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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
Wed Jul 01 2026
Journal Name
Journal Of Baghdad College Of Dentistry
Dental caries, Mutans Streptococci, Lactobacilli and salivary status of type1 diabetic mellitus patients aged 18-22 years in relation to Glycated Haemoglobin
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Background: diabetic mellitus is one of the serious systemic diseases that may cause general systemic changes, which may be reflected in the oral cavity. The aims of this study were to assess the severity of dental caries, Mutans Streptococci and Lactobacilli in addition to flow rate and pH among uncontrolled and controlled diabetic groups in comparison with non-diabetic control group. Materials and Methods: Study groups consisted of 25 uncontrolled diabetic patients (HbA1c > 7), 25 controlled diabetic patients (HbA1c ? 7), in addition to 25 non-diabetic healthy looking individuals. Their age was (18-22) years from both genders. The diagnosis and recording of dental caries was according to severity of dental caries lesion through the applic

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Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Molecular Identification of Methylorubrum extorquens using PCR-Amplified MxaF Gene Fragments as A Molecular Marker
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  Methylotrophs bacteria are ubiquitous, and they have the ability to consume single carbon (C1) which makes them biological conversion machines. It is the first study to find facultative methylotrophic bacteria in contaminated soils in Iraq. Conventional PCR was employed to amplify MxaF that encodes methanol dehydrogenase enzyme. DNA templates were extracted from bacteria isolated from five contaminated sites in Basra. The gene specific PCR detected Methylorubrum extorquens as the most dominant species in these environments. The ability of M. extorquens to degrade aliphatic hydrocarbons compound was tested at the laboratory. Within 7 days, gas chromatographic (GC) studies of remaining utilize

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Publication Date
Sat Jan 31 2026
Journal Name
International Journal Of Intelligent Engineering And Systems
Low-complexity Deep Learning for Joint Channel-type Identification and SNR Estimation in MIMO-OFDM Using CNN–BRNN with LUT Labels
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Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T

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Publication Date
Mon Jun 30 2025
Journal Name
Indonesian Journal Of Biotechnology
Morphological and molecular identification of an unknown fungal isolate from Al‐Dujail District: A new record of Tulostoma winterhoffii in Iraq
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The Tulostoma genus, known as stalkballs or stalked puffballs, belongs to the Agaricaceae family. This study was designed to identify an unknown fungal species collected from the Al‐Dujail district in Iraq based on morphological examination and molecular analysis of the internal transcribed spacer (ITS) region. Between April and July 2019, samples were collected from garden soil in the Al‐Dujail district, Salah Al‐Din Governorate, Iraq. Morphological characteristics were documented using light microscopy. Genomic DNA was extracted and purified, and the ITS region was amplified using conventional PCR with specific primers. The amplified products were sequenced, and phylogenetic analysis was conducted using MEGA11 software. Morp

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Publication Date
Thu Feb 01 2018
Journal Name
Journal Of Engineering
A Realistic Aggregate Load Representation for A Distribution Substation in Baghdad Network
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Electrical distribution system loads are permanently not fixed and alter in value and nature with time. Therefore, accurate consumer load data and models are required for performing system planning, system operation, and analysis studies. Moreover, realistic consumer load data are vital for load management, services, and billing purposes. In this work, a realistic aggregate electric load model is developed and proposed for a sample operative substation in Baghdad distribution network. The model involves aggregation of hundreds of thousands of individual components devices such as motors, appliances, and lighting fixtures. Sana’a substation in Al-kadhimiya area supplies mainly residential grade loads. Measurement-based

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Publication Date
Mon Dec 02 2024
Journal Name
Al-iraqia Journal Of Scientific Engineering Research
Visible Light Communication System Integrating Road Signs with the Vehicle Network Grid
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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Improvement of Traffic Movement for Roads Network in Al-Kadhimiya City Center
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Numerous regions in the city of Baghdad experience the congestion and traffic problems. Due to the religious and economic significance, Al-Kadhimiya city (inside the metropolitan range of Baghdad) was chosen as study area. The data gathering stage was separated into two branches: the questionnaire method which is utilized to estimate the traffic volumes for the chosen roads and field data collection method which included video recording and manual counting for the volumes entering the selected signal intersections. The stage of analysis and evaluation for the seventeen urban roads, one highway, and three intersections was performed by HCS-2000 software.The presented work plots a system for assessing the level of service

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Publication Date
Mon Mar 11 2019
Journal Name
Baghdad Science Journal
Analysing Iraqi Railways Network by Applying Specific Criteria Using the GIS Techniques
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The railways network is one of the huge infrastructure projects. Therefore, dealing with these projects such as analyzing and developing should be done using appropriate tools, i.e. GIS tools. Because, traditional methods will consume resources, time, money and the results maybe not accurate. In this research, the train stations in all of Iraq’s provinces were studied and analyzed using network analysis, which is one of the most powerful techniques within GIS. A free trial copy of ArcGIS®10.2 software was used in this research in order to achieve the aim of this study. The analysis of current train stations has been done depending on the road network, because people used roads to reach those train stations. The data layers for this st

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Publication Date
Tue Aug 01 2023
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Energy generation by crystalline silicon photovoltaic network per meter square in Iraq
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<span lang="EN-US">Iraqi people have been without energy for nearly two decades, even though their geographic position provides a high intensity of radiation appropriate for the construction of solar plants capable of producing significant quantities of electricity. Also, the annual sunny hours in Iraq are between 3,600 to 4,300 hours which makes it perfect to use the photovoltaics arrays to generate electricity with very high efficiency compared to many countries, especially in Europe. This paper shows the amount of electric energy generated by the meter square of crystalline silicon in the photovoltaic (PV) array that already installed in 18 states in Iraq for each month of the year. The results of the meter-square of PV arr

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Publication Date
Sat Dec 31 2022
Journal Name
International Journal Of Intelligent Engineering And Systems
Dynamic Virtual Network Embedding with Latency Constraint in Flex-Grid Optical Networks
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