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bsj-4937
Eyewitnesses’ Visual Recollection in Suspect Identification by using Facial Appearance Model
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Facial recognition has been an active field of imaging science. With the recent progresses in computer vision development, it is extensively applied in various areas, especially in law enforcement and security. Human face is a viable biometric that could be effectively used in both identification and verification. Thus far, regardless of a facial model and relevant metrics employed, its main shortcoming is that it requires a facial image, against which comparison is made. Therefore, closed circuit televisions and a facial database are always needed in an operational system. For the last few decades, unfortunately, we have experienced an emergence of asymmetric warfare, where acts of terrorism are often committed in secluded area with no camera installed and possibly by persons whose photos have never been kept in any official database prior to the event. During subsequent investigations, the authorities thus had to rely on traumatized and frustrated witnesses, whose testimonial accounts regarding suspect’s appearance are dubious and often misleading. To address this issue, this paper presents an application of a statistical appearance model of human face in assisting suspect identification based on witness’s visual recollection. An online prototype system was implemented to demonstrate its core functionalities. Both visual and numerical assessments reported herein evidentially indicated potential benefits of the system for the intended purpose.

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Publication Date
Wed Jul 31 2019
Journal Name
Journal Of Educational And Psychological Researches
An Instructional Design According to the constructivism Model for Chemistry Subject and Its Effect on the Life Skill by the Second Intermediate Female Students
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The research aims to identify the effect of teaching-learning design based on the constructivism theory on the life skills of middle stage students. The sample consisted of (60) students from one of the middle schools in Baghdad's governorate. The experimental group and control group consisted of 30 students for each group, the research tool was the life skills scale composed of (78) items. The scale proved its validity and reliability, which was found to be (0.85). The results showed a statistically significant difference in life skills between the two groups of research in favor of the experimental group, which studied the educational design according to models of constructivism theory compared to the usual method of teaching.

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Publication Date
Sat Oct 01 2022
Journal Name
Baghdad Science Journal
Offline Signature Biometric Verification with Length Normalization using Convolution Neural Network
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Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Geological Journal
A Predictive Model for Estimating Unconfined Compressive Strength from Petrophysical Properties in the Buzurgan Oilfield, Khasib Formation, Using Log Data
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Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

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Publication Date
Mon Feb 11 2019
Journal Name
Journal Of Pharmaceutical Sciences And Research
Design of experiments model for optimization of spectrophotometric determination of phenylephrine hydrochloride in pure and pharmaceutical formulations using p-bromanil
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A simple, fast, inexpensive and sensitive method has been proposed to screen and optimize experimental factors that effecting the determination of phenylephrine hydrochloride (PHE.HCl) in pure and pharmaceutical formulations. The method is based on the development of brown-colored charge transfer (CT) complex with p-Bromanil (p-Br) in an alkaline medium (pH=9) with 1.07 min after heating at 80 °C. ‘Design of Experiments’ (DOE) employing ‘Central Composite Face Centered Design’ (CCF) and ‘Response Surface Methodology’ (RSM) were applied as an improvement to traditional ‘One Variable at Time’ (OVAT) approach to evaluate the effects of variations in selected factors (volume of 5×10-3 M p-Br, heating time, and temperature) on

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Publication Date
Fri Oct 01 2021
Journal Name
Saudi Pharmaceutical Journal
Organization factors influencing nurse ability to prevent and detect adverse drug events in public hospitals using a patient safety model
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Publication Date
Mon Jan 01 2024
Journal Name
Communications In Computer And Information Science
Automatic Identification of Ear Patterns Based on Convolutional Neural Network
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Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

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Publication Date
Fri Aug 30 2024
Journal Name
Iraqi Journal Of Science
Isolation and Identification of Enterococcus spp. Resistant to Macrolides Antibiotics
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Gram-positive enterococciare opportunistic and resistant to many antibiotics. This study aimed to investigate the presence of Enterococcus spp. in our community and whether these isolates are resistant to the macrolides class of antibiotics. Fifty isolates from 112 clinical samples were recognized as Enterococcus spp. and confirmed using Vitek-2 system. The current study found that 50/112 (44.6%) represented the total isolates, 38/50 (76%) of which were Enterococcus faecalis, while 12/50 (24%) were Enterococcus faecium, twenty (40%) isolates from root canals and 30 (60%) isolates from urine were isolated. The sensitivity of the enterococcal isolates to various macrolides (erythromycin, azithromycin and clarithromycin) antibiotics wa

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Comparative analysis of deep learning techniques for lung cancer identification
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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

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Publication Date
Sun Sep 07 2008
Journal Name
Baghdad Science Journal
Isolation and identification of fungi which associated with animal's leather
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The study included the investigation of fungi which associated with heavy animal's leather (Cows and Buffalos) and light (Sheep’s and Goats )through different processing stages (raw hides ,dehairing ,pickling,chrome tanned and stainning or finished stages)there were 10 genera and 25 species in addition to sterile fungi associated with animal leathers which included Alternaria ,Aspergillus,Cladosporium,Fusarium, Mucor , Penicillium , Rhizopus , and Trichoderma .Aspergillus and Penicillium have observed in all leather samples and different processing stages, and that the first time isolate two genera Helminthosporium , Stemphylium form leather for staining stage.

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Publication Date
Sun May 01 2022
Journal Name
Journal Of Engineering
The Collapsible Soil, Types, Mechanism, and identification: A Review Study
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Collapsible soil has a metastable structure that experiences a large reduction in volume or collapse when wetting. The characteristics of collapsible soil contribute to different problems for infrastructures constructed on its such as cracks and excessive settlement found in buildings, railways channels, bridges, and roads. This paper aims to provide an art review on collapse soil behavior all over the world, type of collapse soil, identification of collapse potential, and factors that affect collapsibility soil. As urban grow in several parts of the world, the collapsible soil will have more get to the water. As a result, there will be an increase in the number of wetting collapse problems, so it's very important to com

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