Time is very important in educational institutions. It is also one of our contemporary problem ‚as time is a clear – cut and limited factor‚ it demands that administrators should monitor it by administering and monitoring the principles of time.

Hence‚ the researcher attempts to identify the skills of administrating time and the reasons that cause the waste of time of the Heads of Departments at university of Baghdad.

Significance  of the research:

Time is very important to all educational administrators and one of them is the institutions of Higher education. One of the

The phenomenon of informal building Spread recently in Iraqi residential areas, in general, and in Baghdad, in particular, due to the urgent housing need, on the one hand, and lack of commitment to building controls, on the other hand, to highlight the phenomenon of uncommitted building to controls and housing governing legislation in Iraq, leading to heterogeneity in both building densities and plot areas, and disorder in the urban fabric and urban escape of those areas. Research problem identified as the absence of a clear vision about the General aspects of the phenomenon of informal building in residential street scene, and the role of designed housing projects as a substitute for informal building in built residential areas. The des

The occurrence of two species of the genus Myxobolus Bütschli, 1882 (Myxozoa: Myxosporea) for the first time in Iraq from freshwater fishes.

The compound [K1] was synthesized from the reaction of dichloromethane with linear alkyl benzene (Lab9) using ethanol as a solvent, and from(chloro methyl)-4-nonylbenzene) [K1] it was possible to synthesize the compound Z(4-(nonan-3-yl)phenyl) methane amine) [K2] containing the amine group by synthesized from [K2] reaction with appropriate phenolic aldehydes and using Ethanol as a solvent in the preparation of vinyl chloride4-(((4-nonylbenzyl)imino)methyl)phenol-4-(((4-nonylbenzyl)imino methyl)benzene-1,3diol) [K3-K4] bases has been used. Preparation of a number of Phenolic polymers4-(2- hydroxy-3.5-dimethylbenzyl)-2-methyl-6-(((4-4-(2hyroxy-3, 5-dimethylbenzyl)-2-methyl-6(((4 nonylbenzyl) imino) methyl) benzene-phenolnonylbenzyl) imino) me

The monogenean Gyrodactylus bychowskianus Bogolepova, 1950 is recorded in the present study for the first time in Iraq from the gills of the cyprinid fish Arabibarbus grypus (Heckel, 1843); which was collected from the Tigris River at Al-Taji Beach north of Baghdad Province during the period from July until November 2018.

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve ne

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par