasers. Eight patients were anesthetized during the surgical operation by local infiltration of
anesthesia. Only three patients reported mild post-operative pain within the first 24 hours of the healing
period. During the surgical operation there was no significant bleeding so clear surgical field. There was
no blee

Pyogenic granuloma is one of the inflammatory hyperplasia seen in the oral cavity. The
present study included 10 patients with pyogenic granuloma, involving 4 males and 6 females with 1:1.5
male to female ratio. Patient ages ranged from 5 to 85 years (mean, 30 years) and half of the lesions had
pedunculated base, with surface ulceration in 10% of cases. Treatment consisted of resection, using 810
nm diode lasers. Eight patients were anesthetized during the surgical operation by local infiltration of
anesthesia. Only three patients reported mild post-operative pain within the first 24 hours of the healing
period. During the surgical operation there was no significant bleeding so clear surgical field. There was
no blee

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

        Urban land price is the primary indicator of land development in urban areas. Land prices in holly cities have rapidly increased due to tourism and religious activities. Public agencies are usually facing challenges in managing land prices in religious areas. Therefore, they require developed models or tools to understand land prices within religious cities. Predicting land prices can efficiently retain future management and develop urban lands within religious cities. This study proposed a new methodology to predict urban land prices within holy cities. The methodology is based on two models, Linear Regression (LR) and Support Vector Regression (SVR), and nine variables (land price, land area,