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Design of New Hybrid Neural Controller for Nonlinear CSTR System based on Identification
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This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm namely Particle Swarm Optimization (PSO) algorithm. The numerical simulation results show that the hybrid NARMA-L2 controller with PSO algorithm is more accurate than BPA in terms of achieving fast learning and adjusting the parameters model with minimum number of iterations, minimum number of neurons in the hybrid network and the smooth output one step ahead prediction controller response for the nonlinear CSTR system without oscillation.

 

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Publication Date
Sun Feb 10 2019
Journal Name
Iraqi Journal Of Physics
Wear and mechanical prop erties of epoxy/MgO-SiO2 hybrid nanocomposites
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Preparation of epoxy/MgO and epoxy/SiO2 nanocomposites is
studding. The nano composites were processed by different nano
fillers concentrations (0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.07 and
0.1 wt%). Epoxy resin and nanocomposites containing different
shape nano fillers of (MgO:SiO2 composites), are shear mixing with
ratio 1:1,with different nano hybrid fillers concentrations (0.025,
0.05, 0.1, 0.15, 0.2 and 0.25 wt%) to preparation of epoxy/(MgOSiO2)
hybrid nanocomposites. Experimental tests results indicate that
the composite materials have significantly higher modulus of
elasticity than the matrix material but the hybrid nanocomposites
have lower modulus of elasticity. The wear rate was decreased in
nanoc

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Publication Date
Tue Jun 24 2025
Journal Name
Baghdad Science Journal
Accelerating Face Mask Detection Training Model Based on Multi-GPUs and Multi-core CPU
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Modern machine-learning applications require GPUs, and modern platforms can leverage numerous GPUs on one or more machines to increase performance. Contemporary deep-learning models are too huge for CPU or GPU training. Training these models with many GPUs without performance degradation is necessary to train them rapidly and maximize GPU consumption. Thus, training deep convolutional neural networks (DCNN) with multiple GPUs has become necessary for improving training. Therefore, we presented a parallel design and development of an efficient model for enhancing face mask CNN performance and improving resource efficiency. This DCNN model is a parallel training system over multiple GPUs, a multi-core CPU, and a multi-process GPU platform wit

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Publication Date
Mon Dec 13 2010
Journal Name
المجلة البيطرية العراقية
The isolation and identification of the important pathogenic bacteria from fresh meat
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This study was aimed to investigate the load of bacterial contaminant in fresh meat with different types of bacteria.One handered and seven samples were collected from different regions of Baghdad . These samples included 37 of fresh beef 70 of fresh sheep meat. All samples were cultured on different selective media to identitfy of contaminated bacteria .The result revealed that The percentage of bacterial isolate from raw sheep meat were, % 23.8of StreptococcusgroupD,29.4 % of Staphylococcus aureus ,14.7 % of E.coli , %4.9of Salmonella spp, ,%3.5 of pseudomonas aeruginosa, %14.7.%14.7 of Proteus spp.% 2.1 of Listeria spp while the raw beef meat content %5.55 of Staphylococcus aureus, %8.14 of streptococcus group D , %5.18 %1.85 of E.coli,

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Publication Date
Tue Apr 30 2013
Journal Name
International Journal Of Microbiology Research
IDENTIFICATION AND TYPING OF Haemophilus influenzae IN IRAQI CHILDREN DIAGNOSED WITH MENINGITIS
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Publication Date
Tue Jun 20 2023
Journal Name
Bulletin Of The Iraq Natural History Museum
IDENTIFICATION OF HARD TICKS FROM BUFFALO BUBALUS BUBALIS (LINNAEUS, 1758) IN IRAQ
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Ticks (Acari: Ixodidae) are ectoparasites that infest livestock in every geographic region of the world and are vectors of several viral, bacterial, and protozoan pathogens to both animals and humans. There is little information is available is about tick presence in Buffalo Bubalus bubalis (Linnaeus, 1758) (Artiodactyla, Bovidae) in Iraq. The current study determined the species of ticks parasitizing Buffalo in some central and southern regions included: Baghdad (Al Fathelia), Karbala (Al-Hussainia), Wasit (Kut and Al-Suwairah), Al-Qadisia (Al- Diwaniyah, Al- Saniya, Al-Mihnawea, and Afak), Thi Qar (Al-Nasiriyah and Al-chibayish), Missan (Amara and Qalaat Salih) and Basrah (Al-Haretha, Al-Madena and Al-Deer). A total of 150 Buffal

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Isolation and identification of polyhydroxyalkanoates producing bacteria from biopolymers waste in soil
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Abstract<p>The production of polyhydroxyalkanoates PHAs from biopolymer degrading bacteria was examined <italic>in situ</italic> by screening isolates using Sudan B Black staining process as potential PHAs detecting, and Nile Blue staining as a proof method detection. Five bacterial strains isolated from biopolymer waste buried in a garden soil were able to produce high rate of PHA. <italic>AK1P</italic> and <italic>AK2P</italic> strains demonstrated high productivity of biopolymer by converting 5% (w/v) lactose as the only carbon source to PHA during fermentation. <italic>AY2P</italic> strain converted 5% (w/v) of glucose with less PHA accumulation. The f</p> ... Show More
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Publication Date
Thu Feb 01 2018
Journal Name
Applied Mathematical Modelling
Identification of a multi-dimensional space-dependent heat source from boundary data
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Publication Date
Sat May 01 2021
Journal Name
Iop Conference Series: Earth And Environmental Science
Isolation and Identification of Alkaline Protease Producing Aspergills niger from Iraqi Soils
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Abstract<p>Twenty purified isolates were obtained by using different soil sources, only twelve isolates belonging to Aspergillus genera depending on cultural and morphological characterization. The isolates were used as alkaline protease producer. The highest proteolytic, enzymatic activity (95.83U/ml) was obtained from <italic>Aspergillus</italic> sp. ZE isolate. This isolate was identified by 5.8 rRNA gene sequencing as <italic>Aspergills niger</italic> (accuracy of 99%), which was matched with the sequence of <italic>Aspergills niger</italic> strain GM775228 recorded in Gene bank under the ID: GM 775228.1.</p>
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Publication Date
Wed Apr 01 2020
Journal Name
Indian Journal Of Ecology
Isolation and molecular identification of yersinia entercolitica in Bovine Meat in Iraq
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Publication Date
Mon Nov 21 2022
Journal Name
Sensors
Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets
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Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

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