This study presents a comparative analysis of five widely used machine learning algorithms—Logistic Regression, Support Vector Machines (SVM), Random Forest, Gradient Boosting, and Neural Networks—in the context of business intelligence (BI). The performance of these models was evaluated on both classification and regression tasks, utilizing a comprehensive set of metrics including accuracy, precision, recall, F1 score, AUC-ROC for classification, and R-squared for regression. Results indicate that ensemble models, particularly Random Forest and Gradient Boosting, outperformed other algorithms across both tasks. Random Forest achieved the highest AUC-ROC (96.3%) in classification, while Gradient Boosting led with the highest F1 score (94.2%) and AUC-ROC (97.8%), reflecting its ability to model complex, non-linear relationships. In regression tasks, Gradient Boosting (R² = 0.94) and Random Forest (R² = 0.91) demonstrated superior explanatory power. While Neural Networks (R² = 0.93) performed well, their computational complexity and lack of interpretability pose challenges for certain BI applications. Logistic Regression and SVM, though effective in simpler contexts, were generally outperformed by more complex models. The findings emphasize the importance of selecting the appropriate model based on the business objectives, data characteristics, and computational resources, with ensemble methods being ideal for high-accuracy, complex BI tasks. This study contributes valuable insights for organizations aiming to leverage machine learning for data-driven decision-making and enhances the understanding of algorithmic trade-offs in business intelligence.

The research written by an economist, Omolara Adebimpe Adekanbi is an in-depth comparative analysis of the economic trajectories of Mexico and Nigeria from 1970 to 2018. Leveraging frameworks by Amartya Sen and Paul Collier, the study evaluates the impact of government expenditure on health, education and welfare, foreign direct investment (FDI), external debt, and interest rates on Gross National Income (GNI). It offers a unique methodology to contextualize the effectiveness of policies and provides actionable recommendations for sustainable development across several issues including employment, food and agriculture, conflict and violence as they all impact the level of growth and development. The study's methodology and findings establish it as a vital contribution to the field of development economics.

Fraud within local government agencies poses significant risks to public trust, financial integrity, and effective service delivery. One of the key strategies for mitigating fraud is the establishment of a robust control environment, which includes internal controls, oversight mechanisms, and ethical standards designed to prevent or deter fraudulent activities. This study evaluates the effectiveness of control environments as a deterrent to fraud in local government settings, examining both the structural and behavioral aspects of control mechanisms. By analyzing case studies, internal audit reports, and interviews with public sector officials, the research assesses the strength of control frameworks in identifying vulnerabilities, deterring fraudulent behavior, and ensuring accountability. The findings suggest that while effective control environments can significantly reduce the occurrence of fraud, challenges such as inadequate enforcement, lack of training, and organizational culture remain barriers to optimal effectiveness. The study offers recommendations for enhancing control systems in local government agencies to better prevent fraud and improve overall governance and transparency.

This article analyzes the population density - territorial distribution of the Kashkadarya region and their causes. Along with the population density of the region, the influence of natural and economic factors affecting the location of the population, as well as the landscape features of the region, is described. Also, the problems that have arisen due to the differences in the number of population and the territorial location of the population between the region and its administrative units, as well as proposals and recommendations for their elimination are presented.

There has been much focus toward increasing integration of AI in healthcare generally, with a specific focus on inventory management systems in particular. As more hospitals in the United States public and private side face increasing costs and concerns over cost and length of operation all systems supporting inventory must be made most efficient and AI system offers the US hospitals the opportunity to maintain accurate inventory and hence improve the quality of patient care. But the literature review presents a small number of studies that focus on the effects of these systems as the means to improve healthcare results and organization. In this study, it is proposed that improving inventory management by actioning artificial intelligence in healthcare organizations will result in enhanced patient care and improved inventory accuracy, cost reduction and supply chain efficiency in the United States of America’s healthcare institutions. A cross-sectional, quantitative survey was administrated to 200 healthcare personnel and supply chain managers from different healthcare organizations based on hospitals, clinics and specialty medical centers of the USA. Sample design: Participants were purposefully chosen, who had prior exposure to the AI-induced systems or are aware about workflow of such systems. Respondents’ data was gathered via an online questionnaire and analyzed using SPSS: descriptive analysis, chi-square test, one-way ANOVA, correlation analysis, multiple regression analysis was used to establish the relationships of the study variables: AI systems, healthcare outcomes and supply chain performance.

The results showed that the use of AI in inventory management systems enhances its accuracy in ordering order itself and overall patient care. Separate studies showed that where the management of a facility has deeper insight of the AI system then there will be more operations worked on to improve efficiency or cut costs. Adoption of these pathways among smaller providers including clinics and long-term care facilities was difficult, especially due to cost constraints. An analysis of the multiple regression equation showed that knowledge of AI improved the projection of potential healthcare outcome, creating greater worth and also increases the performance of supply chain. The outcomes of using AI based inventory management systems in the United States seem to have a lot of advantages that may help improve the efficacy of operations in the healthcare system. Issues such as funding and skills needed for implementation, especially in small healthcare organization, continue to slow the uptake of the technology. These existing problems may be solved if more focused investments in the development of comprehensive AI training or systematic solutions are given to enable the healthcare industry to reach the full potential of the application of artificial intelligence.

This comprehensive study evaluates the social obligations of universities recognized as the best-ranked by QS Stars, focusing on their commitment to social responsibility and community engagement. In an era where higher education institutions are increasingly expected to contribute positively to society, this research investigates how these universities define, implement, and assess their social responsibilities. Utilizing a mixed-methods approach, the study combines quantitative analysis of QS Stars metrics with qualitative data obtained from surveys and interviews with university administrators, faculty, and students. Key themes explored include community outreach initiatives, environmental sustainability practices, efforts towards promoting diversity and inclusion, and the ethical frameworks guiding university policies. The findings reveal that while many top-ranked universities exhibit significant dedication to social responsibility, there is considerable variation in the extent and effectiveness of their initiatives. The study concludes with recommendations for enhancing social responsibility practices across these institutions, positioning them as leaders in fostering positive societal impact.