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Smart Doctor: Performance of Supervised ART-I Artificial Neural Network for Breast Cancer Diagnoses
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Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
Measuring Service Delivery Orientation For Doctor and patients Perspective Experimental Study in Numan General Hospital
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This research aims to Measurement provide the service from Two perspectives The first perspective Service Provider (doctors) and the second recipient of the service (patients) in Numan General Hospital, and represented the research problem in perceptions of medical staff in the hospital assigned to them responsibility by providing superior services satisfy customers, and how they maintained ready to assist customers and provide services that exceed their perceptions of these services through the use of the developer scale by (Frimpong and Wilson, 2012), includes orientation to provide the service scale four dimensions (Internal cooperative behaviors, service Competence, Service Responsiveness and Enhanced service) and includes do

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Publication Date
Thu Oct 01 2015
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Prevalence of BRCA1 Oncogen Expression in Breast Cancer Specimens of Patients with Positive Family History
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Background: Breast cancer is the leading female cancer worldwide and in Iraq .Some mutations, particularly in BRCA1, significantly increase the risk of the disease.
Objectives: To demonstrate the frequency of BRCA1 in a group of high risk women with “positive family history’’ of breast cancer; correlating the immune expression of BRCA1 with some parameters of known prognostic significance.
Patients and Methods: Eighty-two female patients diagnosed with breast cancer (50 familial and 32 non familial) were included in the study .The mean age of the patients was 48.07. Immunohistochemistry was performed to assess the BRCA1 oncogene expression, Estrogen Receptor (ER), Progesterone Receptor (PR), Her 2 neu contents of the tumors.<

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Publication Date
Sun Apr 04 2010
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Iraqi Breast Cancer: A Review on Patients' Demographic Characteristics and Clinico-Pathological Presentation.
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Background: Breast Cancer is the commonest type of malignancy in Iraq. The Iraqi Cancer Registry displays an obvious trend for the disease to affect younger women with advanced stages at the time of presentation. This report presents a review on the main demographic characteristics and clinicopathological parameters in Iraqi patients diagnosed with breast cancer.
Patients & Methods: The study was carried out on 721 out of a total of 5044 patients (14.3%) who complained of palpable breast lumps that were diagnosed as cancer. The procedure for tumor nuclear DNA Ploidy assessment was performed by means of Image Cytometry. Immuno-cytochemical and  histochemical assays were applied for the determination of

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Publication Date
Sun Apr 02 2023
Journal Name
Mathematical Modelling Of Engineering Problems
Traffic Classification of IoT Devices by Utilizing Spike Neural Network Learning Approach
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Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

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Publication Date
Wed Mar 23 2022
Journal Name
Modern Sport
Using Artificial intelligence to evaluate skill performance of some karate skills
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Human beings are starting to benefit from the technology revolution that witness in our time. Where most researchers are trying to apply modern sciences in different areas of life to catch up on the benefits of these technologies. The field of artificial intelligence is one of the sciences that simulate the human mind, and its applications have invaded human life. The sports field is one of the areas that artificial intelligence has been introduced. In this paper, artificial intelligence technology Fast-DTW (Fast-Dynamic Time Warping) algorithm was used to assess the skill performance of some karate skills. The results were shown that the percentage of improvement in the skill performance of Mai Geri is 100%.

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Publication Date
Tue Jan 02 2018
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Correlation between the histopathological grade and size of breast cancer with axillary lymph node involvement
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Background: Breast cancer account for 29% of all newly diagnosed cancer in female and is responsible for 14% of cancer related deaths in women. Breast cancer is basically detected either during a screening tests, before symptoms have appeared, or after a woman notices a mass. Overall risk doubles each decade until the menopause, when the increase slows down or remains stable.
Objective: to find the correlation between the tumor size and grade and involvement of axillary lymph node.
Patients and methods: a continuous prospective study of 50 patients from 1st January 2016 to 1st January 2017 in Baghdad teaching hospital at 1st surgical floor, where almost all patients with breast cancer operated on by modified radical mastectomy and

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Publication Date
Tue Feb 01 2022
Journal Name
Int. J. Nonlinear Anal. Appl.
Finger Vein Recognition Based on PCA and Fusion Convolutional Neural Network
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Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network

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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption
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A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

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Publication Date
Tue May 01 2012
Journal Name
Iraqi Journal Of Physics
Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features
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Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti

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Publication Date
Tue Jan 02 2018
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Breast Cancer Subtypes among Iraqi Patients: Identified By Their ER, PR and HER2 Status
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Background: Breast cancer ranks the first among the Iraqi population since three decades and is currently forming a major public health problem being the second cause of death women. Novel management of breast cancer depends upon precise evaluation of their molecular subtypes; identified by Hormone (Estrogen and Progesterone) receptors and HER2 contents of the primary tumor.
Objective: To assess the rates of the different molecular breast cancer subtypes in the examined tissue specimens belonging to females diagnosed with breast cancer in Iraq; correlating the findings with those reported in the literature at the regional and global levels.
Patients and Methods: This retrospective study documented the findings of tissue biopsy exam

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