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Detection of hydrolytic enzymes produced by Azospirillum brasiliense isolated from root soil
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Publication Date
Thu Apr 07 2016
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
DETECTION OF MICROBIAL CONTAMINATION IN SOME TYPES OF FLOUR THAT AVAILABLE IN LOCAL MARKETS USING BACTRAC DEVICE 3400: DETECTION OF MICROBIAL CONTAMINATION IN SOME TYPES OF FLOUR THAT AVAILABLE IN LOCAL MARKETS USING BACTRAC DEVICE 3400
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A microbial study conducted for a number of flour samples (30 samples) Uses in the bakery ovens in various areas of the city of Baghdad, by used the conventional methods used in laboratories in microbial tests and compared with the modern techniqueby usedof BacTrac Device 3400 equipped from SY-LAB Impedance analysersAustrian company.The results of two ways showed (The conventional way and BacTrac Device test)that the total counts of aerobic bacteria, coliform bacteria, StaphylococcusSpp. bacteria, Bacillus cereus bacteria and yeasts and molds,Most of them were within the permissible borders in the Iraqi standard for grain and its products With free samples from SalmonellaSpp. bacteria, and that the screening by BacTrac device are shorten

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Publication Date
Wed Mar 08 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Extraction and Estimation of β-carotene Pigment from Some Species of The Genus Rhodotorula and Study The Ability to Increas Their Production By Mutagenesis
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Beta-carotene pigment was extracted from 6 strains collected from different sources related to some species of the genus Rhodotorula sp. The maximum productivity was in the strain Rhodotorula  mucilaginosa BA61 with amount 10.25 gm/l. The minimum productivity was from the strain R. minuta BA78 with amount 5.39 gm/l. The effects of the chemical mutagen (MNNG) and the physical mutagen (UVC) on the viability of the strains was studied. The results revealed that the chemical mutagen (MNNG) with the concentration 0.2 mg/ml has the clear effect on the viability of the strains , which killing percentage reached to 65.91% in the strain R. minuta BA78. Results of the study of mutagenesis with UVC showed that increase in killing percentage fo

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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
Effect of the fungus Metarhizumanisopliae (Metchnikoff) Sorokin and actelic insecticide in the protection of bean seeds packaged in bag from infection by cowpea beetle
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this study was perform to defined the effect of fungus Metarhiziumanisopliae Sorokin with concentrations 5x101, 5x103 and 5x105 spore/ ml and Actelic insecticide with concentration 0.001% in in rate of germinate treated string bean seeds also study effect of packed bags in two different type: jute and polypropylene bags which treated with fungal suspension pervious mention and insecticide in rate infested the string bean seeds packed in it.Results of the study showed the following: Lower rate of weight loss of cowpea seeds appear in poly propylene bags 4.41, 5.95% after six months of treatment with 5x 105 spore/ ml and Actelic insecticide respectively compared with 15.08% to control treatment. The fungal concentrations and insecticide not

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Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)
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One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

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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
Thu May 30 2024
Journal Name
Iraqi Journal Of Science
A Review Study on Forgery and Tamper Detection Techniques in Digital Images
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Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of variou

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Publication Date
Wed Jul 24 2024
Journal Name
Journal Of Plant Protection Research
Developing smart sprayer for weed control using an object detection algorithm (yolov5)
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Spraying pesticides is one of the most common procedures that is conducted to control pests. However, excessive use of these chemicals inversely affects the surrounding environments including the soil, plants, animals, and the operator itself. Therefore, researchers have been encouraged to...

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Publication Date
Sat Jun 01 2024
Journal Name
Journal Of Engineering
Copy Move Image Forgery Detection using Multi-Level Local Binary Pattern Algorithm
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Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

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Publication Date
Sun Jan 01 2017
Journal Name
Spe
SPE-188966-MS: Drilling problems detection in Basrah oil fields using smartphones
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Scopus (1)
Scopus
Publication Date
Tue Jan 18 2022
Journal Name
Photonic Sensors
Arsenic Detection Using Surface Plasmon Resonance Sensor With Hydrous Ferric Oxide Layer
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Abstract<p>The lethality of inorganic arsenic (As) and the threat it poses have made the development of efficient As detection systems a vital necessity. This research work demonstrates a sensing layer made of hydrous ferric oxide (Fe<sub>2</sub>H<sub>2</sub>O<sub>4</sub>) to detect As(III) and As(V) ions in a surface plasmon resonance system. The sensor conceptualizes on the strength of Fe<sub>2</sub>H<sub>2</sub>O<sub>4</sub> to absorb As ions and the interaction of plasmon resonance towards the changes occurring on the sensing layer. Detection sensitivity values for As(III) and As(V) were 1.083 °·ppb<sup>−1</sup> and 0.922 °·ppb<jats></jats></p> ... Show More
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