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

The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popular applications of clustering in data mining, and it is used to identify useful patterns and interesting distributions in the underlying data. Aggregation methods for classifying nonlinear aggregated data. In particular, DNA methylations, gene expression. That show the differentially skewed by distance sites and grouped nonlinearly by cancer daisies and the change Situations for gene excretion on it. Under these conditions, DBSCAN is expected to have a desirable clustering feature i that can be used to show the results of the changes. This research reviews the DBSCAN and compares its performance with other algorithms, such as the tradit

The main objective of this study is to determine the suitable excitation wavelengths for
urine components reaching to select the suitable lasers to execute the auto fluorescence due to their
high intensities. The auto fluorescence was measured at 305, 325 and 350 nm excitation wavelengths
for eleven urine samples which were also analyzed by conventional methods (chemical and
microscopic examination). Data manipulation using Matlab package programming language showed
that urine sample with normal chemical and biological components have emission peaks which are
different from the infected urine samples. Despite the complexity of the composition of urine,
fluorescence maxima can be observed. Most likely, the peaks obser

The main objective of this study is to determine the suitable excitation wavelengths for
urine components reaching to select the suitable lasers to execute the auto fluorescence due to their
high intensities. The auto fluorescence was measured at 305, 325 and 350 nm excitation wavelengths
for eleven urine samples which were also analyzed by conventional methods (chemical and
microscopic examination). Data manipulation using Matlab package programming language showed
that urine sample with normal chemical and biological components have emission peaks which are
different from the infected urine samples. Despite the complexity of the composition of urine,
fluorescence maxima can be observed. Most likely, the peaks obser

The research work represent a fast and simple method for the determination of methionine using chemiluminescence for the methionine-sodium hydroxide-luminol for the generation of a chemiluminesecent derivative of luminal. The emission was measured by continuous flow analysis made sample size of 83µL was used.Response versus concentration extended from 0.2-20 mM.L-1 with a percentage linearity of 96.17% or with 99.17% percentage of linearity for the range 0.6-20 mM.L-1. Reaching to a L.O.D. at (S/N=3) for 5 µM.L-1 from the gradual dilution for the minimum concentration in the calibration graph with a repeatability of less than 0.5% (n=10). A comparison was made between the new developed method with the classical method for the spectrophoto

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor

Background: The aim of this study was to evaluate the expression of ?broblast growth factor-2 and Heparanase in oral squamous cell carcinoma, and to correlate the two studied marker with each other and with clinicopathologicalfinding including grade, stage. Methods: Sections of 30 formalin-fixed paraffin embedded blocks specimens of oral squamous cell carcinoma were immunostained to assess the expression of ?broblast growth factor-2 and Heparanse in oral squamous cell carcinoma cases. Results: The expression of fibroblast growth factor-2 and Heparanase were positive in all oral squamous cell carcinoma cases (100%). The positive expression of fibroblast growth factor-2 was significantly correlated with tumor site (p=0.016),and clinical pres