Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

In this research, the effect of changing the flood level of Al-Shuwaija marsh was studied using the geographic information systems, specifically the QGIS program, and the STRM digital elevation model with a spatial analysis accuracy of 28 meters, was used to study the marsh. The hydraulic factors that characterize the marsh and affecting on the flooding such as the ranks of the water channels feeding the marsh and the degree of slope and flat areas in it are studied. The area of immersion water, the mean depth, and the accumulated water volume are calculated for each immersion level, thereby, this study finds the safe immersion level for this marsh was determined.

Total of 170 samples were collected from Al-Chibayish Marsh reality in Dhi-Qar governorate southern of Iraq to study the epidemology of viral hepatitis in these areas and to detect the type of hepatitis viruses which include A ,B,C,D .The percentage of hepatitis A was 1.17% and most of them below age of ?10 (66.6%) while infection with hepatitis B account 5.29% and includes all age groups. There was no detected cases of hepatitis C,D. The laboratory study showed that the incidence of hepatitis B higher in male (4.11%) compared to female ( 2.35%)

    Specimens of the sesarmid crab Nanonsesarma sarii  (Naderloo and Türkay 2009) were    collected from the intertidal zone of Khor Al-Zubair, Basrah, Iraq 2012 far from the Arabian Gulf coasts. Morphological features of this species are highlighted and a figure is provided. 

With the continuous progress of image retrieval technology, the speed of searching for the required image from a large amount of image data has become an important issue. Convolutional neural networks (CNNs) have been used in image retrieval. However, many image retrieval systems based on CNNs have poor ability to express image features. Content-based Image Retrieval (CBIR) is a method of finding desired images from image databases. However, CBIR suffers from lower accuracy in retrieving images from large-scale image databases. In this paper, the proposed system is an improvement of the convolutional neural network for greater accuracy and a machine learning tool that can be used for automatic image retrieval. It includes two phases

Background: This study aimed to determine the amount of fluoride in commercially available bottled drinking water in Al-Basra city, Iraq Materials and Methods: Eleven brands of bottled drinking water were obtained from supermarkets in Al-Basra city, Iraq. Five samples of 10 ml. were taking from each one of brands and the fluoride was determined by using fluoride ion selective electrode. Results: The highest fluoride concentration was present in BADIOT brand (1.174 mg/L) while the lowest was in Barakat brand (0.038 mg/L). One way ANOVA test showed a highley significant difference among different commercially branded types. Coclusions: Bottled water available in Al-Basra city contains less concentration of fluoride ion than normal values

The aim of this paper is to determine the significant levels of some heavy metals such: lead, chromium, nickel and cadmium, were determined. Sources of pollution and their distribution according to presence of elements in the soils over the whole zone of the province of Maysan in southern of Iraq were investigated 36 soil samples from different zones: residential, industrial, commercial, agricultural and main roads, were collected from the soil surface and a depth of 30 cm and analyzed measuring of concentrations for heavy metals by a device ICP-MS technique. The results were compared with global standard levels of these elements in the soil.

Some major pollutants of polycyclic aromatic hydrocarbons (PAH) those discharged as water produced (WP) from the AlAhdab oil field (AOF) in the ponds close to it may leak to the water resources around and eventually reaches the marshes which will affect its ecosystem. Thus, this work aims to track the availability of PAH in the water resources and the Main Outfall Drain (MOD) nearby. The determination of PAH was evaluated using “High-Performance Liquid Chromatography (HPLC)”. The mean concentration of sixteen PAH in the produced water within the field was relatively high (0.01 to 10.89 g/ml) with standard deviations of (0.10.9). While, PAH outside the field were gradually diminishes down to (0.01-0.039) x10-2 g/ml which exceeds th

Abstract

This research aim to overcome the problem of dimensionality by using the methods of non-linear regression, which reduces the root of the average square error (RMSE), and is called the method of projection pursuit regression (PPR), which is one of the methods for reducing dimensions that work to overcome the problem of dimensionality (curse of dimensionality), The (PPR) method is a statistical technique that deals with finding the most important projections in multi-dimensional data , and With each finding projection , the data is reduced by linear compounds overall the projection. The process repeated to produce good projections until the best projections are obtained. The main idea of the PPR is to model