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

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

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

Given the importance of possessing the digital competence (DC) required by the technological age, whether for teachers or students and even communities and governments, educational institutions in most countries have sought to benefit from modern technologies brought about by the technological revolution in developing learning and teaching and using modern technologies in providing educational services to learners. Since university students will have the doors to work opened in all fields, the research aims to know their level of DC in artificial intelligence (AI) applications and systems utilizing machine learning (ML) techniques. The descriptive approach was used, as the research community consisted of students from the University

Background: Educational environment is one of the most important determinants of an effective curriculum. Students' perceptions of their educational environment have a significant impact on their behavior and academic progress. Objective: 1. To identify students’ perception to the educational environment.2. To identify any gender or class level differences in the students’ perception.Type of the study: This is a descriptive cross-sectional studyMethodology: The study was carried out on convenient sample of 150 students of 2nd and 5th grade. This study was done in Al Kindy Medical College, Baghdad, Iraq and conducted during the period from the 1st of October 2013 till the end of March 2014, by using DREEM questionnaire a validated uni

  Many managers in geometrical and technical organizations prefer to deal with quantitative values ​​to choose between the available options and choose the best alternative to avoid randomization and bias in decision making. One of them Baghdad Water Department, which seeks to develop the quality of its product (drinking water) and achieve its objectives under increasing growing population and the demand for water, Some of TQM tools, especially the statistical, have this ability because there is chance to use historical data and experiment of employees in Application . Two statistical tools were applied: the nominal group technique, matrix data analysis technique as well as the brainstorming tool to search for the best o