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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 based on the fusion of probabilities. Individually, the classifier based on PI achieved 93.1% accuracy, whereas the deep classifiers reached classification accuracies over 90% only in isolated cases. Overall, the average accuracy of the deep networks over the four corneal maps ranged from 86% (SfN) to 89.9% (AN). The classifier ensemble increased the accuracy of the deep classifiers based on corneal maps to values ranging (92.2% to 93.1%) for SqN and (93.1% to 94.8%) for AN. Including in the ensemble-specific combinations of corneal maps’ classifiers and PI increased the accuracy to 98.3%. Moreover, visualization of first learner filters in the networks and Grad-CAMs confirmed that the networks had learned relevant clinical features. This study shows the potential of creating ensembles of deep classifiers fine-tuned with a transfer learning strategy as it resulted in an improved accuracy while showing learnable filters and Grad-CAMs that agree with clinical knowledge. This is a step further towards the potential clinical deployment of an improved computer-assisted diagnosis system for KCN detection to help ophthalmologists to confirm the clinical decision and to perform fast and accurate KCN treatment.</p>
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Publication Date
Mon Apr 01 2019
Journal Name
Journal Of Engineering
Rehabilitation of Reinforced Concrete Deep Beam by Epoxy Resin
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This investigation presents an experimental and analytical study on the behavior of reinforced concrete deep beams before and after repair. The original beams were first loaded under two points load up to failure, then, repaired by epoxy resin and tested again. Three of the test beams contains shear reinforcement and the other two beams have no shear reinforcement. The main variable in these beams was the percentage of longitudinal steel reinforcement (0, 0.707, 1.061, and 1.414%). The main objective of this research is to investigate the possibility of restoring the full load carrying capacity of the reinforced concrete deep beam with and without shear reinforcement by using epoxy resin as the material of repair. All be

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Publication Date
Sun Jan 01 2017
Journal Name
البحوث التربويةوالنفسية
Preparing a teacher’s guide for computer books for the intermediate stage according to learning styles
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Publication Date
Wed Mar 20 2024
Journal Name
Journal Of Petroleum Research And Studies
Advanced Machine Learning application for Permeability Prediction for (M) Formation in an Iraqi Oil Field
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Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science (ijeecs)
A new smart approach of an efficient energy consumption management by using a machine-learning technique
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Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s

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Publication Date
Mon May 01 2023
Journal Name
Journal Of Engineering
Experimental Investigation of Heat Transfer Enhancement in a Double Pipe Heat Exchanger Using Compound Technique of Transverse Vibration and Inclination Angle
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Numerous tests are recently conducted to assess vibration's role in accelerating the heat transfer rate in various heat exchangers. In this work, the enhancement of heat transfer by the effect of transfer vibration and inclination angles on the surface of a double pipe heat exchanger experimentally has been investigated. A data acquisition system is applied to record the data of temperatures, flow rates, and frequencies over the tests. A compound technique was adopted, including the application of a set of inclination angles of (0°, 10°, 20°, and 30°) under the effect of frequency of vibration ranging from sub-resonance to over-resonance frequencies. The results showed that the overall heat transfer coefficient enhan

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Publication Date
Sat Feb 02 2019
Journal Name
Journal Of The College Of Education For Women
The effect of learning program in developing some social concepts for nursery kids about ( 4 – 5 ): The effect of learning program in developing some social concepts for nursery kids about ( 4 – 5 )
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Abstact:
Nursery is one of educational institution in the process of developing the
social concepts that it includes a quirking the knowledge and experiences that
help the kid to adjust with environment through arrangement words ,
movements and concrete things which are transferred to the kids so as to
realize these concepts .
Social concepts are numbers of words and statements with social nature
which the kids learn through the family or nursery in order to effect their
educational style of independent and helping the others .
The re searcher adopted this theory because of the little studies in the
filed of social concepts in the nursery.
The aims of the study are as following :
1- building tools for

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Theoretical and experimental studies for different compounds to calculate: electronic transfer, energy gap and NLO properties
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This work involves theoretical and experimental studies for seven compounds to calculate the electrons spectrum and NLO properties. The theoretical study is done by employing the Time Depending Density Functional Theory TD-DFT and B3LYP/high basis set 6-311++G (2d,2p), using Gaussian program 09. Experimental study by UV/VIS spectrophotometer device to prove the theoretical study. Theoretical and experimental results were applicable in spectrum and energy gap values, in addition to convergence theoretically the energy gap results from ΔEHOMO-LUMO and UV/VIS. spectrum. Consider the theoretical method very appropriate to compounds that absorb in vacuum UV.

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Publication Date
Mon Jun 30 2025
Journal Name
Iraqi Journal Of Science
New Weighted Synthetic Oversampling Method for Improving Credit Card Fraud Detection
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The use of credit cards for online purchases has significantly increased in recent years, but it has also led to an increase in fraudulent activities that cost businesses and consumers billions of dollars annually. Detecting fraudulent transactions is crucial for protecting customers and maintaining the financial system's integrity. However, the number of fraudulent transactions is less than legitimate transactions, which can result in a data imbalance that affects classification performance and bias in the model evaluation results. This paper focuses on processing imbalanced data by proposing a new weighted oversampling method, wADASMO, to generate minor-class data (i.e., fraudulent transactions). The proposed method is based on th

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
GIS as A Tool for Expansive Soil Detection at Sulaymaniyah City
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Geotechnical engineering like any other engineering field has to develop and cope with new technologies. This article intends to investigate the spatial relationships between soil’s liquid limit (LL), plasticity index (PI) and Liquidity index (LI) for particular zones of Sulaymaniyah City. The main objective is to study the ability to produce digital soil maps for the study area and determine regions of high expansive soil. Inverse Distance Weighting (IDW) interpolation tool within the GIS (Geographic Information System) program was used to produce the maps. Data from 592 boreholes for LL and PI and 245 boreholes for LI were used for this study. Layers were allocated into three depth ranges (1 to 2, 2 to 4 and 4 to 6)

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Publication Date
Sat May 24 2025
Journal Name
Iraqi Journal For Computer Science And Mathematics
Intrusion Detection System for IoT Based on Modified Random Forest Algorithm
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An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets men

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