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A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning
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Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.

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Publication Date
Wed Dec 14 2016
Journal Name
Journal Of Baghdad College Of Dentistry
Detection of Granulocyte Chemotactic Protein 2 in Serum of Periodontitis Patients
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Background: Chronic periodontitis defined as “an infectious inflammatory disease within supporting tissues of the teeth, progressive attachment loss and bone loss". Aggressive periodontitis is rare which in most cases manifest themselves clinically during youth. It characterized by rapid rate of disease progression .Pro-inflammatory chemokines organized inflammatory responses. Granulocyte chemotactic protein 2 is involved in neutrophil gathering and movement. The purpose of the study is to detect serum of Granulocyte Chemotactic Protein 2 and correlate to periodontal condition in patients with chronic periodontitis, Aggressive periodontitis and Healthy Control subjects and measurement the count of neutrophils for the studied groups. S

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Publication Date
Thu May 06 2021
Journal Name
Tesol International Journal
The Effect of Types of Blended Learning Strategies on EFL Students` Achievements
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This work aims at finding out the impact of teaching types blended learning strategies on academic students` achievement. A review of related literature indicates that almost no study has ever attempted to focus specifically on the effect of the different kinds of blended learning strategies on EFL students` achievement in the educational research writing, and the present study attempts to fill this gap. The study focused on the students at the Master's degree in Educational Research Writing in the first semester of the academic year 2020/2021. The sample has selected from the college of Education Ibn-Rushd (18) students. Material has been designed for the Master candidates’ participants of the study was divided into two groups: one an e

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Publication Date
Sun Jun 07 2026
Journal Name
Ishraqat Tanmawia
Formation Engineering and Pedagogy of E-Learning in Light of Corona Pandemic
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Publication Date
Mon Jun 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
The Philosophy of Organizational Forgetting In Frame of Learning and Organizational knowledge
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current research aims to build an intellectual framework for concept of organizational forgetting, which is considered one of the most important topics in contemporary management thought, which is gain the consideration of most scholars and researchers in field of organizational behavior, which is to be a loss of intentional or unintentional knowledge of any organizational level. It turned out that just as organizations should learn and acquire knowledge, they must also forget, especially knowledge obsolete and worn out. And represented the research problem in the absence of Arab research dealing with organizational forgetting, and highlights the supporting infrastructure core, and show a close relationship with organizational le

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Publication Date
Wed Dec 01 2021
Journal Name
Journal Of Physics: Conference Series
Disc damage likelihood scale recognition for Glaucoma detection
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Abstract<p>Glaucoma is a visual disorder, which is one of the significant driving reason for visual impairment. Glaucoma leads to frustrate the visual information transmission to the brain. Dissimilar to other eye illness such as myopia and cataracts. The impact of glaucoma can’t be cured; The Disc Damage Likelihood Scale (DDLS) can be used to assess the Glaucoma. The proposed methodology suggested simple method to extract Neuroretinal rim (NRM) region then dividing the region into four sectors after that calculate the width for each sector and select the minimum value to use it in DDLS factor. The feature was fed to the SVM classification algorithm, the DDLS successfully classified Glaucoma d</p> ... Show More
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Publication Date
Tue Jan 01 2013
Journal Name
Innovative Systems Design And Engineering
Automated Surface Defect Detection using Area Scan Camera
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Publication Date
Sun Dec 01 2002
Journal Name
Iraqi Journal Of Physics
An edge detection algorithm matching visual contour perception
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For several applications, it is very important to have an edge detection technique matching human visual contour perception and less sensitive to noise. The edge detection algorithm describes in this paper based on the results obtained by Maximum a posteriori (MAP) and Maximum Entropy (ME) deblurring algorithms. The technique makes a trade-off between sharpening and smoothing the noisy image. One of the advantages of the described algorithm is less sensitive to noise than that given by Marr and Geuen techniques that considered to be the best edge detection algorithms in terms of matching human visual contour perception.

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Publication Date
Wed Sep 11 2019
Journal Name
Journal Of Mechanical Engineering Research And Developments
INDUSTRIAL TRACKING CAMERA AND PRODUCT VISION DETECTION SYSTEM
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Publication Date
Mon May 15 2017
Journal Name
International Journal Of Image And Data Fusion
Image edge detection operators based on orthogonal polynomials
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Publication Date
Tue Oct 12 2021
Journal Name
Engineering, Technology And Applied Science Research
Automated Pavement Distress Detection Using Image Processing Techniques
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Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels wit

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