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Attention-Deficit Hyperactivity Disorder Prediction by Artificial Intelligence Techniques
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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 were 96.5% and 93.47%, respectively, before applying balancing to the data. In addition, 98.59% and 97.18%, respectively, after applying the balancing technique The extreme gradient boosting (XGBoost) technique had been applied to selecting the important features and the Pearson correlation for finding the correlation between features.

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Publication Date
Mon Jul 01 2019
Journal Name
International Journal Of Computer Science And Mobile Computing
Color Image Compression of Inter-Prediction Base
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Publication Date
Tue Sep 01 2015
Journal Name
European Academic Research
Developing Students` Achievement by Using Self –Observation as a Reflective Teaching Techniques in Conversation Classes at the University Level.
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The present study deals with the effect of self -observation on EFL University students` achievement in conversation classes. The process of self-observation helps the teacher to understand one’s own actions and the reactions in the process of teaching. The sample of this study is EFL students in the third stage at the Department of English Language, morning studies, College of Education /Ibn-Rushd .The sample of the study consists of (84) students distributed on experimental group(A) includes (42) students, and (42) students as control group(B). In order to achieve the aim of the study ,and to gain a closer idea about the impact of reflective teaching technique(self-observation) on the students achievement in conversation classes, a chec

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Publication Date
Sat Oct 01 2022
Journal Name
Baghdad Science Journal
A Crime Data Analysis of Prediction Based on Classification Approaches
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Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or livin

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Publication Date
Sat Dec 01 2018
Journal Name
Journal Of Hydrology
Complementary data-intelligence model for river flow simulation
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Publication Date
Sun Oct 01 2023
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Intelligence framework dust forecasting using regression algorithms models
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<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

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Publication Date
Sun Feb 28 2021
Journal Name
Journal Of Economics And Administrative Sciences
Enhancing national security through foreseeing the economic intelligence system in Iraq
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         Economic intelligence represents a modern field of knowledge that has been and is still the focus of many studies and research, including this research that deals with economic intelligence and its role in strengthening Iraqi national security - an analytical study. Economic development and economic development without security, and in a situation such as that of Iraq, which is still suffering from conflicts, conflicts and the effects of wars, as well as the unstable conditions in its regional environment, the directions of that relationship cannot be determined except through the availability of accurate information, indicators, full knowledge of reality and the pos

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Publication Date
Sun Jan 01 2023
Journal Name
Reviews In Agricultural Science
Technological Advances in Soil Penetration Resistance Measurement and Prediction Algorithms
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Soil compaction is one of the most harmful elements affecting soil structure, limiting plant growth and agricultural productivity. It is crucial to assess the degree of soil penetration resistance to discover solutions to the harmful consequences of compaction. In order to obtain the appropriate value, using soil cone penetration requires time and labor-intensive measurements. Currently, satellite technologies, electronic measurement control systems, and computer software help to measure soil penetration resistance quickly and easily within the precision agriculture applications approach. The quantitative relationships between soil properties and the factors affecting their diversity contribute to digital soil mapping. Digital soil maps use

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Publication Date
Mon Dec 30 2024
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Removal of artificial anionic dye by electrocoagulation and electro-oxidation combined system using aluminum and nano (Cu-Mn-Ni) composite electrodes
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      The combined system of electrocoagulation (EC) and electro-oxidation (EO) is one of the most promising methods in dye removal. In this work, a solution of 200 mg/l of Congo red was used to examine the removal of anionic dye using an EC-EO system with three stainless steel electrodes as the auxiliary electrodes and an aluminum electrode as anode for the EC process, Cu-Mn-Ni Nanocomposite as anode for the EO process. This composite oxide was simultaneously synthesized by anodic and cathodic deposition of Cu (NO3)2, MnCl2, and Ni (NO3)2 salts with 0.075 M as concentrations of each salt with a fixed molar ratio (1:1:1) at a constant current density of 25 mA/cm2. The characteristics structure and surface morphology of the depo

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Publication Date
Tue Sep 01 2015
Journal Name
Journal Of Engineering
Application of Box-Behnken Method Based ANN-GA to Prediction of wt.% of Doping Elements for Incoloy 800H Coated by Aluminizing-Chromizing
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In this work , an effective procedure of Box-Behnken based-ANN (Artificial Neural Network) and GA (Genetic Algorithm) has been utilized for finding the optimum conditions of wt.% of doping elements (Ce,Y, and Ge) doped-aluminizing-chromizing of Incoloy 800H . ANN and Box-Behnken design method have been implanted for minimizing hot corrosion rate kp (10-12g2.cm-4.s-1) in Incoloy 800H at 900oC . ANN was used for estimating the predicted values of hot corrosion rate kp (10-12g2.cm-4.s-1) . The optimal wt.% of doping elements combination to obtain minimum hot corrosion rate was calculated using genetic alg

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Publication Date
Tue Feb 28 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Development prediction algorithm of vehicle travel time based traffic data
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This work bases on encouraging a generous and conceivable estimation for modified an algorithm for vehicle travel times on a highway from the eliminated traffic information using set aside camera image groupings. The strategy for the assessment of vehicle travel times relies upon the distinctive verification of traffic state. The particular vehicle velocities are gotten from acknowledged vehicle positions in two persistent images by working out the distance covered all through elapsed past time doing mollification between the removed traffic flow data and cultivating a plan to unequivocally predict vehicle travel times. Erbil road data base is used to recognize road locales around road segments which are projected into the commended camera

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