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Optimizing Application of UAV-Based SfM Photogrammetric 3D Mapping in Urban Areas
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In recent years, the extensive need for high-quality acquisition platforms for various 3D mapping applications has rapidly increased, especially in sensor performance, portability, and low cost. Image-based UAV sensors have overwhelming merits over alternative solutions for their high timeline and resilience data acquisition systems and the high-resolution spatial data they can provide through extensive Computer Vision (CV) data processing approaches. However, applying this technique, including the appropriate selection of flight mission and image acquisition parameters, ground settings and targeting, and Structure from Motion- Multi-View Stereo (SfM-MVS) post-processing, must be optimized to the type of study site and feature characteristics. This research focuses on optimizing the application of UAV-SfM photogrammetry in an urban area on the east bank of the Tigris River in the north region of Iraq following optimized data capturing plan and SfM-MVS photogrammetric workflow. The research presented the practical application of optimized flight planning, data acquisition, image processing, accuracy analysis, and evaluation based on ground truth targets designed for the proposed optimal routine. This includes investigating the influence of the number and distribution of GCPs, flying heights, and processing parameters on the quality of the produced 3D data. The research showed the potential of low-budget and affordable UAV devices to deliver robust 3D products in a relatively short period by demonstrating the value of UAV-based image techniques when contributed to CV algorithms. The results showed powerful outcomes with validation errors reaching a centimeter-level from 100 m flying height when applying the optimized flight plan settings and the appropriate selection of the number and distribution of GCPs. The study established a streamlined UAV mapping procedure, demonstrated the viability of UAV use for 3D mapping applications, offered suggestions for enhancing future applications, and offered clues as to whether or not UAVs could serve as a viable alternative to conventional ground-based surveying techniques in accurate applications.

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Publication Date
Fri Dec 06 2019
Journal Name
Ssociation Of Arab Universities Journal Of Engineering Sciences
Application of Artificial Neural Network and GeographicalInformation System Models to Predict and Evaluate the Quality ofDiyala River Water, Iraq
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This research discusses application Artificial Neural Network (ANN) and Geographical InformationSystem (GIS) models on water quality of Diyala River using Water Quality Index (WQI). Fourteen water parameterswere used for estimating WQI: pH, Temperature, Dissolved Oxygen, Orthophosphate, Nitrate, Calcium, Magnesium,Total Hardness, Sodium, Sulphate, Chloride, Total Dissolved Solids, Electrical Conductivity and Total Alkalinity.These parameters were provided from the Water Resources Ministryfrom seven stations along the river for the period2011 to 2016. The results of WQI analysis revealed that Diyala River is good to poor at the north of Diyala provincewhile it is poor to very polluted at the south of Baghdad City. The selected parameters wer

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Publication Date
Wed Nov 11 2020
Journal Name
Journal Of Research In Medical And Dental Science
Effect of Topical Melatonin Application on the Peri-Implant Proximal Bone Level and Cortical Plate Thickness (Pilot Clinical Trial)
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Publication Date
Mon Jan 01 2018
Journal Name
2018 Detroit, Michigan July 29 - August 1, 2018
Design and validation of an electronic data logging systems (CAN Bus) for monitoring machinery performance and management- Planting application
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Publication Date
Mon Jun 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
Impact of non-current assets impairment reporting on certain financial indicators application on Al-Mansour Pharmaceuticals- a private corporation
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   This research deals with the financial reporting for non-current assets impairment from the viewpoint of international accounting standards, particularly IAS 36 "Impairment of non-current assets." The research problems focus on the presence of internal and external indicators on impairment of non-current assets in many of companies listed in Iraqi stock exchange. So it is required to apply IAS 36 to reporting for the impairment loss of assets since this impairment impact certain financial indicators. These indicators help users in their decision-making and forecasting future financial situation and the ability of the company to achieve future profits or maintain current profits. The research aims to shedding lig

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Publication Date
Tue Jun 23 2020
Journal Name
Annal S Of Tropical Medicine & Public Health
Histological Evaluation of local application of Phyllunthus amarus extracted powder and its combination with fibronectin protein on Wound Healing of rats
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Background: A wound is defined as a break or damage in the skin, resulting from physical or thermal damage or as a result of the presence of an underlying medical or physical condition. Herbal medicine can be called one of the branches of medicine in various forms.Phyllunthus amarus is a small herb well known for its medicinal properties and widely used worldwide. P. amarus is an important plant of Indian Ayurvedic system of medicine. Fibronectin is a major component of the extracellular matrix. It is secreted by various cells, primarily fibroblasts, as a soluble protein dimer and is then assembled into an insoluble matrix in a complex cell-mediated process. Materials and methods: Forty rats will be subjected for a surgical operation

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Publication Date
Fri Dec 01 2023
Journal Name
Iop Conference Series: Earth And Environmental Science
Effect of Exogenous Application of Nano Fertilizers and Seaweeds Extract on the Growth, Yield, and Total Alkaloids Content of Hyoscyamus niger
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Abstract<p>The study was conducted at the College of Agricultural Engineering Sciences - University of Baghdad in 2022. It aimed to improve the growth of the European black Henbane plant (<italic>Hyoscyamus niger</italic>), leaf yield, and its content of the total alkaloids as effective medicinal secondary metabolite compounds by spraying with three levels of nano-nitrogen (N=46%) and three levels of nano-potassium (K = 50%)) is 1 gm L<sup>-1</sup>, 2 gm L<sup>-1</sup>, and 3 gm L<sup>-1</sup>, respectively, for each of these fertilizers, in addition to spraying with two levels of seaweed extract (Acadian) at a concentration of 2 and 3 ml L<sup>-1</sup></p> ... Show More
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Publication Date
Sun Jan 20 2019
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Text Classification Based on Weighted Extreme Learning Machine
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The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed   a great competence of the proposed WELM compared to the ELM. 

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Publication Date
Fri Dec 29 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
A Smartphone -Based Model for Human Activity Recognition
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Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classif

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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Engineering
Face-based Gender Classification Using Deep Learning Model
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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

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Publication Date
Sat Oct 01 2022
Journal Name
Therapeutic Delivery
Particles-based Medicated Wound Dressings: A Comprehensive Review
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