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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
Thu Mar 30 2023
Journal Name
Iraqi Journal Of Science
Facial Expression Recognition Based on Deep Learning: An Overview
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      Recognizing facial expressions and emotions is a basic skill that is learned at an early age and it is important for human social interaction. Facial expressions are one of the most powerful natural and immediate means that humans use to express their feelings and intentions. Therefore, automatic emotion recognition based on facial expressions become an interesting area in research, which had been introduced and applied in many areas such as security, safety health, and human machine interface (HMI). Facial expression recognition transition from controlled environmental conditions and their improvement and succession of recent deep learning approaches from different areas made facial expression representation mostly based on u

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Publication Date
Wed Mar 16 2022
Journal Name
International Journal Of Recent Contributions From Engineering, Science & It
Smart Learning based on Moodle E-learning Platform and Digital Skills for University Students
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Publication Date
Sat Mar 29 2014
Journal Name
International Journal Of Academic Research In Progressive Education And Development
The Effects of Problem-Based Learning on Self-Directed Learning Skills among Physics Undergraduates
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The aim of this study is to compare the effects of three methods: problem-based learning (PBL), PBL with lecture method, and conventional teaching on self-directed learning skills among physics undergraduates. The actual sample size comprises of 122 students, who were selected randomly from the Physics Department, College of Education in Iraq. In this study, the pre- and post-test were done and the instruments were administered to the students for data collection. The data was analyzed and statistical results rejected null hypothesis of this study. This study revealed that there are no signifigant differences between PBL and PBL with lecture method, thus the PBL without or with lecture method enhances the self-directed learning skills bette

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Publication Date
Thu Mar 30 2023
Journal Name
Iraqi Journal Of Science
Image Processing Approaches as a Diagnostic Parameter to Determine Pollution by Using Satellite Imagery, Northern Iraq
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     This work highlights the estimation of the Al-Khoser River water case that disposes of its waste directly into the Tigris River within Mosul city. Furthermore, the work studies the effects of environmental and climate change and the impact of pollution resulting from waste thrown into the Al-Khoser River over the years. Al-Khoser River is located in the Northern Mesopotamia of Mosul city. This study aims to detect the polluted water area and the polluted surrounding area. Temporal remote sensing data of different Landsat generations were considered in this work, specifically Enhanced Thematic Mapper Plus of 2000 and Operational Land Imager of 2015. The study aims to measure the amount of pollution in the study area over 15 years

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Publication Date
Sun Jan 13 2019
Journal Name
Iraqi Journal Of Physics
Adaptive digital technique for discriminating between shadow and water bodies in the high resolution satellite imagery
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This research presents a new algorithm for classification the
shadow and water bodies for high-resolution satellite images (4-
meter) of Baghdad city, have been modulated the equations of the
color space components C1-C2-C3. Have been using the color space
component C3 (blue) for discriminating the shadow, and has been
used C1 (red) to detect the water bodies (river). The new technique
was successfully tested on many images of the Google earth and
Ikonos. Experimental results show that this algorithm effective to
detect all the types of the shadows with color, and also detects the
water bodies in another color. The benefit of this new technique to
discriminate between the shadows and water in fast Matlab pro

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Publication Date
Sun Sep 03 2023
Journal Name
Iraqi Journal Of Computers, Communications, Control & Systems Engineering (ijccce)
Efficient Iris Image Recognition System Based on Machine Learning Approach
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HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Journal Of Science
Heart Disease Classification–Based on the Best Machine Learning Model
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    In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne

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Publication Date
Thu Dec 28 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Spectrophotometric Determination of Sulfamethoxazole Based on Charge-Transfer Complexation with Sodium Nitroprusside
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   A simple, accurate and precise spectrophotometric method has been developed for the analysis of sulfamethoxazole (SMZ) in pure form and pharmaceutical preparation. The method involves a direct charge transfer complexation of sulfamethoxazole (SMZ) with sodium nitroprusside (SNP) in alkaline medium and the presence of hydroxyl amine hydrochloride. Variables affecting the formation of the formed orange colored complex were optimized following two approaches univariate and central composite experimental design (CCD) multivariate. Under optimum recommended conditions, the formed complex exhibits λmax at 512 nm and the method conforms Beer's law for SMZ concentration in the range of 5.0-150.0 (µg.mL-1) with molar absorptivi

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Publication Date
Fri Jan 26 2024
Journal Name
Iraqi Journal Of Science
Monthly Carbone Monoxide (CO) Distribution Based on the 2010 MOPITT Satellite Data in Iraq
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Carbon monoxide (CO) plays an important indirect greenhouse gases due to its influences on the budgets of hydroxyl radicals (OH) and Ozone (O3). The atmospheric carbon monoxide (CO) observations can only be made on global and continental scales by remote sensing instruments situated in space. One of instrument is the Measurements of Pollution in the Troposphere (MOPITT), which is designed to measure troposphere CO and CH4 by use of a nadir-viewing geometry and was launched aboard the Earth Observing System (EOS) Terra spacecraft on 18 December 1999. Results from the analysis of the retrieved monthly (1ºх1º) spatial grid resolution, from the MOPITT data were utilized to analyze the distribution of CO surface mixing ratio in Iraq for th

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Publication Date
Wed Apr 01 2020
Journal Name
Plant Archives
Land cover change detection using satellite images based on modified spectral angle mapper method
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This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio

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