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Detection and Classification of The Osteoarthritis in Knee Joint Using Transfer Learning with Convolutional Neural Networks (CNNs)
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    Osteoarthritis (OA) is a disease of human joints, especially the knee joint, due to significant weight of the body. This disease leads to rupture and degeneration of parts of the cartilage in the knee joint, which causes severe pain. Diagnosis of this disease can be obtained through X-ray. Deep learning has become a popular solution to medical issues due to its fast progress in recent years. This research aims to design and build a classification system to minimize the burden on doctors and help radiologists to assess the severity of the pain, enable them to make an optimal diagnosis and describe the correct treatment. Deep learning-based approaches, such as Convolution Neural Networks (CNNs), have been used to detect knee OA using transfer learning with fine-tuning. This paper proposed three versions of pre-trained networks (VGG16, VGG19, and ResNet50) for handling the classification task. According to the classification results, The proposed model ResNet50 outperformed the other models a validation accuracy of 91.51% has been obtained.

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Publication Date
Thu Feb 28 2019
Journal Name
Iraqi Journal Of Science
Automatic Detection of Sunspots Size and Activity using Matlab
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A study is made about the size and dynamic activity of sunspot using automatically detecting Matlab code ''mySS .m'' written for this purpose which mainly finds a good estimate about Sunspot diameter (in km). Theory of  the Sunspot size has been described using  equations, where the growth and decay phases and the area of Sunspot could be calculated. Two types of images, namely H-alpha and HMI magnetograms, have been implemented. The results are divided into four main parts. The first part is sunspot size automatic detection by the Matlab program. The second part is numerical calculations of Sunspot growth and decay phases. The third part is the calculation of  Sunspot area. The final part is to explain the Sunspot activit

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Publication Date
Sat Aug 01 2020
Journal Name
Journal Of Engineering Science And Technology (jestec)
Influence of A River Water Quality on The Efficiency of Water Treatment Using Artificial Neural Network
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Tigris River is the lifeline that supplies a great part of Iraq with water from north to south. Throughout its entire length, the river is battered by various types of pollutants such as wastewater effluents from municipal, industrial, agricultural activities, and others. Hence, the water quality assessment of the Tigris River is crucial in ensuring that appropriate and adequate measures are taken to save the river from as much pollution as possible. In this study, six water treatment plants (WTPs) situated on the two-banks of the Tigris within Baghdad City were Al Karkh; Sharq Dijla; Al Wathba; Al Karama; Al Doura, and Al Wahda from northern Baghdad to its south, that selected to determine the removal efficiency of turbidity and

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Data Aggregation in Wireless Sensor Networks Using Modified Voronoi Fuzzy Clustering Algorithm
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Data centric techniques, like data aggregation via modified algorithm based on fuzzy clustering algorithm with voronoi diagram which is called modified Voronoi Fuzzy Clustering Algorithm (VFCA) is presented in this paper. In the modified algorithm, the sensed area divided into number of voronoi cells by applying voronoi diagram, these cells are clustered by a fuzzy C-means method (FCM) to reduce the transmission distance. Then an appropriate cluster head (CH) for each cluster is elected. Three parameters are used for this election process, the energy, distance between CH and its neighbor sensors and packet loss values. Furthermore, data aggregation is employed in each CH to reduce the amount of data transmission which le

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Publication Date
Sun Mar 03 2024
Journal Name
The Science Teacher
Using Scenarios to Assess Student Learning
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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The classification of fetus gender based on fuzzy C-mean using a hybrid filter
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This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT),(median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on re

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Intelligent Systems
Trip generation modeling for a selected sector in Baghdad city using the artificial neural network
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Abstract<p>This study is planned with the aim of constructing models that can be used to forecast trip production in the Al-Karada region in Baghdad city incorporating the socioeconomic features, through the use of various statistical approaches to the modeling of trip generation, such as artificial neural network (ANN) and multiple linear regression (MLR). The research region was split into 11 zones to accomplish the study aim. Forms were issued based on the needed sample size of 1,170. Only 1,050 forms with responses were received, giving a response rate of 89.74% for the research region. The collected data were processed using the ANN technique in MATLAB v20. The same database was utilized to</p> ... Show More
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Publication Date
Mon Jan 01 2018
Journal Name
Lecture Notes Of The Institute For Computer Sciences, Social Informatics And Telecommunications Engineering
Sensor Data Classification for the Indication of Lameness in Sheep
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Publication Date
Tue Jun 30 2015
Journal Name
Al-kindy College Medical Journal
Magnetic resonance imaging findings of knee injury
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Background: Since its introduction to musculoskeletal imaging in the early 1980, magnetic resonance imaging (MRI) has revolutionized diagnostic imaging of the knee. It is therefore become the examination of choice in the evaluation of internal joint structures of the knee like menisci, cruciate ligaments, and articular cartilage.Objectives: to describe the MRI finding in various knee injuries.Patients and methods: A cross sectional study was done on 130 patients with history of knee injury in MRI unit at institute of radiology and al-Shaheed Ghazi Al-Hariri Hospital in medical city complex - Baghdad, from October 2011 to February 2013 includes 103 men, 27 women; the mean age was 33.86 years. MR imaging studies of the knee performed using

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Publication Date
Wed Nov 25 2015
Journal Name
Research Journal Of Applied Sciences, Engineering And Technology
Subject Independent Facial Emotion Classification Using Geometric Based Features
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Accurate emotion categorization is an important and challenging task in computer vision and image processing fields. Facial emotion recognition system implies three important stages: Prep-processing and face area allocation, feature extraction and classification. In this study a new system based on geometric features (distances and angles) set derived from the basic facial components such as eyes, eyebrows and mouth using analytical geometry calculations. For classification stage feed forward neural network classifier is used. For evaluation purpose the Standard database "JAFFE" have been used as test material; it holds face samples for seven basic emotions. The results of conducted tests indicate that the use of suggested distances, angles

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Publication Date
Mon Feb 04 2019
Journal Name
Iraqi Journal Of Physics
Satellite image classification using proposed singular value decomposition method
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In this work, satellite images for Razaza Lake and the surrounding area
district in Karbala province are classified for years 1990,1999 and
2014 using two software programming (MATLAB 7.12 and ERDAS
imagine 2014). Proposed unsupervised and supervised method of
classification using MATLAB software have been used; these are
mean value and Singular Value Decomposition respectively. While
unsupervised (K-Means) and supervised (Maximum likelihood
Classifier) method are utilized using ERDAS imagine, in order to get
most accurate results and then compare these results of each method
and calculate the changes that taken place in years 1999 and 2014;
comparing with 1990. The results from classification indicated that

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