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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
Wed Aug 28 2024
Journal Name
World Academy Of Sciences Journal
Serum angiopoietin‑1 as a biomarker of missed miscarriage
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Publication Date
Sun Sep 01 2013
Journal Name
Baghdad Science Journal
Biochemical Study of Gonad Hormones in Sera of Iraqi Patients with Thyroid Disorder
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The objective of this study was to evaluate the alteration in levels of gonado trophins hormones i.e.,Leutizing (LH),Follicular(FSH) in sera of patients with thyroid disorders and molecular binding study of (LH ,FSH) with their antibodies The study was conducted at the specialized center for endocrinology and diabetes from January / 2009 to March / 2010.Two hundreds and twenty three Iraqi subjects, 109 patients with thyroid disorders at age range between (40-50) years and 114 healthy individuals as control group were included in this study.The majority of patients were female with hyperthyroidism and (49.54 % ) were at age range between(40 - 50) years. The levels of hormones(LH,FSH.tri iodothyronine(T3).thyroxine(T4), thy

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Publication Date
Sun Jul 01 2018
Journal Name
Comprehensive Psychiatry
Complexity analysis of spontaneous brain activity in mood disorders: A magnetoencephalography study of bipolar disorder and major depression
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Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
Minimum Spanning Tree Algorithm for Skin Cancer Image Object Detection
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This paper proposes a new method Object Detection in Skin Cancer Image, the minimum
spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the
structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer
Images only. The Skin Cancer Image Detection of test objects relies on their distances to the
closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs
especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer
Image.
The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on
different Skin Cancer Images. We obtained very good results . The experiment showed that

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Publication Date
Fri Jul 05 2013
Journal Name
Hemoglobin
The Spectrum of β-Thalassemia Mutations in Baghdad, Central Iraq
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Publication Date
Wed Dec 27 2017
Journal Name
Al-khwarizmi Engineering Journal
A New Structure for Cascaded Single-Stage Distributed Amplifier Using Proposed Active Inductor Loads
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A modification to cascaded single-stage distributed amplifier (CSSDA) design by using active inductor is proposed. This modification is shown to render the amplifier suitable for high gain operation in small on-chip area. Microwave office program simulation of the Novel design approach shows that it has performance compatible with the conventional distributed amplifiers but with smaller area. The CSSDA is suitable for optical and satellite communication systems.

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Publication Date
Sun Mar 31 2013
Journal Name
Inventi Impact: Artificial Intelligence
SIMULATION OF IDENTIFICATION AND CONTROL OF SCARA ROBOT USING MODIFIED RECURRENT NEURAL NETWORKS
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This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett

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Publication Date
Sun Jan 01 2012
Journal Name
Journal Of Educational And Psychological Researches
اضطراب ما بعد الضغوط الصدمية والعنف لدى طلبة الإعدادية
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Human beings are under the threat of the environment they are living in. With the course of time, they seriousness of this threat and its effect in changing their life . The most common threat that human beings might pass through is the trauma which is commonly bound with wars. The greater trauma that human beings might have is the threatening sudden facing or death facing. Consequently there would be disorder especially War disorder. This Phenomenon is clearly observed in Iraqi society as Iraq has been passing through continuous wars and disasters namely during and after the American Invasion. These events might lead to changes in Peoples  behavior and to Social Violence. Thus, the Study aims at:

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Publication Date
Fri Jan 01 2016
Journal Name
International Journal Of Wireless And Mobile Computing
A comprehensive simulation study of a network-based distributed mobility management framework
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Publication Date
Sun Dec 01 2024
Journal Name
Nordic Concrete Research
Evaluating Concrete Strength Under Various Curing Conditions Using Artificial Neural Networks
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Abstract<p>This study examines the impact of different curing methods on the compressive strength of concrete. It investigates techniques such as air curing, periodic water spraying, full water submersion, and polyethylene encasement. Artificial neural network models were employed to evaluate the compressive strength under each curing condition. A model for calculating compressive strength that considers surrounding conditions was created using an artificial neural network. The current study’s figures were generated using this model. The research thoroughly examined the impact of curing environments and concrete mix components on strength properties, taking into account factors such as tempera</p> ... Show More
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