This study focused on two areas in AL-Najaf city, AL-Ruhbah and Al-Haydariyah regions because of the importance and widespread use of groundwater in these areas. The two areas were compared quantitatively and qualitatively. For the quantitative approach, the GMS software was used in conjunction with the GIS software to simulate the groundwater flow behavior. The solid model for both areas was created, the geological formation was determined, and the hydraulic properties were identified using GMS software. To test the quantity of groundwater in both areas, the wells have been redistributed to a distance of 2000 m between them, and a period of 1000 days was chosen. When a discharge of 10 l/s and operation times of 4, 8, an

This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining

Through the early childhood and after the ablactating the child learns acquired food habbits that might studying with him throughout his life. Here the parents role arises: teaching the child the sound food habits and hygienic styles and whatever beneficial to the health and with the sufficient quantities for the body. In this way the experiences the child learns at home will be of great help in his future life in choosing the suitable food after becoming more dependent in making his decisions and choices away from his parents. The results in this study showed that the averages of the children’s consumption of the high energy foods in comparison with the other highest consumption average , after that comes the con sumption of soft drills

     The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.

     Was conducted to compare the two methods above and it became clear by comparing the  logistic regression model best of a Linear Discriminant  function written

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental