In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g

Abstract

This research  aims to reform the Iraqi public budget through going into the challenges the budget faces in applying item-line budget in its preparation, implementation and control; which encourage extravagance and waste instead of rationalizing expenditures. This  is shown in the data analysis of Federal public budget laws in Iraq for the years from 2005 till 2013; there was a continuous increase in the aggregate public expenditures in the public budget for the years previously mentioned, as the public expenditures growth has reached into the percent 284.71% in 2013. In addition the public budget for these years (2005-2013) is being prepared with planned deficit without confirming that

Mishrif Formation is the main reservoir in Amara Oil Field. It is divided into three units (MA, TZ1, and MB12). Geological model is important to build reservoir model that was built by Petrel -2009. FZI method was used to determine relationship between porosity and permeability for core data and permeability values for the uncored interval for Mishrif formation. A reservoir simulation model was adopted in this study using Eclipse 100. In this model, production history matching executed by production data for (AM1, AM4) wells since 2001 to 2015. Four different prediction cases have been suggested in the future performance of Mishrif reservoir for ten years extending from June 2015 to June 2025. The comparison has been mad

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

Background:Pediatric acute myeloid leukemia (AML) has a poor prognosis, and novel therapies are needed. The FLT3 tyrosine kinase inhibitorsrepresents a promising target in pediatric AML.
Objectives:This study was done to estimate the frequency of FLT3- ITD mutation in childhood acute myeloid leukemia using conventional PCR & correlate this mutation with the clinical presentation and response to induction therapy.
Patients, Materials &Methods: Twenty children with AML, and 16 children with reactive bone marrow as negative control were enrolled in this study. Those patients were attending Child Welfare Teaching Hospital in Baghdad from March 2010 to July 2011 .For each patient hematological investigations including complete b