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Gait Recognition Based on Deep Learning

      In current generation of technology, a robust security system is required based on biometric trait such as human gait, which is a smooth biometric feature to understand humans via their taking walks pattern. In this paper, a person is recognized based on his gait's style that is captured from a video motion previously recorded with a digital camera. The video package is handled via more than one phase after splitting it into a successive image (called frames), which are passes through a preprocessing step earlier than classification procedure operation. The pre-processing steps encompass converting each image into a gray image, cast off all undesirable components and ridding it from noise, discover difference between two successive images to discover the place motion occurs, converting the result to a binary image, and finally use morphological operation to close holes resulted from the previous steps. The last and most important stage in the system is the classification stage, which depends on deep neural network. The results obtained indicate a high quality of performance and an accuracy of 99.5%.

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Publication Date
Thu Sep 30 2021
Journal Name
Iraqi Journal Of Science
Implementation of K-Nearest Neighbors Algorithm for Predicting Heart Disease Using Python Flask

     Heart disease is a non-communicable disease and the number 1 cause of death in Indonesia. According to WHO predictions, heart disease will cause 11 million deaths in 2020. Bad lifestyle and unhealthy consumption patterns of modern society are the causes of this disease experienced by many people. Lack of knowledge about heart conditions and the potential dangers cause heart disease attacks before any preventive measures are taken. This study aims to produce a system for Predicting Heart Disease, which benefits to prevent and reduce the number of deaths caused by heart disease. The use of technology in the health sector has been widely practiced in various places and one of the advanced technologies is machine lea

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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
Diagnosing Pilgrimage Common Diseases by Interactive Multimedia Courseware

In this study, we attempt to provide healthcare service to the pilgrims. This study describes how a multimedia courseware can be used in making the pilgrims aware of the common diseases that are present in Saudi Arabia during the pilgrimage. The multimedia courseware will also be used in providing some information about the symptoms of these diseases, and how each of them can be treated. The multimedia courseware contains a virtual representation of a hospital, some videos of actual cases of patients, and authentic learning activities intended to enhance health competencies during the pilgrimage. An examination of the courseware was conducted so as to study the manner in which the elements of the courseware are applied in real-time learn

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Reconstruction of Three-Dimensional Object from Two-Dimensional Images by Utilizing Distance Regularized Level Algorithm and Mesh Object Generation

Three-dimensional (3D) reconstruction from images is a most beneficial method of object regeneration by using a photo-realistic way that can be used in many fields. For industrial fields, it can be used to visualize the cracks within alloys or walls. In medical fields, it has been used as 3D scanner to reconstruct some human organs such as internal nose for plastic surgery or to reconstruct ear canal for fabricating a hearing aid device, and others. These applications need high accuracy details and measurement that represent the main issue which should be taken in consideration, also the other issues are cost, movability, and ease of use which should be taken into consideration. This work has presented an approach for design and construc

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Publication Date
Fri Sep 01 2017
Journal Name
International Research Journal Of Engineering And Technology
A POWERFUL AUTOMATED IMAGE INDEXING AND RETRIEVAL TOOL FOR SOCIAL MEDIA Sample

The Internet image retrieval is an interesting task that needs efforts from image processing and relationship structure analysis. In this paper, has been proposed compressed method when you need to send more than a photo via the internet based on image retrieval. First, face detection is implemented based on local binary patterns. The background is notice based on matching global self-similarities and compared it with the rest of the image backgrounds. The propose algorithm are link the gap between the present image indexing technology, developed in the pixel domain, and the fact that an increasing number of images stored on the computer are previously compressed by JPEG at the source. The similar images are found and send a few images inst

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Publication Date
Thu Aug 30 2018
Journal Name
Iraqi Journal Of Science
Best Approximation in Modular Spaces By Type of Nonexpansive Maps

This paper presents results about the existence of best approximations via nonexpansive type maps defined on modular spaces. 

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Publication Date
Fri Dec 08 2023
Journal Name
Iraqi Journal Of Science
Integration Remote Sensing and GIS Techniques to Evaluate Land Use-Land Cover of Baghdad Region and Nearby Areas

The study area of Baghdad region and nearby areas lies within the central part of the Mesopotamia plain. It covers about 5700 Km2. The remote sensing techniques are used in order to produce possible Land Use – Land Cover (LULC) map for Baghdad region and nearby areas depending on Landsat TM satellite image 2007. The classification procedure which was developed by USGS used and followed with field checking in 2010. Land Use-land cover digital map is created depending on maximum likelihood classifications (ML) of TM image using ERDAS 9.2.The LULC raster image is converted to vector structure, using Arc GIS 9.3 Program in order to create a digital LULC map. This study showed it is possible to produce a digital map of LULC and it can be co

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Publication Date
Fri Apr 01 2022
Journal Name
Baghdad Science Journal
Data Mining Techniques for Iraqi Biochemical Dataset Analysis

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB

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Publication Date
Mon Oct 01 2018
Journal Name
Iraqi Journal Of Physics
Classification of brain tumors using the multilayer perceptron artificial neural network

Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect

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Publication Date
Tue Oct 20 2020
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Comparison of Artificial Neural Network and Box- Jenkins Models to Predict the Number of Patients with Hypertension in Kalar

    Artificial Neural Network (ANN) is widely used in many complex applications. Artificial neural network is a statistical intelligent technique resembling the characteristic of the human neural network.  The prediction of time series from the important topics in statistical sciences to assist administrations in the planning and make the accurate decisions, so the aim of this study is to analysis the monthly hypertension in Kalar for the period (January 2011- June 2018) by applying an autoregressive –integrated- moving average model  and artificial neural networks and choose the best and most efficient model for patients with hypertension in Kalar through the comparison between neural networks and Box- Je

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Publication Date
Sun Oct 01 2006
Journal Name
Journal Of The Faculty Of Medicine Baghdad
The Significance Of Maternal Total Serum Homocysteine Level In Iraqi Mothers Who Had Previous Babies With Neural Tube Defects

Background: Neural tube defects (NTDs) are said to be inherited in a multifactorial fashion, i.e. genetic-environmental interaction. Maternal nutritional deficiencies had long been reported to cause NTDs, especially folate deficiency during early pregnancy. More attention had been paid to the exact mechanism by which this deficiency state causes these defects in the developing embryo. The most significant of all researches was that connecting reduced folate and increased homocysteine level in maternal serum on one hand and the risk of developing a NTD baby on the other hand.
Objectives : to determine the significance of homocysteine level in Iraqi mothers who gave birth to babies with NTDs as compare

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