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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
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges
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The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an in

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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Publication Date
Tue Jun 08 2021
Journal Name
مجلة العلوم و التكنولوجية للنشاطات البدنية و الرياضية
The effectiveness of using (7E’s) learning cycle in learning a movement chain on the uneven bars in the artistic gymnastics for women
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Abstract The Object of the study aims to identify the effectiveness of using the 7E’s learning cycle to learn movement chains on uneven bars, for this purpose, we used the method SPSS. On a sample composed (20) students on collage of physical education at the university of Baghdad Chosen as two groups experimental and control group (10) student for each group, and for data collection, we used SPSS After collecting the results and having treated them statistically, we conclude the use 7E’s learning cycle has achieved remarkable positive progress, but it has diverged between to methods, On this basis, the study recommended the necessity of applying 7E’s learning cycle strategy in learning the movement chain on uneven bar

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Publication Date
Sat Dec 17 2022
Journal Name
Applied Sciences
A Hybrid Artificial Intelligence Model for Detecting Keratoconus
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Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

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Publication Date
Mon May 06 2019
Journal Name
Journal Of Physical Education
Designing Teaching Aids and Their Effects on Learning and Retaining Diving and Cartwheel on Floor Exercises in Women’s’ Artistic Gymnastics
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Designing Teaching Aids and Their Effects on Learning and Retaining Diving and Cartwheel on Floor Exercises in Women’s’ Artistic Gymnastics

The research aimed at designing teaching aids that develop and help retain diving and cartwheel for third year college of physical education and sport sciences students in women’s artistic gymnastics. In addition to that, the researchers aimed at identifying the effect of these aids on learning and retaining cartwheel and diving in floor exercises. The researchers used the experimental method. The subjects were (20) third year female students from the college of physical education and sport sciences/ university of Baghdad sections K and H. the main experiment lasted for

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Publication Date
Mon Nov 14 2022
Journal Name
Biomedicine
Cytotoxic effect of cloned EGFP gene on NCI-H727 cell line via genetically engineered gene transfer system
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Introduction and Aim: Cancers are a complex group of genetic illnesses that develop through multistep, mutagenic processes which can invade or spread throughout the body. Recent advances in cancer treatment involve oncolytic viruses to infect and destroy cancer cells. The Newcastle disease virus (NDV), an oncolytic virus has shown to have anti-cancer effects either directly by lysing cancer cells or indirectly by activating the immune system. The green fluorescent protein (GFP) has been widely used in studying the anti-tumor activity of oncolytic viruses. This study aimed to study the anticancer effect of a recombinant rNDV-GFP clone on NCI-H727 lung carcinoma cell line in vitro.   Materials and Methods: The GFP gene was inserted t

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Publication Date
Mon Nov 14 2022
Journal Name
Biomedicine
Cytotoxic effect of cloned EGFP gene on NCI-H727 cell line via genetically engineered gene transfer system
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Introduction and Aim: Cancers are a complex group of genetic illnesses that develop through multistep, mutagenic processes which can invade or spread throughout the body. Recent advances in cancer treatment involve oncolytic viruses to infect and destroy cancer cells. The Newcastle disease virus (NDV), an oncolytic virus has shown to have anti-cancer effects either directly by lysing cancer cells or indirectly by activating the immune system. The green fluorescent protein (GFP) has been widely used in studying the anti-tumor activity of oncolytic viruses. This study aimed to study the anticancer effect of a recombinant rNDV-GFP clone on NCI-H727 lung carcinoma cell line in vitro.   Materials and Methods: The GFP gene was inserted t

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Publication Date
Sun Apr 08 2018
Journal Name
Al-khwarizmi Engineering Journal
Experimental Investigation of the Effect of Curvature Ratio on Heat Transfer in Double Pipe Helical Heat Exchanger
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Different parameters of double pipe helical coil were investigation experimentally. Four coils were used; three with a curvature ratio (0.037, 0.031, and 0.028) and 11mm diameter of the inner tube while the fourth with 0.033 curvature ratio and 13 mm diameter of the inner tube. The hot water flow in the inner tube whereas the cold water flows in the annulus. The inlet temperatures of hot and cold water are 50 0C and 18 0C respectively. The inner mass flow rate ranges from 0.0167 to 0.0583 kg/s. The results show the Nusselt number increase with increase curvature ratio. The Nusselt number of the coil with 0.037 curvature ratio increases by approximately 12.3 % as compare with 0.028 curvature ratio. The results also r

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Publication Date
Sat Mar 01 2008
Journal Name
Al-khwarizmi Engineering Journal
The Effects of Vortex Generator Types on Heat Transfer and Flow Structure in a Rectangular Duct Flows
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    In this numerical study a detailed evaluation of the heat transfer characteristics and flow structure in a laminar and turbulent flow through a rectangular channel containing built-in of different type vortex generator has been a accomplished in a range of Reynolds number between 500 and 100,000.A modified version of ESCEAT code has been used to solve Navier-Stokes and energy equations. The purpose of this paper is to present numerical comparisons in terms of temperature, Nusselt number and flow patterns on several configurations of longitudinal vortex generator including new five cases. The structures of heat and flow were studied, using iso-contours of velocity components, vortices, temperature and Nusselt n

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Publication Date
Thu Aug 02 2012
Journal Name
International Journal Of Advanced Research In Computer Science
User Authentication based on Keystroke Dynamics Using Backpropagation Network
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Computer systems and networks are being used in almost every aspect of our daily life; as a result the security threats to computers and networks have also increased significantly. Traditionally, password-based user authentication is widely used to authenticate legitimate user in the current system0T but0T this method has many loop holes such as password sharing, shoulder surfing, brute force attack, dictionary attack, guessing, phishing and many more. The aim of this paper is to enhance the password authentication method by presenting a keystroke dynamics with back propagation neural network as a transparent layer of user authentication. Keystroke Dynamics is one of the famous and inexpensive behavioral biometric technologies, which identi

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