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Residual Network with Attention to Neural Cells Segmentation
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      Many neuroscience applications, including understanding the evolution of the brain, rely on neural cell instance segmentation, which seeks to integrate the identification and segmentation of neuronal cells in microscopic imagery. However, the task is complicated by cell adhesion, deformation, vague cell outlines, low-contrast cell protrusion structures, and background imperfections. On the other hand, existing segmentation approaches frequently produce inaccurate findings. As a result, an effective strategy for using the residual network with attention to segment cells is suggested in this paper. The segmentation mask of neural cells may be accurately predicted. This method is built on U-net, with EfficientNet serving as the encoder's backbone. The attention approach is employed in the detection and segmentation modules to guide the model's attention to the most valuable features. A massive collection of neural cell microscopic images tests the proposed method. According to the findings of the experiments, this technology can accurately detect and segment neuronal cell occurrences with an intersection over the union IoU of 95.47 and a Dice-Coeff of 98.34, which is superior to current state-of-the-art approaches.

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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
Enhancement of conversion efficiency of solar cells with different thickness of fluorescent coloring agent
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Different coating layers of fluorescent agent (FCA) on the solar cells were used. An increase of 35% in the energy conversion efficiency of the solar cell have been obtained. This increase is attributed to the reduction ofthe reflected light, eflection spectra show low values at higher thickness which explained the increase ofthe conversion efficiency with increases of layer thickness.

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Publication Date
Fri Dec 29 2023
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
How Do Parental Factors Increase the Probability for Autism and Attention Deficit Hyperactivity Disorder?
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Two prevalent neurodevelopment disorders in children are attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The fifth version of the Diagnostic and Statistical Manual of Mental Disorders describes autism as a condition marked by limitations in social communication as well as restricted, repetitive behavior patterns. While impulsivity, hyperactivity, and lack of concentration are signs of attention deficit hyperactivity disorder. Boys experience it more frequently than girls do. This study sought for possible factors that put children at risk for autism and attention deficit hyperactivity disorder, and it investigated the association between neurodevelopment disorders in children and parental risk factor i

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Publication Date
Sat Jun 20 2020
Journal Name
Al-bahith Al-a'alami
Creativity in the television advertising message andits effectivenessin attracting the attention of the recipient
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eclaration has become today has an important and active and influential role in the recipient public life، and are concentrated advertising on the creativity component manufacture to attract his attention toward what to be announced from a variety products ، and is dominated by television commercials tempo and imagination، and display them a variety of ways catches the attention and an impressive simulates the their senses of hearing and sight ، to influence in the receiver and the public paid for purchase. Through it crystallization the subject of our research on the importance of creativity in television advertising and effective for attracting the attention of the public towards the receiver advertised products، and in

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Publication Date
Thu Nov 30 2023
Journal Name
Iraqi Journal Of Science
Attention Mechanism Based on a Pre-trained Model for Improving Arabic Fake News Predictions
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     Social media and news agencies are major sources for tracking news and events. With these sources' massive amounts of data, it is easy to spread false or misleading information. Given the great dangers of fake news to societies, previous studies have given great attention to detecting it and limiting its impact. As such, this work aims to use modern deep learning techniques to detect Arabic fake news. In the proposed system, the attention model is adapted with bidirectional long-short-term memory (Bi-LSTM) to identify the most informative words in the sentence. Then, a multi-layer perceptron (MLP) is applied to classify news articles as fake or real. The experiments are conducted on a newly launched Arabic dataset called the Ara

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Publication Date
Wed Jun 07 2023
Journal Name
Journal Of Educational And Psychological Researches
Selective Visual Attention among Students of Colleges of Education At The University Of Mosul
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The research aims to identify the level of selective visual attention among students of the faculties of education at the University of Mosul. To achieve the goal of the research, the researchers chose a stratified random sample of students from the faculties of education at the University of Mosul for the academic year (2020-2021). The sample size was (652) students from the scientific and humanitarian specializations, the second and fourth stages. The researchers developed a test of multiple-choice to measure the selective visual attention, which consisted of (42) items. The results revealed that the students of the faculties of education for human sciences have an appropriate level of selective visual attention. There are statisticall

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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Journal Of Science
Detection and Classification of The Osteoarthritis in Knee Joint Using Transfer Learning with Convolutional Neural Networks (CNNs)
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    Osteoarthritis (OA) is a disease of human joints, especially the knee joint, due to significant weight of the body. This disease leads to rupture and degeneration of parts of the cartilage in the knee joint, which causes severe pain. Diagnosis of this disease can be obtained through X-ray. Deep learning has become a popular solution to medical issues due to its fast progress in recent years. This research aims to design and build a classification system to minimize the burden on doctors and help radiologists to assess the severity of the pain, enable them to make an optimal diagnosis and describe the correct treatment. Deep learning-based approaches, such as Convolution Neural Networks (CNNs), have been used to detect knee OA usin

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Publication Date
Fri May 02 2014
Journal Name
Remote Sensing
Calibrated Full-Waveform Airborne Laser Scanning for 3D Object Segmentation
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Segmentation of urban features is considered a major research challenge in the fields of photogrammetry and remote sensing. However, the dense datasets now readily available through airborne laser scanning (ALS) offer increased potential for 3D object segmentation. Such potential is further augmented by the availability of full-waveform (FWF) ALS data. FWF ALS has demonstrated enhanced performance in segmentation and classification through the additional physical observables which can be provided alongside standard geometric information. However, use of FWF information is not recommended without prior radiometric calibration, taking into account all parameters affecting the backscatter energy. This paper reports the implementation o

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Publication Date
Sat Jan 01 2022
Journal Name
1st Samarra International Conference For Pure And Applied Sciences (sicps2021): Sicps2021
Circularity segmentation for the groups 𝒫𝒮𝓛(2,23) and 𝒫𝒮𝓛(2,29)
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Publication Date
Tue Aug 31 2021
Journal Name
Iraqi Journal Of Science
Characterization of Flexible Multi-Walled Carbon Nanotubes Network Sensor to Freon Gas Detection
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     MWCNTs-OH was used to prepare a flexible gas sensor by deposition as a network on a filter cake using the method of filtration from suspension (FFS). The morphological and structural properties of the MWCNTs network were characterized before and after exposure to Freon gas using FTIR spectra and X-ray diffractometer, which confirmed that the characteristics of the sensor did not change after exposure to the gas. The sensor was exposed to a pure Freon134a gas as well as to a mixture of Freon gas and air with different ratios at room temperature. The experiments showed that the sensor works at room temperature and the sensitivity values increased with increasing operating temperature, to be 58% unt

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Publication Date
Wed Nov 27 2019
Journal Name
Iraqi Journal Of Science
An Improved Segmentation Technique for Early Detection of Exudates of Diabetic Retinopathy Disease
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Diabetic retinopathy (DR) is a diabetes- caused disease that is associated with  leakage of fluid from the blood vessels into the retina, leading to its damage. It is one of the most common diseases that can lead to weak vision and even blindness. Exudates is a clear indication of diabetic retinopathy, which is the main cause of blindness in people with diabetes. Therefore, early detection of exudates is a crucial and essential step to prevent blindness and vision loss is in the analysis of digital diabetic retinopathy systems. This paper presents an improved approach for detection of exudates in retina image using supervised-unsupervised Minimum Distance (MD) segmentation method. The suggested system includes three stages; First, a

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