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

              In light of the general inadequacy in the performance of the economic units operating in Iraq, and the contemporary developments in all the various sciences, Iraqi economic units have become obligated to use modern technologies applied around the world. Keeping abreast of these developments is done by moving away from traditional methods of evaluating performance and applying approved and accepted methods of evaluating performance. This will lead to an increase in the efficiency and effectiveness of the activities of economic units. In addition, this drives to reduce production costs. Accordingly, this study aims to clarify the application of the balanced scor

The research aims to know the impact of workers values on their performance which is reflected on increasing productivity and improving its quality as well as the organization's progress and success.  Application of this research took place in the General Company Of Electrical Industries; it contained four main pillars; the first involved research methodology، regarding the problem، importance، aim، basic theory، and method of data collection، the second is dedicated to the theoretical framework related to the research basic variables (values and workgroups) the third is assigned to analyze the actual data by using number of statistical methods، such as mathematical medium and standard deviation and also spearman rank

Voice over Internet Protocol (VoIP) is important technology that’s rapidly growing in the wireless networks. The Quality of Service (QoS) and Capacity are two of the most important issues that still need to be researched on wireless VoIP. The main aim of this paper is to analysis the performance of the VoIP application in wireless networks, with respect to different transport layer protocols and audio codec. Two scenarios used in the simulation stage. In the first scenario VoIP with codec G.711 transmitted over User Datagram Protocol (UDP), Stream Control Transmission Protocol (SCTP), and Real-Time Transport Protocol (RTP). While, in the second scenario VoIP with codec G.726 transmitted over UDP, SCTP, and RTP protocols. Network simulator

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach