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Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder

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Publication Date
Sat Sep 30 2023
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
A proposed model program to audit the contribution of municipal institutions to achieving the environmental dimension of sustainable development "applied research"
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Abstract

               The research aimed to prepare an audit program focusing on the activities of municipal institutions related to the environmental dimension as one of the dimensions of sustainable development, and applying the program for the purpose of preparing an oversight report related to assessing the impact of the activities of municipal institutions on the environmental reality as the main channel through which municipal institutions contribute to achieving the part related to it. Among the requirements of sustainable development, the proposed program was prepared and applied to the institutions affiliated to the Directorate of Mu

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Publication Date
Fri Feb 08 2019
Journal Name
Iraqi Journal Of Laser
Laser Induced Fluorescence Spectroscopy of Decamethylrhenocene ( - C5 Me5)2 Re in Supersonic Jet
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In this paper we report the use of supersonic jet laser induced fluorescence (LIF) spectroscopy to facilitate the study of the spectra of some organometallic sandwich compounds particularly the metallocenes. The charge-transfer processes within these compounds, especially ligand to metal charge transfer within decamethylrhenocene ( - C5 Me5)2 Re were of particular interest. The spectrum shows a high degree of structures, indicating that there are several levels populated and these molecules are able to undergo many possible transitions

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Publication Date
Thu Oct 01 2015
Journal Name
Journal Of Educational And Psychological Researches
Posttraumatic cognitions and its Relationship with Posttraumatic Stress Disorder of Traumatized Individuals
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Symptoms of posttraumatic stress disorder are associated with various variables such as the exposure to traumatic events, sex and age. Such events could lead to negative cognitions towards self and the world. These cognitions, in turn, may lead to traumatic related disorders.

The present study aims to identify the percentage of traumatised individuals according to sex and age category variables. It also aims to assess the average of spreading symptoms of posttraumatic stress disorder of traumatised individuals according to sex and age category variables. Likewise, it aims to test variables significance in cognitions towards the world and the self according to the level of the spread of posttraumatic stre

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Publication Date
Fri Aug 01 2014
Journal Name
2014 36th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society
Spectral analysis of resting state magnetoencephalogram activity in patients with bipolar disorder
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Publication Date
Wed Jan 21 2026
Journal Name
Al–bahith Al–a'alami
EMPLOYING TWITTER IN THE U.S. PRESIDENTIAL ELECTION CAMPAIGN IN 2016: U.S. PRESIDENTIAL CANDIDATE DONALD TRUMP’S TWEETS AS A “MODEL”: (A Research Drawn from a Master’s Thesis) (An Analytical Study)
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This study aimed to identify the employment of the social networking platform «Twitter» in the 2016 presidential campaign led by the Republican candidate, Donald Trump; and analyse his tweets through his personal account on «Twitter»  for the period from: 10/ 8/2016 to: 11/ 8/2016 which represents the last month of the election campaign.

The study belongs to the type of descriptive studies using the analytical method through an analysis index that includes sub-categories and other secondary categories. The research has adopted the ordinary unit of information material (tweet) as an analysis unit for this purpose.

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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Increasing validation accuracy of a face mask detection by new deep learning model-based classification
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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

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Publication Date
Fri Jul 27 2012
Journal Name
Journal Of Prosthodontics
A Three-Dimensional Finite Element Analysis for Overdenture Attachments Supported by Teeth and/or Mini Dental Implants
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Publication Date
Mon Apr 10 2023
Journal Name
Journal Of Oral And Dental Research
Prevalence of Temporomandibular Joint Disorder among Dental Students of the University of Baghdad
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Publication Date
Tue Dec 28 2021
Journal Name
2021 2nd Information Technology To Enhance E-learning And Other Application (it-ela)
Pedestrian and Objects Detection by Using Learning Complexity-Aware Cascades
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Publication Date
Mon May 01 2017
Journal Name
Australian Journal Of Basic And Applied Sciences
Sprite Region Allocation Using Fast Static Sprite Area Detection Algorithm
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Background: Sprite coding is a very effective technique for clarifying the background video object. The sprite generation is an open issue because of the foreground objects which prevent the precision of camera motion estimation and blurs the created sprite. Objective: In this paper, a quick and basic static method for sprite area detection in video data is presented. Two statistical methods are applied; the mean and standard deviation of every pixel (over all group of video frame) to determine whether the pixel is a piece of the selected static sprite range or not. A binary map array is built for demonstrating the allocated sprite (as 1) while the non-sprite (as 0) pixels valued. Likewise, holes and gaps filling strategy was utilized to re

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