This research aims to identify the relationship between occupational hypocrisy and organizational strategic success, It was done by analyzing the correlations and influence between variables,  applied to a random sample of university professors at the University of Kufa faculty of administration and economic.

 The main tool for data collection is the survey were questionnaires were distributed randomly to the professors , and (43) questionnaires were returned, and test its validity by using (SEM) (Structural Equation Modeling), Hypothesis has been tested by using Statistical Package for Social Sciences (SPSS v. 18), The research found a set of conclusions:(The occupational hypocrisy has

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

The research aimed to identify "the effectiveness of educational-learning design according to the model of brain compatibility in achievement among firstmiddle grade students in mathematics", in schools affiliated with the Second Karkh Directorate of Education. To achieve the goal of research, the following zero hypothesis has been formulated: " There is no statistically significant difference at the semantic level (05.0) between the average scores of experimental group students who will study with design accreditation (educational - learning) according to the brain compatibility model and the grades of control group students who will study in the usual way in the achievement thinking test". The research community, which is represented by

A new ligand [N-(4-methoxy benzoyl amino)-thioxo methyl ] leucine (MBL) was prepared from the reaction of (4-methoxy benzoyl isothiocyanate with leucine acid in molar ratio (l:l), it was characterized by elemental analysis (C.H.N.S), FT-IR, UV-Vis, 1H and 13C-NMR. The complexes of the bivalent ions (Mn, Fe, Co, Ni, Cu, Zn, Cd and Hg ) have been prepared and characterized too. The structural was established by elemental analysis (C.H.N.S), FT-IR, UV-Vis spectra, conductivity measurements atomic absorption and magnetic susceptibility and determination of molar ration (M:L). The complexes showed characteristic behavior of tetrahedral geometry around the metal ions except with (Cu) complex showed square planer.

The electronic characteristics, including the density of state and bond length, in addition to the spectroscopic properties such as IR spectrum and Raman scattering, as a function of the frequency of Sn₁₀O₁₆, C₂₄O_6, and hybrid junction (Sn₁₀O₁₆/C₂₄O₆) were studied. The methodology uses DFT for all electron levels with the hybrid function B3-LYP (Becke level, 3-parameters, Lee–Yang-Parr), with 6-311G (p,d)  basis set, and Stuttgart/Dresden (SDD) basis set, using Gaussian 09 theoretical calculations. The geometrical structures were calculated by Gaussian view 05 as a supplementary program. The band gap was calculated and compared to the measured valu

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

In the mist of developments in the course of scientific research in general and humanities in particular and the accompanying changes in the visions and policies inspired by the need for these sciences to follow the qualitative methods in dealing with many of the topics or problems that require for their solution to obtain qualitative information which can be provided by resorting to quantitative research. Therefore, a new trend has emerged in many public relations› researchers who believe that qualitative research methods should be used by establishing scientific foundations and methodological classification based on the use of these methods and determining the nature of the subjects applied in them to reach results that are character