The physical sports sector in Iraq suffers from the problem of achieving sports achievements in individual and team games in various Asian and international competitions, for many reasons, including the lack of exploitation of modern, accurate and flexible technologies and means, especially in the field of information technology, especially the technology of artificial neural networks. The main goal of this study is to build an intelligent mathematical model to predict sport achievement in pole vaulting for men, the methodology of the research included the use of five variables as inputs to the neural network, which are Avarage of Speed (m/sec in Before distance 05 meters latest and Distance 05 meters latest, The maximum speed achieved in t

<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

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

أن كرة السلة بما تتضمنه من مهارات حركية متنوعة تتطلب من ممارسيها امتلاك عدد من القدرات الحركية الخاصة وبشكل خاص التوافق العضلي العصبي والرشاقة والقوة المميزة بالسرعة، فضلاعن قدرات الإدراك الحس- حركي (إدراك المكان وقوة دفع الكرة سواء بالطبطبة العالية أو الواطئة .... الخ ) لذا تكمن أهمية البحث في إيجاد الطرق والوسائل التي تطور قدرة الطالبة على إدراك قوة دفع الكرة والإحساس بها أثناء تعلم وتدريب الطبطبة بأنواعها ب

    Biomedical signal such as ECG is extremely important in the diagnosis of patients and is commonly recorded with a noise. Many different kinds of noise exist in biomedical environment such as Power Line Interference Noise (PLIN). Adaptive filtering is selected to contend with these defects, the adaptive filters can adjust the filter coefficient with the given filter order. The objectives of this paper are: first an application of the Least Mean Square (LMS) algorithm, Second is an application of the Recursive Least Square (RLS) algorithm to remove the PLIN. The LMS and RLS algorithms of the adaptive filter were proposed to adapt the filter order and the filter coefficients simultaneously, the performance of existing LMS

Sixteen polycyclic aromatic hydrocarbons (PAHs) concentrations were measured in aerosol samples collected for the period from April 2012 to February 2013 at thermal south power station of Baghdad. Fourty one aerosol sample were extracted with (1:1) dichloromethane and methanol using soxhlet for seventeen hour. The extraction solution was analyzed applying GC/MS. The PAH concentrations outside thermal south power station were higher than those inside it, and higher in summer season than in winter. Naphthalene, pyrene, Anthracene, Indeno [1, 2, 3-cd] pyrene and Phenanthrene were the most abundant PAHs detected in all points at the site sampling. The total polycyclic aromatic hydrocarbon (TPAH) and total suspended particles (TSP) concentrat