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Image segmentation by using thresholding technique in two stages
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Publication Date
Thu Apr 20 2023
Journal Name
Fire
An Efficient Wildfire Detection System for AI-Embedded Applications Using Satellite Imagery
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Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

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Publication Date
Sat Dec 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
Classification of Iraqi Children According to Their Nutritional Status Using Fuzzy Logic
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In this paper, we build a fuzzy classification system for classifying the nutritional status of children under 5 years old in Iraq using the Mamdani method based on input variables such as weight and height to determine the nutritional status of the child. Also, Classifying the nutritional status faces a difficult challenge in the medical field due to uncertainty and ambiguity in the variables and attributes that determine the categories of nutritional status for children, which are relied upon in medical diagnosis to determine the types of malnutrition problems and identify the categories or groups suffering from malnutrition to determine the risks faced by each group or category of children. Malnutrition in children is one of the most

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Publication Date
Tue Sep 04 2018
Journal Name
Al-khwarizmi Engineering Journal
Effect of Carburization Parameters on Hardness of Carburized Steel Using MOORA Approach
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In this research, (MOORA) approach based– Taguchi design was used to convert the multi-performance problem into a single-performance problem for nine experiments which built (Taguchi (L9) orthogonal array) for carburization operation. The main variables that had a great effect on carburizing operation are carburization temperature (oC), carburization time (hrs.) and tempering temperature (oC). This study was also focused on calculating the amount of carbon penetration, the value of hardness and optimal values obtained during the optimization by Taguchi approach and MOORA method for multiple parameters. In this study, the carburization process was done in temperature between (850 to 950 ᵒC) for 2 to 6

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network
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     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

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Publication Date
Mon Dec 31 2012
Journal Name
Al-khwarizmi Engineering Journal
Dehydration of Ethanol Using Pervaporation Separation with Nanoporous Hydrophilic Silica Ceramic Membrane
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The pervaporation using a commercial hydrophilic ceramic membrane supplied from PERVATECH was conducted. The dehydration of ethanol/ water system was used as a model for the pervaporation study. Pervaporation experiments of ethanol/water system were carried out in the temperature range of 303-343K, ethanol concentration in the feed 10-90 vol. % and the feed flow rate in the range of 0.5-10 L/min.  In this work, the effect of operation parameters on permeates fluxes as well as permeates separation factors have been studied. The Water flux is strongly dependent on the temperature; it increased with increasing in temperature, which in turn decreased the selectivity of membrane to water molecules.

In addition water flux was decr

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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 Jan 06 2022
Journal Name
Kuwait Journal Of Science
AVO analysis for high amplitude anomalies using 2D pre-stack seismic data
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Amplitude variation with offset (AVO) analysis is an 1 efficient tool for hydrocarbon detection and identification of elastic rock properties and fluid types. It has been applied in the present study using reprocessed pre-stack 2D seismic data (1992, Caulerpa) from north-west of the Bonaparte Basin, Australia. The AVO response along the 2D pre-stack seismic data in the Laminaria High NW shelf of Australia was also investigated. Three hypotheses were suggested to investigate the AVO behaviour of the amplitude anomalies in which three different factors; fluid substitution, porosity and thickness (Wedge model) were tested. The AVO models with the synthetic gathers were analysed using log information to find which of these is the

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Publication Date
Wed Jun 16 2021
Journal Name
Cognitive Computation
Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps
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Abstract <p>Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b</p> ... Show More
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Publication Date
Sat Feb 27 2021
Journal Name
Journal Of Engineering
Improvement of Unconfined Compressive Strength of Soft Clay using Microbial Calcite Precipitates
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The precipitation of calcite induced via microorganisms (MICP) is a technique that has been developed as an innovative sustainable ground improvement method utilizing ureolytic bacteria to soil strengthening and stabilization. Locally isolated Bacillus Sonorensis from Iraqi soil samples were found to have high abilities in producing urease. This study aims to use the MICP technique in improving the undrained shear strength of soft clay soil using two native urease producing bacteria that help in the precipitation of calcite to increase the cementation between soil particles. Three concentrations of each of the locally prepared Bacillus sonorensis are used in this study for cementation reagent (0.25M, 0.5M, and 1M) during

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Engineering
Voltage Profile Enhancing Using HVDC for 132KV Power System: Kurdistan Case Study
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Nowadays power systems are huge networks that consist of electrical energy sources, static and lumped load components, connected over long distances by A.C. transmission lines. Voltage improvement is an important aspect of the power system. If the issue is not dealt with properly, may lead to voltage collapse.  In this paper, HVDC links/bipolar connections were inserted in a power system in order to improve the voltage profile. The load flow was simulated by Electrical Transient Analyzer Program (ETAP.16) program in which Newton- Raphson method is used. The load flow simulation studies show a significant enhancement of the power system performance after applying HVDC links on Kurdistan power systems. Th

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