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Analyzing Financial Failures: A Study of Iraqi Banks and Key Financial Indicators

This research addresses the financial failures occurring in the Iraqi financial market. An in-depth analysis is conducted on the practices that contribute to financial failure in Iraq. The research objectives and questions focus on the issue of financial failure within the economic context of Iraq. Additionally, an analysis is performed on the key financial factors exacerbating the situation. A literature review highlights secondary data sources that reflect the key financial indicators contributing to failure risks for banks in Iraq. Low contributions to GDP, high cash dependency, and elevated debt levels were identified as prominent key performance indicators (KPIs) in the research, based on both secondary and primary data sources. The research method employs a mixed-method approach, utilizing questionnaires and five interviews to analyze the impact of poor financial performance on the financial failure of banks in Iraq. Data collection is conducted through both primary and secondary sources, increasing the validity and reliability of the conclusions drawn. The data is analyzed using SPSS, with methods including descriptive statistics, regression, and correlations. The significance level is noted to be greater than 0.05, indicating a clear link between poor financial performance and financial failure. In conclusion, the research emphasizes the necessity for both private and government-owned banks to understand the key factors responsible for their success and failure.

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Hermit Crab Optimizer Approach: Harmonics Reduction of Hybrid Renewable Energy Sources

Micro Grids (MGs) have gained a lot of popularity recently because of their benefits in terms of high transmission efficiency and efficient power conversion. However, the nonlinear properties of renewable energy sources (RESs), the increased usage of power electronic devices, and unanticipated fluctuations in load are what lead to the formation of stability issues in the MG. This manuscript presents the optimization approach to improve the performance of microgrid with renewable energy sources (RES). The proposed method is the Hermit Crab Optimizer (HCO) Algorithm. Here, Solar PV and Wind Turbine is utilized as power sources, supported by DC/DC and AC/DC converters for efficient energy conversion. The main objective of proposed technique is to minimize the THD and maximize the efficiency of system. The HCO algorithm is used to optimize the duty cycle of the DC to DC converters within the system. By then, the proposed method's performance is put into practice using the MATLAB platform, and it is contrasted with a number of existing methods, including Salp Swarm Algorithm (SSA), Ant Lion Optimizer (ALO), and Particle Swarm Optimization (PSO). From the result, the THD of proposed method is -17.75. The THD of existing PSO is 17.81465, ALO is -17.81469 and SSA is -17.81545.

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AI-Based Applications Enhancing Computer Science Teaching in Higher Education

The incorporation of Artificial Intelligence (AI) in higher education has gained significant attention as it presents new opportunities to improve the teaching and learning process. This paper aims to analyze how AI applications can support the teaching and learning of computer science in higher education. By reviewing various scientific publications, this paper offers an in-depth analysis of how AI-driven tools and applications have been successfully integrated into computer science courses. Key AI applications considered include intelligent tutoring systems, assessment, performance prediction, academic management, educational innovation, and adaptive learning. These tools have been shown to increase student engagement, provide tailored instruction, offer timely feedback, and enable the scalability of high-quality education. Also, the paper addresses the challenges associated with AI in education, such as diversity of educational contexts, security and data privacy, algorithmic bias, and the importance of faculty preparation. This article emphasizes the transformative potential of AI to enhance computer science education in the context of higher education and identifies mechanisms for research and practice to take full advantage of AI capabilities in designing effective and inclusive learning environments, guided by a comprehensive synthesis of current research and case studies.

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Credit Risk Identification and Prevention Strategies of Small and Medium-sized Banks Based on Big Data Technology

The topic of this analysis is the application of big data technology to improve credit risk identification and prevention measures in SME banks. T¬he research, it highlights credit risk’s crucial role in banking and underscores the problems smaller banks encounter when dealing with this risk due to limited resources and less evolved tools. An extensive review is conducted, showing the progression of credit risk management and big data integration into financial risk management. It discusses the revolutionary aspect of big data in credit risk analysis as well as its practical applications in small and medium-sized banks. The secondary data from Kaggle datasets are used within a quantitative research approach. Regression analysis and hypothesis testing are some of the statistical tools used in EViews to uncover patterns and correlations related to credit risk. The study determines essential factors that affect credit risk, such as borrower credit score, loan amount, interest rate, and employment. It assesses the effectiveness of big data analytics in forecasting and mitigating this risk, focusing on accuracy and model resilience. This implies that the findings show that the borrowers have a moderate level of credit risk, and the traditional financial metrics have minimal influence on the Big Data risk score predictions, which require advanced analytical approaches. Big data technology can take more than traditional credit scoring, giving small and medium-sized banks a more advanced credit risk perspective. Limitations of the research include using a simulated dataset and the range of the analysed variables. Real-world data and variables should be used in future research studies.

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Advancing Security and Efficiency in IoT Healthcare Applications with Challenges Benefits and Latency Reduction Techniques

With the expansion of the Internet of Things (IoT) and cloud computing, conventional reliance on centralized cloud data centres for the storage, analysis, and real-time processing of vast data volumes presents significant challenges. This is particularly applicable to the elevated latency and stringent security demands necessary for real-time IoT healthcare applications. Critical and time-sensitive applications, including e-healthcare, telemedicine, and robotic surgery, necessitate ultra-low latency and stringent security measures. Suboptimal processing, connectivity, and networks impede the performance of these applications. Moreover, conventional cloud designs frequently fail to provide the necessary Quality of Service (QoS) for IoT healthcare systems. Consequently, this essay also explores latency reduction and security improvement techniques in IoT healthcare focusing on the need for information transmission in the respective areas. It wishes to list the first principles for approaches that reduce latency and protect communications, and computational architectures that can operate with such systems. It also explores the features that are crucial for understanding latency and security, as well as comparing several methods of addressing latency reduction and improvement in security alongside their effectiveness. It critically assesses previous approaches, identifies gaps in the literature and emphasizes unanswered questions in the study that may be useful in other works in this field. This research incorporates these findings into propelling concepts relative to IoT device connection and enhancing general paradigms relating to the better employment of healthcare applications. These findings support the need to design future IoT healthcare systems that will be latent sensitive, security-enhanced, and high-performance systems due to emerging characteristics of IoT in healthcare.

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