The aim of this paper is to design suitable neural network (ANN) as an alternative accurate tool to evaluate concentration of Copper in contaminated soils. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Copper. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this study show that the ANN technique trained on experimental measurements can be successfully applied to the rapid est

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A

Breast cancer is one of the most common cancers in females. In Iraq there are noticeable elevation in incidence rates and prevalence of advanced stages of breast cancer. Ferritin is intracellular iron storage protein abundant in circulation and its main application in differential diagnosis of anemia.

The level of serum ferritin was found raised in various cancers including breast cancer. The aim of this study was to assess whether the serum ferritin concentration would be altered in Iraqi women with breast cancer and it could be related to progression of disease.

Sixty eight females participated in this study. The mean age of these females was 53.25± 9.52 .The level of serum ferritin was measured in 24

Ad-Hoc Networks are a generation of networks that are truly wireless, and can be easily constructed without any operator. There are protocols for management of these networks, in which the effectiveness and the important elements in these networks are the Quality of Service (QoS). In this work the evaluation of QoS performance of MANETs is done by comparing the results of using AODV, DSR, OLSR and TORA routing protocols using the Op-Net Modeler, then conduct an extensive set of performance experiments for these protocols with a wide variety of settings. The results show that the best protocol depends on QoS using two types of applications (+ve and –ve QoS in the FIS evaluation). QoS of the protocol varies from one prot

In this paper, the computational complexity will be reduced using a revised version of the selected mapping (SLM) algorithm. Where a partial SLM is achieved to reduce the mathematical operations around 50%. Although the peak to average power ratio (PAPR) reduction gain has been slightly degraded, the dramatic reduction in the computational complexity is an outshining achievement. Matlab simulation is used to evaluate the results, where the PAPR result shows the capability of the proposed method.  

Background: Breast cancer is the most common malignancy affecting the Iraqi population and the leading cause of cancer related mortality among Iraqi women. It has been well documented that prognosis of patients depends largely upon the hormone receptor contents and HER-2 over expression of their neoplasm. Recent studies suggest that Triple Positive (TP) tumors, bearing the three markers, tend to exhibit a relatively favorable clinical behavior in which overtreatment is not recommended. Aim: To document the different frequencies of ER/PR/HER2 breast cancer molecular subtypes focusing on the Triple Positive pattern; correlating those with the corresponding clinico-pathological characteristics among a sample of Iraqi patients diagnosed with th

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to