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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
Wed Mar 16 2022
Journal Name
Journal Of Educational And Psychological Researches
Awareness of Diagnosing Autism Spectrum Disorders and Social (Pragmatic) Communication Disorder among Student Teachers According to Some Variables
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The research aims to identify the level of awareness of student teachers in the behavioral disorders and autism specialization about the diagnosing Autism Spectrum Disorder and Social (Pragmatic) Communication Disorder according to some variables. The study was conducted on a sample of (113) student teachers. The researcher employed the awareness scale of a teacher-screening questionnaire for autism spectrum disorder and social pragmatic communication disorder. The results showed that the average of teachers in the total degree of awareness of autism spectrum disorder and social communication have recorded a moderate degree. As for the awareness of autism spectrum disorder was high. Then, the awareness of social communication disorder wa

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Indian Academy Of Oral Medicine And Radiology
The Degenerative Condylar Change in Temporomandibular Joint Disorder of Iraqi Population and their Variability Based on Clinical Diagnosis
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Background: As a multifactorial disorder, temporomandibular joint (TMD) is difficult to diagnose, and multiple factors affect the joint and cause the temporomandibular disorder. Standardization of clinical diagnosis of TMD should be used to reach a definite clinical diagnosis; the condylar bone may degenerate in accordance with these disorders. Aims: Evaluate the correlation between the clinical diagnosis and degenerative condylar change (flattening, sclerosis, erosion, and osteophyte). Materials and Methods: A prospective study with a study group of 97 TMD patients (total of 194 joints) aged 20 to 50. Patients were sent to cone beam computed tomography (CBCT) to assess the degenerative condylar change. Results: No association was found bet

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Publication Date
Sat Jan 01 2011
Journal Name
Journal Of Engineering
FILTRATION MODELING USING ARTIFICIAL NEURAL NETWORK (ANN)
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In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

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Publication Date
Sat Nov 03 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Prevalence of Hypochondriasis Disorder among Iraqi Former Prisoners of Iraq-Iran War, 1980-1988
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Objective : To find out the prevalence of Hypochondriasis among Iraqi repatriated prisoners of
Iraq-Iran war, and the relationship with some variables.
Methodology: A descriptive study was carried out from Jan. 2nd , 2006 through May 4th , 2006. A
non-probability accidental sample of 400 repatriates who had visited; Ministry of Human Rights,
Ministry of Health, and Ministry of Defense. A questionnaire was constructed for this purpose, which
consisted of 6 items for demographic data, and 14 items for measuring Hypochondriasis. Reliability
and validity of the questionnaire had been determined through the pilot study (Test and retest) and the
experts panel. Data were collected with using the constructed questionnaire an

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Publication Date
Tue Jan 01 2019
Journal Name
Indian Journal Of Public Health Research & Development
Post-Traumatic Stress Disorder among Women with Breast Cancer in Iraq: A Preliminary Report
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Publication Date
Sun Mar 01 2020
Journal Name
Journal Of Engineering
Wellbore Breakouts Prediction from Different Rock Failure Criteria
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One of the wellbore instability problems in vertical wells are breakouts in Zubair oilfield. Breakouts, if exceeds its critical limits will produce problems such as loss circulation which will add to the non-productive time (NPT) thus increasing loss in costs and in total revenues. In this paper, three of the available rock failure criteria (Mohr-Coulomb, Mogi-Coulomb and Modified-Lade) are used to study and predict the occurrence of the breakouts. It is found that there is an increase over the allowable breakout limit in breakout width in Tanuma shaly formation and it was predicted using Mohr-Coulomb criterion. An increase in the pore pressure was predicted in Tanuma shaly formation, thus; a new mud weight and casing pr

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Publication Date
Mon Jan 01 2024
Journal Name
Jordanian Journal Of Computers And Information Technology
BEYOND WORDS: HARNESSING SPEECH SOUND FOR SPEAKER AGE AND GENDER DETECTION USING 1D CNN ARCHITECTURE WITH SELF-ATTENTION MECHANISM
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Beyond the immediate content of speech, the voice can provide rich information about a speaker's demographics, including age and gender. Estimating a speaker's age and gender offers a wide range of applications, spanning from voice forensic analysis to personalized advertising, healthcare monitoring, and human-computer interaction. However, pinpointing precise age remains intricate due to age ambiguity. Specifically, utterances from individuals at adjacent ages are frequently indistinguishable. Addressing this, we propose a novel, end-to-end approach that deploys Mozilla's Common Voice dataset to transform raw audio into high-quality feature representations using Wav2Vec2.0 embeddings. These are then channeled into our self-attentio

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Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Emotional Intelligence and Leadership Personality for Managers
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The purpose of this research is to analyze the relationship between the emotional intelligence and the leadership personality of the managers . the research was tested at the college of administration and economics – university of Baghdad through applying it on a sample of (67) members and units of the college. a questionnaire was used as a major tool for collecting data and information . for the purpose of researching to conclusion, the research aimed to test two main hypotheses related to the correlation coefficient and the effect correlation between the two main variable of the research, some statistical techniques such as (the mean, student deviation, percentages, correlation coefficient spearman, simple regression) were us

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Publication Date
Thu Sep 01 2022
Journal Name
Computers And Electrical Engineering
Automatic illness prediction system through speech
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Publication Date
Wed Feb 01 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Bitcoin Prediction with a hybrid model
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In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

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