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Synthesis, characterization and Study Bioactivity of Silver Nanocomposites
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The work include synthesis of nanocomposites (X / S / Ag) based on blend from Xanthan gum / sodium alginate polymers (X / S) with different loading of synthesized silver nanoparticales (0.01, 0.03 and 0.05 wt%) were added to the blend. The silver nanoparticles were prepared by reduction method and were characterized and analyzed using X-ray diffraction (XRD) and Atomic force microscope (AFM). XRD study showed the presence nanoparticle of silver with crystalline nature and face-centered cubic (FCC) structure and an average size of nanoparticles ranging from 32 to 37 nm. The surface study was performed using AFM which showed a fairly uniform shape to the nanocomposites and a spherical nature for the silver nanoparticles. The nanocomposite exhibited increase in the thermal stability and as well (X / S /0.05 Ag) nanocomposite film showed superior antimicrobial activity against Proteus, Basileus cyrus, Ecole, staph, pseudomonas, and Basilus subtilius.

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Publication Date
Sun Jan 30 2022
Journal Name
Iraqi Journal Of Science
A Survey on Arabic Text Classification Using Deep and Machine Learning Algorithms
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    Text categorization refers to the process of grouping text or documents into classes or categories according to their content. Text categorization process consists of three phases which are: preprocessing, feature extraction and classification. In comparison to the English language, just few studies have been done to categorize and classify the Arabic language. For a variety of applications, such as text classification and clustering, Arabic text representation is a difficult task because Arabic language is noted for its richness, diversity, and complicated morphology. This paper presents a comprehensive analysis and a comparison for researchers in the last five years based on the dataset, year, algorithms and the accuracy th

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Publication Date
Thu Aug 31 2023
Journal Name
Journal Européen Des Systèmes Automatisés​
An IoT and Machine Learning-Based Predictive Maintenance System for Electrical Motors
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The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

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Publication Date
Fri Jul 01 2016
Journal Name
Composite Structures
Bond behaviour between CFRP laminates and steel members under different loading rates
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Carbon fibre reinforced polymers are widely used to strengthen steel structural elements. These structural elements are normally subjected to static, dynamic and fatigue loadings during their life-time. A number of studies have focused on the characteristics of CFRP sheets bonded to steel members under static, dynamic and fatigue loadings. However, there is a gap in understanding the bonding behaviour between CFRP laminates and steel members under impact loading. This paper shows the effect of different load rates from quasi-static to 300 × 103 mm/min on this bond. Two types of CFRP laminate, CFK 150/2000 and CFK 200/2000, were used to strengthen steel joints using Araldite 420 epoxy. The results show a significant bond strength enhancemen

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Publication Date
Tue Feb 06 2018
Journal Name
International Journal Of Gynecology And Obstetrics
FIGO consensus guidelines on placenta accreta spectrum disorders: Prenatal diagnosis and screening
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Recent population studies have shown that placenta accreta spectrum (PAS) disorders remain undiagnosed before delivery in half to two-thirds of cases. In a series from specialist diagnostic units in the USA, around one-third of cases of PAS disorders were not diagnosed during pregnancy. Maternal

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Scopus
Publication Date
Sun Jun 01 2025
Journal Name
Methodsx
How mathematical models might predict desertification from global warming and dust pollutants
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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine
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Publication Date
Sun Jun 02 2019
Journal Name
Baghdad Science Journal
Fog Computing Resource Optimization: A Review on Current Scenarios and Resource Management
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            The unpredictable and huge data generation nowadays by smart computing devices like (Sensors, Actuators, Wi-Fi routers), to handle and maintain their computational processing power in real time environment by centralized cloud platform is difficult because of its limitations, issues and challenges, to overcome these, Cisco introduced the Fog computing paradigm as an alternative for cloud-based computing. This recent IT trend is taking the computing experience to the next level. It is an extended and advantageous extension of the centralized cloud computing technology. In this article, we tried to highlight the various issues that currently cloud computing is facing. Here

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Publication Date
Sat Mar 14 2020
Journal Name
Medico-legal Update,
Fasting Versus Non-Fasting in Assessing Lipid Profile and Complete Blood Picture
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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection
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Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

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Publication Date
Wed Jan 01 2025
Journal Name
Iraqi Journal Of Materials
Silicon Nitride Nanostructures Prepared from n-type and p-type Silicon Targets
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Silicon nitride nanostructures were prepared by reactive sputtering technique using silicon targets with different types of electrical conductivity (n-type and p-type) and Ar:N2 gas mixing ratio of 70:30. The optical microscopy and spectroscopic characteristics of these films were determined in order to introduce the effect of target conductivity type on these characteristics. The results showed that using p-type silicon target would produce Si3N4 films with lower tendency to adsorb water vapor and other constituents of the atmospheric air, higher absorbance in the visible range 400-700nm, and lower variation in the energy band gap with film thickness than the Si3N4 films prepared from n-type silicon target.

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