Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer

Abstract. In this research, the uranium concentration in (16) water samples collected from some agricultural areas surrounded with AlTuwitha nuclear site in Baghdad-Iraq was measured by using a CR-39 detector. The concentration of uranium in this study was from (0.6 ± 0.33mg/l) to (2.51 ± 0.49 mg/l), and the weighted average for the concentrations (1.262 ± 0.402 mg/l). The results showed it is a concentration of uranium level in water samples studied is higher than the allowed limit recommended by WHO and ICRP.

Treated effluent wastewater is considered an alternative water resource which can provide an important contribution for using it in different purposes, so, the wastewater quality is very important for knowing its suitability for different uses before discharging it into fresh water ecosystems. The wastewater quality index (WWQI) may be considered as a useful and effective tool to assess wastewater quality by indicating one value representing the overall characteristic of the wastewater. It could be used to indicate the suitability of wastewater for different uses in water quality management and decision making. The present study was conducted to evaluate the Al-Diwaniyah sewage treatment plant (STP) effluent quality based on wastewa

According to the importance of the conveyor systems in various industrial and service lines, it is very desirable to make these systems as efficient as possible in their work. In this paper, the speed of a conveyor belt (which is in our study a part of an integrated training robotic system) is controlled using one of the artificial intelligence methods, which is the Artificial Neural Network (ANN). A visions sensor will be responsible for gathering information about the status of the conveyor belt and parts over it, where, according to this information, an intelligent decision about the belt speed will be taken by the ANN controller. ANN will control the alteration in speed in a way that gives the optimized energy efficiency through

Three stations were chosen on the water treatment plan of al- madaan .The Samples collected from the (Raw water) and the Sedimentation, filtration and storage water and the drinking water of outlet. Coliform densities T.S and F.C and TS and F.S and total bacterial count as bacteriological pollution indicators, as moste probable number (MPN) method was studied in test. Also some of the chemical characteristics of the water like pH , total suspended solid T.S.S, T.D.D.and S04 , T.Hardness , Ca++ , Mg++ . From the results it were indicated . The study showed the drinking water of outlet (distriputed in system) was agree with WHO criteria and Iraqi limits standards .

Occurrence the heavy metals in water is one of the most important concerns. may cause savior health problems. In this work we made an attempt to know the quantity of six heavy metals in groundwater in different locations of Baghdad city. Examinations were made on groundwater of the review region to assess the heavy metals. Groundwater samples were gathered and analyzed utilizing Atomic Absorption Spectrophotometer for their Manganese, Iron, Zinc, Cadmium, Copper and Lead content and their levels compared with World Health Organization (WHO) specified maximum contaminant level. In order to accomplish this, water samples were obtained from 10 randomly selected wells in the region, in February and August, 2016. The study showed that the ground

Abstract

This Research aims for harnessing critical and innovative thinking approaches besides innovative problem solving tools in pursuing continual quality improvement initiatives for the benefit of achieving operations results effectively in water treatment plants in Baghdad Water Authority. Case study has been used in fulfilling this research in the sadr city water treatment plant, which was chosen as a study sample as it facilitates describing and analyzing its current operational situation, collecting and analyzing its own data, in order to get its own desired improvement opportunity be done. Many statistical means and visual thinking promoting methods has been used to fulfill research task.

With the continuous progress of image retrieval technology, the speed of searching for the required image from a large amount of image data has become an important issue. Convolutional neural networks (CNNs) have been used in image retrieval. However, many image retrieval systems based on CNNs have poor ability to express image features. Content-based Image Retrieval (CBIR) is a method of finding desired images from image databases. However, CBIR suffers from lower accuracy in retrieving images from large-scale image databases. In this paper, the proposed system is an improvement of the convolutional neural network for greater accuracy and a machine learning tool that can be used for automatic image retrieval. It includes two phases

Dora petroleum refinery waste water is the one of the important source of pollution by priority pollutant aromatic compound discharged to Tigris river in Iraq. the station has waste water treatment unit contains many treatment subunits The most important sub units is :skimmer units ,physiochemical unit ,daf unit, biological unit. The aim of research project is to study the ability of unit to remove the priority pollutant aromatic compound and follow up these compounds in river to study ability of river to self removal. A solid phase extraction (SPE) followed by high performance liquid chromatography-ultra violet (HPLC-UV) technique is depicted for the quantitative estimation of benzidines and phenols. Experimental studies were performed to