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Postmortem Panoramic Dental Radiography: Human Identification Based on Convolution Neural Network and Contourlet Transform
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Human identification is crucial in forensics for the investigation of large-scale disasters such as fires, epidemics, earthquakes, and tsunamis. Even though biometric identification using panoramic dental radiography (PDR) has been the subject of several studies in the literature, further study remains a necessary and challenging issue. In this research, a human identification system was developed based on a convolutional neural network (CNN) and contour transform (CT). The proposed system was implemented on a total of 1540 PDR from 302 individuals. The preprocessing applied to PDRs for enhancing and taking the Region of Interest (ROI). The features were extracted using CT transform. These features were fused with features extracted from the CNN to perform identification. Various models with different numbers of layers were applied as a try and test for the proposed system. Data augmentation is used to enhance the system's results. The experimental results illustrate that the best accuracy is 98.9% achieved by implementing the PDRs of size 224*224 using 16 layers (VGG16) with data augmentation of batch size 64*64 and 200 epochs. The prediction time of the proposed system to test PDR was just 3.1 sec. per image. The proposed system can be used to generate candidate images for critical issues. It will likely help with criminal investigations and people identification in large-scale disasters.

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Publication Date
Tue Mar 15 2022
Journal Name
Journal Of Baghdad College Of Dentistry
The relationship of implant stability quotient and insertion torque in dental implant stability
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Background: The insertion torque (IT) values and implant stability quotient (ISQ) values are the measurements most used to assess primary implant stability. This study aimed to assess the relationship between ISQ values and IT. Materials and methods: This study included 24 patients with a mean (SD) age of 47.9 (13.64) years (range 25-75 years). The patients received 42 dental implants (DI), 33 in the mandible and 9 in the maxilla. The DI were installed using the motorized method with 35 Ncm torque, When DI could not be inserted to the requisite depth by the motorized method, a hand ratchet was used and the IT was recorded as ˃ 35 Ncm. Implant stability was measured utilizing Osstell® ISQ. The secondary stability was measured after 16

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Publication Date
Thu Jan 01 2009
Journal Name
Computer And Information Science 2009
The Stochastic Network Calculus Methodology
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Home Computer and Information Science 2009 Chapter The Stochastic Network Calculus Methodology Deah J. Kadhim, Saba Q. Jobbar, Wei Liu & Wenqing Cheng Chapter 568 Accesses 1 Citations Part of the Studies in Computational Intelligence book series (SCI,volume 208) Abstract The stochastic network calculus is an evolving new methodology for backlog and delay analysis of networks that can account for statistical multiplexing gain. This paper advances the stochastic network calculus by deriving a network service curve, which expresses the service given to a flow by the network as a whole in terms of a probabilistic bound. The presented network service curve permits the calculation of statistical end-to-end delay and backlog bounds for broad

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Publication Date
Wed Sep 01 2021
Journal Name
Baghdad Science Journal
Isolation and Identification of Fungi from Clinical Samples of Diabetic Patients and Studying the Anti-Fungal Activity of Some Natural Oils on Isolated Fungi
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Isolation of fungi was performed from February to July, 2019. One hundred clinical specimens were collected from King Abdullah Hospital (KAH) Bisha, Saudi Arabia. Samples were collected from twenty patients of different ages (30 - 70 years old) ten males and ten females. The samples were collected from patients with the two types of diabetics. Specimens included blood, hair, nail, oral swabs and skin.  Specimens were inoculated on Sabourauds Dextrose agar containing chloramphenicol. Thirteen fungal species were isolated and identified. The isolated species were: Aspergillus flavus, A. niger, A. terrus, A. nidulans, A. fumigatus, Candida albicans, C. krusei, C. parapsilosis, C. Tropicalis, Curvularia lunata, Fusarium solani, Penicill

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Publication Date
Tue Jan 02 2018
Journal Name
Journal Of Entomology And Zoology Studies
Isolation and identification of Enterococcus fecalis from cow milk samples and vaginal swab from
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The aim of the present study was to isolated the Enterococcus spp. from milk samples of cow and vaginal swabs from aborted women and patient women in Baghdad during September 2016 to april 2017. All 100 milk sample collecting was carried out on California Mastitis Test (CMT) and the positive Percentage of CMT reactions was 5% and the percentage of Enterococcus isolates from mastitic milk was 60% and 30% from nonmasitic milk. The prevalence of Enterococcus spp was 31% of milk samples and the prevalence of Enterococcus spp. Isolates were 67.74% of the isolates of cow milk samples were Enterococcus faecalis, 25.80% was Group D and 6.45% was non groupable while Enterococcus spp. isolates from aborted women samples were 20% and all isolated was

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Publication Date
Wed Jun 01 2022
Journal Name
Baghdad Science Journal
Schultz and Modified Schultz Polynomials for Edge – Identification Chain and Ring – for Square Graphs
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In a connected graph , the distance function between each pair of two vertices from a set vertex  is the shortest distance between them and the vertex degree  denoted by  is the number of edges which are incident to the vertex  The Schultz and modified Schultz polynomials of  are have defined as:

 respectively, where the summations are taken over all unordered pairs of distinct vertices in  and  is the distance between  and  in  The general forms of Schultz and modified Schultz polynomials shall be found and indices of the edge – identification chain and ring – square graphs in the present work.

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Publication Date
Wed Jan 13 2016
Journal Name
University Of Baghdad
Employ Mathematical Model and Neural Networks for Determining Rate Environmental Contamination
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Publication Date
Wed Dec 01 2021
Journal Name
Baghdad Science Journal
Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
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Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file.  In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus,

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Publication Date
Sun Jan 05 2025
Journal Name
Science Journal Of University Of Zakho
DETECTION AND RECOGNITION OF IRAQI LICENSE PLATES USING CONVOLUTIONAL NEURAL NETWORKS
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Due to the large population of motorway users in the country of Iraq, various approaches have been adopted to manage queues such as implementation of traffic lights, avoidance of illegal parking, amongst others. However, defaulters are recorded daily, hence the need to develop a mean of identifying these defaulters and bring them to book. This article discusses the development of an approach of recognizing Iraqi licence plates such that defaulters of queue management systems are identified. Multiple agencies worldwide have quickly and widely adopted the recognition of a vehicle license plate technology to expand their ability in investigative and security matters. License plate helps detect the vehicle's information automatically ra

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Publication Date
Mon Oct 02 2023
Journal Name
Journal Of Engineering
Skull Stripping Based on the Segmentation Models
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Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor

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Publication Date
Fri Nov 11 2022
Journal Name
Al-mansour Journal
Text Cryptography Based on Three Different Keys
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Secure information transmission over the internet is becoming an important requirement in data communication. These days, authenticity, secrecy, and confidentiality are the most important concerns in securing data communication. For that reason, information hiding methods are used, such as Cryptography, Steganography and Watermarking methods, to secure data transmission, where cryptography method is used to encrypt the information in an unreadable form. At the same time, steganography covers the information within images, audio or video. Finally, watermarking is used to protect information from intruders. This paper proposed a new cryptography method by using thre

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