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Detecting Damaged Buildings on Post-Hurricane Satellite Imagery based on Transfer Learning
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In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performance measures are used as a criterion to decide which classifier is the best one to detect the images with high accuracy. Eventually, the simulation results show that each classifier detect the damage/no damage image with different performance measures and then makes it easy to select the best one.

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Publication Date
Fri Sep 01 2023
Journal Name
Journal Of Engineering
The Effect of Cohesive Debonding Elimination on Enhancing the Flexural Performance of Damaged Unbonded Prestressed Concrete Girders Strengthened Using NSM CFRP
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This manuscript studied the effect of U-CFRP wrapped sheet anchorage on the flexural performance of unbonded post-tensioned PC members subjected to partial strand damage and strengthened using CFRP Near-Surface Mounting techniques. The program includes six girders as a control girder, a girder with strand damage of 14.2%, and four girders strengthened by CFRP laminates using the NSM technique with and without U-CFRP wrapped sheet anchorages. The testing results show that the strand damage of 14.2% has reduced the flexural strength of the girder by 5.71%. The NSM-CFRP laminate has a significant effect on flexural strength by 17.4%. On the other hand, the application of end U-CFRP wrapped sheet anchorages improves flexural

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Publication Date
Sun Apr 02 2017
Journal Name
Journal Of Educational And Psychological Researches
Standraidization of Post traumatic Stress Disorder Scale on Students of University of Baghdad
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There are a lot of measures for Post Traumatic Stress Disorder (PTSD), but what distinguishes it from other measures is the number of short paragraphs and concise depending on the classification of American Psychiatric Association (APA), which can be applied by the researchers on a large number, Because the Iraqi society suffered from psychological trauma due to wars, conflicts and occupation and the resulting psychological disorders of the members of Iraqi society, especially post-traumatic stress disorder, as mentioned by many studies and researches.

Therefore, the aim of this study was to standardize the PTSD scale for students at Baghdad University. For the Psychologist Fua . E. which was applied in many foreign studies. 

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Publication Date
Mon Feb 01 2021
Journal Name
Iop Conference Series: Materials Science And Engineering
Experimental Study on the Behaviours of Post-tensioned Concrete Members with Unbonded Tendons
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Abstract<p>This paper presents experimental results regarding the behaviours of eight simply supported partially prestressed concrete beams with internally unbonded tendons, focusing particularly on the effect of three different variables: concrete compressive strength, <italic>f<sub>c</sub> </italic>′; jacking stress, <italic>f<sub>j</sub> </italic>; and the prestressing index, ω. Increasing the concrete compressive strength from 35 MPa to 60 MPa was seen to lead to an increase in load-carrying capacity by about 10%. The load capacity was also affected, though to a lesser extent, by the jacking stress. The failure lo</p> ... Show More
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Publication Date
Wed Dec 01 2021
Journal Name
Structures
The effect of ground motion characteristics on the fragility analysis of reinforced concrete frame buildings in Australia
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Publication Date
Sun Jan 01 2017
Journal Name
Journal Of Engineering
Effect of Air Bubbles on Heat Transfer Coefficient in Turbulent Convection Flow
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Experimental and numerical studies have been conducted for the effect of injected air bubbles on the heat transfer coefficient through the water flow in a vertical pipe under the influence of uniform heat flux. The investigated parameters were water flow rate of (10, 14 and 18) lit/min, air flow rate of (1.5, 3 and 4) lit/min for subjected heat fluxes of (27264, 36316 and 45398) W/m2. The energy, momentum and continuity equations were solved numerically to describe the motion of flow. Turbulence models k-ε was implemented. The mathematical model is using a CFD code Fluent (Ansys15). The water was used as continuous phase while the air was represented as dispersed. phase. The experimental work includes design, build and instrument a test

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Publication Date
Thu Dec 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Cryptography based on retina information
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The security of message information has drawn more attention nowadays, so; cryptography has been used extensively. This research aims to generate secured cipher keys from retina information to increase the level of security. The proposed technique utilizes cryptography based on retina information. The main contribution is the original procedure used to generate three types of keys in one system from the retina vessel's end position and improve the technique of three systems, each with one key. The distances between the center of the diagonals of the retina image and the retina vessel's end (diagonal center-end (DCE)) represent the first key. The distances between the center of the radius of the retina and the retina vessel's end (ra

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Research on Emotion Classification Based on Multi-modal Fusion
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Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

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Publication Date
Sun Nov 01 2015
Journal Name
Energy Conversion And Management
Laminar convective heat transfer of hexylamine-treated MWCNTs-based turbine oil nanofluid
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Multi-walled carbon nanotubes (MWCNTs) were functionalized by hexylamine (HA) in a promising, cost-effective, rapid and microwave-assisted approach. In order to decrease defects and remove acid-treatment stage, functionalization of MWCNTs with HA was carried out in the presence of diazonium reaction. Surface functionality groups and morphology of chemically-functionalized MWCNTS were characterized by FTIR, Raman spectroscopy, thermogravimetric analysis (DTG), and transmission electron microscopy (TEM). To reach a promising dispersibility in oil media, MWCNTs were functionalized with HA. While the cylindrical structures of MWCNTs were remained reasonably intact, characterization results consistently confirmed the sidewall-functionalization o

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Publication Date
Mon Jan 09 2023
Journal Name
2023 15th International Conference On Developments In Esystems Engineering (dese)
Deep Learning-Based Speech Enhancement Algorithm Using Charlier Transform
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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Cybersecurity And Information Management
Machine Learning-based Information Security Model for Botnet Detection
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Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

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