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

The rate of births delivered by cesarean section (CS) has gone up substantially all over the world. Post-cesarean surgical site infection (SSI) is a common cause of maternal morbidity and mortality that results in prolonged period of hospitalization with increased cost and direct health implications, especially in low socioeconomic population, resource- restricted settings, and war- related conditions with internal forced movement. This study was aimed to find incidence of post cesarean section surgical site infection withthe accompanying risk factors.Pregnant ladies admitted to department of obstetrics and gynecology at Medical City Hospital in Baghdad who had undergone CSs were followed up prospectively from first of January 2017 till end

The aim of the current study was to evaluate the knowledge and perception of the fifth stage pharmacy students (college of pharmacy/ University of Baghdad /Iraq) regarding generic medicines. This study is a cross-sectional study carried in a college of pharmacy /University of Baghdad during the period from (November 2018- March 2019). The number of students included in the current study was 168 undergraduate stager pharmacists. A questionnaire was used to collect data of the study. Nearly 86% of the students said that they had heard of generic and brand medicines, and pharmacy was the main source of knowledge regarding generic medicines (66.7%).  About (33.3%) of the respondents agreed that generic medicines are bioequivalent to br

In 2020 one of the researchers in this paper, in his first research, tried to find out the Modified Weighted Pareto Distribution of Type I by using the Azzalini method for weighted distributions, which contain three parameters, two of them for scale while the third for shape.This research compared the distribution with two other distributions from the same family; the Standard Pareto Distribution of Type I and the Generalized Pareto Distribution by using the Maximum likelihood estimator which was derived by the researchers for Modified Weighted Pareto Distribution of Type I, then the Mont Carlo method was used–that is one of the simulation manners for generating random samples data in different sizes ( n= 10,30,50), and in di

Objectives: The study aim was to explore the knowledge, perceptions, and attitudes of Iraqi physicians regarding generic and locally manufactured medicines. Methods: A total of 124 physicians were involved in this cross -sectional study. The convenience sample was collected from five public hospitals in Baghdad. A self-administered questionnaire was distributed and collected in-person. Fisher's Exact Test was used to measure the association between physician years of experience, gender and categorical (perception and knowledge) variables. Results: Most respondent answers regarding the knowledge of generic medicines were incorrect. Only up to one-third of the participants knew that generic medicines are therapeutically eq

Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The

Aspect-based sentiment analysis is the most important research topic conducted to extract and categorize aspect-terms from online reviews. Recent efforts have shown that topic modelling is vigorously used for this task. In this paper, we integrated word embedding into collapsed Gibbs sampling in Latent Dirichlet Allocation (LDA). Specifically, the conditional distribution in the topic model is improved using the word embedding model that was trained against (customer review) training dataset. Semantic similarity (cosine measure) was leveraged to distribute the aspect-terms to their related aspect-category cognitively. The experiment was conducted to extract and categorize the aspect terms from SemEval 2014 dataset.

Because of the vulnerability of the concept of historical cost adopted as a basis for accounting measurement to many of the criticisms in reaction counter to the concept of fair value, the aim of the research is to try to make a comparison between the historical cost and fair value to prove the health and safety of any of the measurement best for the preparation of financial statements and through the state of each of the two study secretary and good financial investment after being diagnosed with a realistic problem is the limitations of the concept of historical cost in the evaluation of assets in spite of the supposed information disclosed in the financial statements compared to appropriate property for the concept of the fair value o