General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k dataset demonstrate superior performance compared to traditional methods, achieving higher accuracy, faster processing speed, and improved boundary preservation. Novelty: The proposed model effectively combines deep learning with fusion techniques, enhancing matting quality while maintaining robustness across various environmental conditions. Implications: These findings highlight the potential of integrating fusion techniques with deep learning for image matting, offering valuable insights for future research in automated image processing applications, including augmented reality, gaming, and interactive video technologies. Highlights: Better Precision: Fusion techniques enhance fine detail preservation. Faster Processing: Lightweight U-Net improves speed and accuracy. Wide Applications: Useful for AR, gaming, and video processing. Keywords: Deep image matting, computer vision, deep learning, fusion techniques, U-Net
The aim of this research is to diagnose the impact of competitive dimensions represented by quality, cost, time, flexibility on the efficiency of e-learning, The research adopted the descriptive analytical method by identifying the impact of these dimensions on the efficiency of e-learning, as well as the use of the statistical method for the purpose of eliciting results. The research concluded that there is an impact of the competitive dimensions on the efficiency of e-learning, as it has been proven that the special models for each of the research hypotheses are statistically significant and at a level of significance of 5%, and that each of these dimensions has a positive impact on the dependent variable, and the research recommended
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Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims
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Abstract
Objective(s): To evaluate high school male students' knowledge about substance use, to determine the effectiveness of the education program on high school mal students' knowledge about substance use.
Methodology: A quasi-experimental (pre-posttest) design was carried out to determine the effectiveness of an educational program on knowledge of high school students about substance use in AL-Kut city. The study was started from 20th h September 2022 to 24th November 2022. The sample was non-probability (purposive sample) sample of (60) student were selected according to the study that are working in Al Kut Education Directo
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The effect of three high temperatures for five exposure periods on the developments of larvae, pupae and adults of Trogoderma granarium (Everts) and their biological performance were investigated. The results revealed that the percent of mortality was increased as the temperature and the exposure period increased, e. g. exposing last instar larvae to 45°C for 6 hrs caused 100% death of this stage, while exposing adults (1-3) days old to the same temperature and exposure time resulted in that these adults did not able to survive more than 24 hrs.; in addition, the results showed that the ability of reproduction of adults was depended on the temperature, duration of exposure and the sex.
The present investigation considers the effect of curing temperatures (30, 40, and 50˚C) and curing compound method on compressive strength development of high performance concrete, and compares the results with concrete cured at standard conditions and curing temperature (21˚C). The experimental results showed that at early ages, the rate of strength development at high curing temperature is greater than at lower curing temperature, the maximum increasing percentage in compressive strength is 10.83% at 50C˚ compared with 21C˚ in 7days curing age. However, at later ages, the strength achieved at higher curing temperature has been less, and the maximum percentage of reduction has been 5.70% at curing temperature 50C˚ compared with 21
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