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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
Mon Jun 20 2022
Journal Name
Nano Hybrids And Composites
Fabrication and Characterization of Functionalized Multi-Wall Carbon Nanotubes Flexible Network Modified by a Layer of Polypyrrole Conductive Polymer and Metallic Nanoparticles
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Short Multi-Walled Carbon Nanotubes functionalized with OH group (MWCNTs-OH) were used to synthesize flexible MWCNTs networks. The MWCNTs suspension was synthesized using Benzoquinone (BQ) and N, N Dimethylformamide alcohol (DMF) in specific values and then deposited on filter paper by filtration from suspension (FFS) method. Polypyrrole (PPy) conductive polymer doped with metallic nanoparticles (MNPs) prepared using in-situ chemical polymerization method. To improve the properties of the MWCNTs networks, a coating layer of (PPy) conductive polymer, PPy:Ag nanoparticles, and PPy: Cu nanoparticles were applied to the network. The fabricated networks were characterized using an X-ray diffractometer (XRD), UV-Vis. spectrometer, and Ato

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Publication Date
Fri Jun 01 2007
Journal Name
Journal Of Al-nahrain University Science
ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS
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The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Production of protease, an analyst for the blood clot from a variety of fungal isolates / 1 rating fungal isolates and agricultural circles
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House 21 fungal isolates fungus to the analyst Albroca output of manufactured blood clot from the Blama human blood showed positive fungi to test analyzes blood clot variation in times where decomposition recorded fungi

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Publication Date
Mon Jul 01 2024
Journal Name
Journal Of Engineering
Efficient Intrusion Detection Through the Fusion of AI Algorithms and Feature Selection Methods
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With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

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Publication Date
Sat Feb 18 2023
Journal Name
Luminescence
Online column preconcentration for speciation and selective determination of Cr(III) in natural water samples using flow injection with chemiluminescence detection
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Abstract<p>A simple, rapid, sensitive and inexpensive approach is described in this work based on a combination of solid‐phase extraction of 8‐hydroxyquinoline (8HQ), for speciation and preconcentration of Cr(III) and Cr(VI) in river water, and the direct determination of these species using a flow injection system with chemiluminescence detection (FI–CL) and a 4‐diethylamino phenyl hydrazine (DEAPH)–hydrogen peroxide system. At different pH, the two forms of chromium [Cr(III) and Cr(VI)] have different exchange capacities for 8HQ, therefore two columns were constructed; the pH of column 1 was adjusted to pH 3 for retaining Cr(III) and column 2 was adjusted to pH 1 for retaining of Cr(VI). The sorbe</p> ... Show More
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Publication Date
Wed Sep 01 2021
Journal Name
Baghdad Science Journal
A Posteriori L_∞ (L_2 )+L_2 (H^1 )–Error Bounds in Discontinuous Galerkin Methods For Semidiscrete Semilinear Parabolic Interface Problems
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The aim of this paper is to derive a posteriori error estimates for semilinear parabolic interface problems. More specifically, optimal order a posteriori error analysis in the - norm for semidiscrete semilinear parabolic interface problems is derived by using elliptic reconstruction technique introduced by Makridakis and Nochetto in (2003). A key idea for this technique is the use of error estimators derived for elliptic interface problems to obtain parabolic estimators that are of optimal order in space and time.

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Publication Date
Tue May 05 2026
Journal Name
Journal Of Pioneering Medical Sciences
Clinical and Biochemical Indicators Associated with Impaired Kidney Function in Children with Type 1 Diabetes: A Case-Control Study.
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Background: Kidney failure is the leading cause of early death for diabetic patients and is regarded as the most dangerous consequence of the disease. This can only be reversed through early detection and intervention. The incidence of type 1 diabetes, which comprises 5-10 per cent of all diabetic diseases, is rising across the globe with a rate of 15 per 100,000 people per year. Methods: 90 samples were collected and used in this study. Thirty healthy children served as controls (aged 5-15 years), 30 patients with Type 1 Diabetes Mellitus (T1DM, aged 5-15 years) and 30 patients with type 1 diabetic nephropathy (DN), aged 5-20 years). Results: highly significant increase in Urea and Creatinine levels when compared control with DN al

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Publication Date
Sun Mar 01 2020
Journal Name
Baghdad Science Journal
Eyewitnesses’ Visual Recollection in Suspect Identification by using Facial Appearance Model
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Facial recognition has been an active field of imaging science. With the recent progresses in computer vision development, it is extensively applied in various areas, especially in law enforcement and security. Human face is a viable biometric that could be effectively used in both identification and verification. Thus far, regardless of a facial model and relevant metrics employed, its main shortcoming is that it requires a facial image, against which comparison is made. Therefore, closed circuit televisions and a facial database are always needed in an operational system. For the last few decades, unfortunately, we have experienced an emergence of asymmetric warfare, where acts of terrorism are often committed in secluded area with no

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Publication Date
Sun Dec 01 2013
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Strategic dimension for theory of constraints and its impact in decision making: بحث تطبيقي في الشركة العامة للصناعات الجلدية
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The research illustrated that the theory of constraints is   "A group of concepts and basics that aim at helping the management in order to determine the difficulties and how to overcome these difficulties through determining the necessary change and how these change can be done efficiently and effectively ".

       The research showed that the theory of constraint including its tools calls for magnifying throughput, constricting the product cycle , determining  the optimal production mix , utilization of scare resources and canceling the idle time , that achieving  throughput  increasing  then  increasing  profit .

      Moreov

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Decolorization of Phenol Red Dye by Immobilized Laccase in Chitosan Beads Using Laccase - Mediator - System Model
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This work describes the enhancement of phenol red decolorization through immobilizing of laccase in chitosan and enzyme recycling. Commercial laccase from white rot fungus, Trametesversicolor (Tvlac), was immobilizedin to freshly prepared chitosan beads by using glutaraldehyde as a cross linker. Characterization of prepared chitosan was confirmed by FTIR and scanning electron microscope (SEM). Tvlac (46.2 U/mL) immobilized into chitosan beads at 0.8 % glutaraldehyde (v/v) within 24 hrs. Synthetic (HBT) and natural (vanillin) mediators were used to enhance dye decolorizoation. It was found that 89 % of phenol red was decolorized by chitosan beads within 180 min. in the absence of enzyme and mediator, while decolorization percenta

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