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

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance

Microorganisms establish both structural and functional construction in the marine environment, despite scientific advances, the identification of marine bacterial species is still considered as a common challenge in microbiology. Nevertheless, the present study aims to make an effort, although it seems modest, but it could establish a basis for studying the bacterial diversity in the Iraqi marine area, because of what this aspect entails of the poverty of studies related to this aspect in the studied area. The current results show the marine studied area are classified within worming area, where the average temperature ranged from 23.17 to 26.17 ºC. The recorded number of bacteria was increased with temperature increasing (0.210, 0.250

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

The work in this paper involves the planning, design and implementation of a mobile learning system called Nahrain Mobile Learning System (NMLS). This system provides complete teaching resources, which can be accessed by the students, instructors and administrators through the mobile phones. It presents a viable alternative to Electronic learning. It focuses on the mobility and flexibility of the learning practice, and emphasizes the interaction between the learner and learning content. System users are categorized into three categories: administrators, instructors and students. Different learning activities can be carried out throughout the system, offering necessary communication tools to allow the users to communicate with each other

Abstract:
      This research emerged due to the needs of Iraqi social sector for diagnosing the problems ,finding the appropriate solutions,and exploiting the social opportunities to solve these problems .The research problem focused on raising the following question: "Were Iraqi Managers in the Ministry of Labor and Social Affairs able to use their qualifications as social entrepreneurs in the ministry to improve the quality of life of the disadvantaged groups?", In light of that, the importance and objectives of the study were determined, and this research derives its importance from trying to address social problems by measuring the degree of meeting the subjective and objective needs of the custo

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

          Purpose  :This study seeks to provide the point of view stands on the marketing performance of the banks in the context of Dynamic Equilibrium  of marketing. It aims to develop a prototype of measuring marketing performance under the umbrella of marketing dynamic prototype, embodied of the strategic alliance and competition, and the  laws and legislation, and to explore any of the dimensions more effective in achieving performance.  This study aims to discusses the importance of the Dynamic Equilibrium  of marketing and it role in achieving the marketing performance <