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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
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
Sat Jan 01 2022
Journal Name
Mustansiriya Medical Journal
Molecular Detection of Agglutinin-Like Sequence 1 Gene in Candida albicans that is Isolated from Diabetic Foot Patients
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Objectives:

Candida albicans is a microbe living within the natural human flora and is found in the upper respiratory tract, mouth, intestines, and vagina. C. albicans is able to cause infections that range from superficial infections of the skin to life-threatening systemic infections.

Aim of Study:

Detection of virulence gene agglutinin-like sequence (ALS) 1 by using molecular technology from clinical samples (C. albicans

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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Application of Artificial Neural Network for Predicting Iron Concentration in the Location of Al-Wahda Water Treatment Plant in Baghdad City
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Iron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies.  In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul

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Publication Date
Tue Jun 01 2021
Journal Name
Al-khwarizmi Engineering Journal
The The Application of Virtual Reality to (Mechatronics Engineering) by Creating an Articulated Robotic Work Cell Using EON Reality V9.22.24.24477
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Virtual reality, VR, offers many benefits to technical education, including the delivery of information through multiple active channels, the addressing of different learning styles, and experiential-based learning. This paper presents work performed by the authors to apply VR to engineering education, in three broad project areas: virtual robotic learning, virtual mechatronics laboratory, and a virtual manufacturing platform. The first area provides guided exploration of domains otherwise inaccessible, such as the robotic cell components, robotic kinematics and work envelope.  The second promotes mechatronics learning and guidance for new mechatronics engineers when dealing with robots in a safe and interactive manner. And the thir

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Publication Date
Thu Aug 01 2024
Journal Name
Water Practice & Technology
Artificial neural network and response surface methodology for modeling oil content in produced water from an Iraqi oil field
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ABSTRACT<p>The majority of the environmental outputs from gas refineries are oily wastewater. This research reveals a novel combination of response surface methodology and artificial neural network to optimize and model oil content concentration in the oily wastewater. Response surface methodology based on central composite design shows a highly significant linear model with P value &lt;0.0001 and determination coefficient R2 equal to 0.747, R adjusted was 0.706, and R predicted 0.643. In addition from analysis of variance flow highly effective parameters from other and optimization results verification revealed minimum oily content with 8.5 ± 0.7 ppm when initial oil content 991 ppm, tempe</p> ... Show More
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Publication Date
Wed Feb 01 2023
Journal Name
Journal Of The World Federation Of Orthodontists
Validity and reliability of three-dimensional modeling of orthodontic dental casts using smartphone-based photogrammetric technology
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Publication Date
Mon Apr 20 2026
Journal Name
International Journal Of Intelligent Engineering And Systems
A Robust Base-layer Design for Hierarchical IoT Intrusion Detection Using Hybrid Deep Learning
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The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines

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Publication Date
Sat Dec 31 2022
Journal Name
International Journal Of Intelligent Engineering And Systems
Using Three-Dimensional Logistic Equations and Glowworm Swarm Optimization Algorithm to Generate S-Box
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Publication Date
Sun Feb 12 2017
Journal Name
World J Exp Biosc
Detection and sequencing of blaVEB-1 gene in clinical isolates of Proteus mirabilis Isolates from Baghdad City`s hospitals
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In Present study, 25 clinical isolates of Proteus spp. of clinical samples, urine, wounds and burns collected from different hospitals in Baghdad city, all isolates were identified as Proteus mirabilis using different bacteriological media, biochemical assays and Vitek-2 system. It was found that 15 (60%) isolates were identifying as P. mirabilis. The susceptibility of P. mirabilis isolates to cefotaxime was 66.6 %, while to ceftazidime was 20%. Extended spectrum β-lactamses producing Proteus was 30.7 %. DNA of 5 isolates of P. mirabilis was extracted and detection for blaVEB-1 gene by using multiplex polymerase chain reaction (PCR). Results showed that the presence of this gene in all tested isolates, as an important indicator for increas

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Publication Date
Thu Mar 25 2021
Journal Name
International Journal Of Drug Delivery Technology
A comparative study of retinol-binding protein-4 and progranulin in iraqi women with thyroid disorder
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Thyroid hormones (TH) regulate the metabolic processes required for normal development and growth; also, to organizemetabolism in adults, any defect in thyroid function leads to abnormality in thyroid hormones level. The current study hasbeen designed to find the relationship between retinol-binding protein-4 and progranulin in the serum of Iraqi women withhypothyroidism and hyperthyroidism, also, to study whether these patients are exposed to a risk of developing diabetes mellitus,and PGRN may be a biomarker in detection early stage of diabetes mellitus.Materials and Methods: in this study, serum samples were obtained from 50 Iraqis women patients, [25 patients withhypothyroidism (G2) and 25 patients with hyperthyroidism (G3)] in addition

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