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This study explores the integration of AI-driven process optimization in Waste-to-Energy (WtE) systems to enhance urban sustainability. The research focuses on designing a gasification-based WtE system, incorporating AI predictive control to optimize energy conversion processes. The AI system adjusts operational parameters in real-time, improving energy conversion efficiency by 25% and reducing carbon emissions by 40%. Additionally, the system's waste-to-energy conversion rate is projected to increase by 20%, and operational costs are expected to decrease by 30%. Data collection and analysis are carried out using advanced sensors to monitor key parameters such as temperature, gas composition, and energy output, which are then processed by machine learning algorithms for predictive analysis. The results show that the AI optimization significantly enhances system performance, offering a sustainable solution for urban waste management. The study highlights the technical and operational challenges of integrating AI into existing WtE systems, including the need for infrastructure upgrades and scalability considerations. It also discusses the socio-economic impacts, including job creation, reduced energy costs, and improved public health. The findings demonstrate the potential of AI-based WtE systems in reducing waste, generating clean energy, and mitigating climate change, positioning them as a viable solution for sustainable urban development.

Artificial Intelligence (AI) is increasingly used in financial accounting to improve decision-making effectiveness. This research analyzes the role of AI in supporting data-driven decision making and identifies challenges in its implementation. Using a qualitative approach with the Systematic Literature Review (SLR) method, this study reviewed 41 relevant articles from national and international journals. The results showed that 28 studies supported the effectiveness of AI in improving financial decision-making by automating transaction recording, enabling algorithm-based predictive analysis, and detecting financial anomalies. AI enables companies to respond faster to market changes, increase transparency of financial reports, and reduce human errors in accounting processes.However, 13 studies highlighted challenges such as technological complexity, limited transparency in decision-making, algorithmic bias, and organizational readiness. In addition, evolving regulations are an obstacle to ensuring optimal use of AI while minimizing ethical and legal risks. The success of AI in financial decision-making depends on infrastructure readiness, regulatory support, and human resource competencies. Without a well-planned strategy, AI may pose new challenges that hinder its effectiveness. Therefore, this study provides insights into the optimal AI implementation strategy to ensure that this technology improves the accuracy and transparency of decision making while maintaining financial accounting accountability.

The rapid development of information and communication technology has driven the need for information services that are more relevant and adaptive to user behaviour. This research aims to integrate data analytics in the study of user behaviour to support the development of effective information services. The dataset used is Kaggle's Online Retail Dataset, which includes sales transaction data of online retail companies in the UK from December 2010 to December 2011. The analysis was conducted through customer segmentation using K-Means Clustering algorithm and predictive analysis with Association Rule Mining. The segmentation results successfully grouped customers into four main clusters, namely loyal customers, potential customers, passive customers, and low-spending customers. Model evaluation showed optimal performance with an accuracy rate of 85%, precision of 82%, recall of 78%, and F1-Score of 80%, and Silhouette Score of 0.62, indicating effective customer segmentation. The findings prove that the application of data analytics can provide deep insights into customer behaviour and support the development of more personalised and adaptive information services. This research is expected to be a reference in designing data-driven information service development strategies in various sectors.

In today’s date Artificial intelligence (AI) has substantially transformed marketing strategies and specifically Viral Marketing by enhancing the content personalization, targeting the audience and real time campaign optimization. The study explored the Artificial Intelligence impact on Viral marketing with a comprehensive review of 20 literatures that highlights the diverse applications of AI such as predictive analytics, natural language processing (NLP) and AI-driven visual content creation. This study employed meta analysis approach to evaluate how effectively AI could boost marketing reach, engagement and return on investment (ROI). The finding of the study indicates a positive correlation between the efficiency of Viral Marketing campaigns and the integration of AI, despite the fact highlighting ethical and transparency. The study concludes with practical suggestions for using AI in Viral marketing in a responsible and efficient manner to enhance its potential while mitigating related dangers. This study also highlights AI’s revolutionary role in changing market dynamics.

This article explores the utilization of Artificial Intelligence (AI) as a tool for enhancing efficiency in public communication within the BANI era (Brittle, Anxious, Nonlinear, Incomprehensible). The aim of this research is to investigate how AI can improve the effectiveness of public communication amidst uncertainty and complexity. The research employs a descriptive qualitative approach with a literature review, analyzing data from relevant journal articles, books, and case studies. The study finds that AI plays a crucial role in addressing system fragility through misinformation detection, reducing public anxiety by providing personalized and responsive information, and managing uncertainty and complexity through predictive analysis and data simplification. The results indicate that AI can enhance the efficiency and clarity of public communication but must be complemented by stringent regulations and ethical considerations to ensure responsible use. With the right approach, AI can be an effective tool in improving public communication in this challenging era.

The aim of the study is to delve into the disclosure of future financial statements and its reflection on the sustainability of the banking sector by extrapolating and reporting the pillars of banking sustainability. It adopting a mechanism at a high level in the country to promote data technology and increase support for its resources in order to provide an appropriate structure for the development of the banking and financial sector by providing appropriate information at the right time and expanding the preparation of future studies and research and linking them to many variables that are considered influences faced by the Iraqi financial market in the contemporary business environment for providing a suitable environment in the capital market by supporting the banking and financial sector by means of modern technology and obliging companies within the sectors. The study's goal was accomplished by using the content analysis method for the annual reports of banking units listed in the Iraqi financial market for the years 2020–2023. This was done after developing an indicator to gauge future financial statements' level of disclosure in compliance with the Iraqi Financial Market Law and the disclosure guidelines issued in accordance with it, as well as the fact that the index is being used in the country for the first time. The study draws several conclusions, the most important of which being that , One of the most notable recommendations reached by the study is to require the banking units listed in the Iraqi financial market to display future financial statements in order to ensure the achievement of sustainability supporting the banking sector within the annual reports. The level of disclosure of future financial statements varied in the financial reports of the banking units listed in the Iraqi financial market, where the average disclosure was high with a positive moral relationship to the market financial value.

The development of digital technology, particularly Artificial Intelligence (AI), has brought significant changes to various industrial sectors, including accounting. In recent years, AI has become an essential tool used in various business applications, such as data processing, predictive analysis, and automated financial reporting. This technology can automate routine tasks like data entry and account reconciliation, which previously required human intervention, thereby increasing efficiency and reducing potential human errors. However, the application of AI in accounting has sparked discussions about the future of this profession. While AI can automate many aspects of accounting, it cannot fully replace human accountants, especially in areas requiring data interpretation, strategic decision-making, and ethical considerations. The webinar conducted for this study explored the implications of AI in accounting, highlighting both the opportunities and challenges of integrating AI into accounting practices. The findings emphasize the importance of adaptive accounting education and the need for continuous professional development to prepare future accountants to work in an increasingly digital and automated environment. Therefore, the future of the accounting profession will be determined by how AI and human accountants can work together to achieve the best outcomes, maintaining high ethical and professional standards.

Big data has revolutionized the landscape of natural sciences by providing extensive datasets that enable deeper insights and more accurate predictions. However, effectively analyzing such vast and complex data requires optimized machine learning algorithms tailored to specific applications. This study focuses on enhancing the performance of machine learning models in big data analysis for applications in natural sciences. The research aims to identify key optimization techniques, including feature selection, hyperparameter tuning, and algorithm customization, to improve model accuracy and computational efficiency. A combination of supervised and unsupervised learning approaches was applied to real-world datasets in fields such as climate science, genomics, and ecology. The findings demonstrate significant improvements in predictive accuracy and processing speed, highlighting the potential of optimized machine learning techniques in solving complex problems in natural sciences. The implications of this research extend to more efficient resource utilization and improved decision-making in scientific exploration and environmental management.

The presence of artificial intelligence (AI) technology has revolutionized efforts to protect data in offices. The challenges organizations face in maintaining information security are becoming increasingly complex as technology advances, but the benefits of AI provide an effective solution to optimize information security. In this summary, we discuss the challenges and benefits of AI in office data protection, focusing on enhancing information security.The first challenge is the increasing complexity of cyber attacks. Attackers are constantly looking for new vulnerabilities and using more sophisticated attack techniques to breach security systems. Adequate data protection is needed to address this challenge and prevent unauthorized access to critical company information. Additionally, a lack of knowledge about managing and monitoring security systems is a major challenge for many businesses.However, the use of AI to protect office data offers several advantages. First, AI's ability to quickly and accurately detect security threats aids in early detection of cyber attacks. Predictive analysis supported by AI can also detect dangerous patterns and prevent attacks before they occur. Furthermore, using AI to automate security processes can optimize operational efficiency and accountability for events that occur. AI-based security can significantly reduce the risk of data breaches and cyber attacks.The use of AI technology in office data protection is not only a supportive tool but also an innovative and efficient solution to address increasingly complex challenges in information security. We hope this overview provides a deeper understanding of the challenges and benefits of AI in protecting office data, as well as efforts to optimize information security in this digital era.