Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

The research aimed to use HIIT exercises, and to know the effect of HIIT exercises on some physiological and physical indicators of the young badminton players, and to identify the degree of competition anxiety and the performance of some offense skills among the young badminton players. The research community (the young badminton players), the research sample and its selection method (the research sample was chosen by the intentional method (8) badminton player from the Athwari Club), the scientific method (the experimental method with pre and post tests), measurement tools: physiological tests (high and low blood pressure) , pulse, and physical exams (explosive force of arms and legs) and the offense skills and the scale of competit

The research aimed to use HIIT exercises, and to know the effect of HIIT exercises on some physiological and physical indicators of the young badminton players, and to identify the degree of competition anxiety and the performance of some offense skills among the young badminton players. The research community (the young badminton players), the research sample and its selection method (the research sample was chosen by the intentional method (8) badminton player from the Athwari Club), the scientific method (the experimental method with pre and post tests), measurement tools: physiological tests (high and low blood pressure) , pulse, and physical exams (explosive force of arms and legs) and the offense skills and the scale of competition an

Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS, 

In this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.

Background: Diabetes mellitus is a metabolic disorder affecting people worldwide, which require constant monitoring of their glucose levels. Commonly employed procedures include collection of blood or urine samples causing discomfort to the patients. Necessity arises to find alternative non invasive technique is required to monitor glucose levels. Saliva is one of most abundant secretions in the human body and its collection is easy, noninvasive and painless technique. Objective: The aim of this study was to determine the efficacy of saliva as a diagnostic tool by study the correlation between blood and salivary glucose levels and glycosylated hemoglobin (HbA1c%) in diabetes and non diabetes, and the comparison of salivary glucose level

Iraq within the ranks of the fledgling communities characterized by a broad base of the population pyramid, because they pose the age group (under 15 years) of a large proportion of the community, as it exceeded the proportion (40%) during the years of research extended (1986-2010) Despite the relative decline in the rates fertility during that period, but the proportion of young people remained high, especially for groups of at least five years, amounting to about 14% in 2012, a little more than the proportion of what constitutes age group (5-9 above) years, where it was (13%) and this naturally predicts continuing population increases in coming decades, due to the entry of those numbers of individuals in the reproductive stage,