Knowledge discovery helps mitigate the shortcomings of classical machine learning, especially those so-called imbalanced, high-dimensional, and noisy data challenges. Adaptive combination of multiple models, voting and other data fusion strategies, and the incorporation of other disparate information fusion methods characterize ensemble learning, which addresses the improvement of a predictive model’s accuracy, stability, and generalization. This paper provides a summary of the important approaches to ensemble learning and their real-world uses, emphasizing challenges and opportunities for future work. This paper also discusses how ensemble learning integrates with emergent areas such as deep learning and reinforcement learning. This paper also describes the most important machine learning methods for predicting heart disease, which include decision trees, support vector machines, artificial neural networks, Naïve Bayes, random forest, and K-nearest neighbors.

This article emphasizes the importance of Small and Medium-Sized Enterprises (SMEs) and large companies in driving economic growth. SMEs are labour-intensive and agile, creating more jobs, while large companies are capital-intensive and rely on technology, having more resources for research and development. In the Gulf Cooperation Council (GCC) region, SMEs contribute significantly to Gross Domestic Product (GDP) and job opportunities, while large companies dominate specific sectors. The research employs a multidisciplinary approach using an extensive literature review to summarize the current literature, highlight the economic impact of SMEs and large companies in GCC, and highlight the importance of large companies in developing local citizens. Policy-makers must consider these differences to integrate these dynamic changes for effective support policies. This study examines the economic impact of SMEs and large companies in the GCC region, providing recommendations to support large businesses. It addresses challenges and opportunities related to employment, household earnings, economic output, and value addition. Promoting the economic impact of SMEs and large companies can lead to sustainable economic growth and development in the GCC region. Also, this article pointed out the importance of large companies and their economic impact in the GCC region; policy recommendations will help the governing bodies in decision-making towards promoting sustainable economic growth.

This study aims to identify the critical factors essential for establishing and enhancing an effective entrepreneurial ecosystem within private universities in the gulf cooperation council (GCC) region. The study follows a cross-sectional quantitative design, focusing on private universities in the GCC. Data were collected from 432 respondents via an online survey using a perception-based questionnaire developed from entrepreneurial university models. The 21 items were measured on a seven-point Likert scale. Both exploratory and confirmatory factor analyses were conducted to assess reliability, validity, and relationships between variables using SPSS and SmartPLS software. Through a rigorous methodological approach, five key factors were identified: ecosystem support, industry partnership, impact evaluation, leadership commitment, and commercialization support. The findings underscore the interconnectedness of these factors, highlighting leadership commitment as a pivotal driver of entrepreneurial activities, which aligns with the existing literature. Additionally, the integration of ecosystem support with commercialization and industry partnerships provides a comprehensive model for fostering sustainable entrepreneurship within universities. This research contributes to both theoretical and practical discussions on the development of entrepreneurial universities, offering valuable insights for university administrators and policymakers. By focusing on leadership, industry collaboration, and regular impact assessments, universities can enhance commercialization efforts, drive innovation, and contribute to economic and social development. Future research could explore the role of government policies in shaping entrepreneurial ecosystems, adding further depth to the model presented.

Access to clean and safe drinking water for all remains a global challenge, mainly for rural populations and areas affected by natural disasters or humanitarian crises. The traditional water quality treatment technologies can work well in laboratory or controlled settings, but they are usually applied under conditions unavailable in these types of conditions. Traditional water quality treatment methods are limited by established infrastructure, expensive operating costs, energy requirements, and the ability to perform in-field water treatment. To improve the barriers of traditional water quality treatment technologies, recently developed scientific discoveries of nanozymes, a new class of nanomaterials with enzyme-like catalytic activity, have shown the ability to decentralise water purification. Nanozymes provide a mechanism for water treatment that does not require the infrastructure or the cost of traditional water quality treatment methods. Also, nanozymes possess extremely high catalytic activity, chemical stability, are inexpensive, and are suitable for a variety of contaminants. This review gives a systematic overview of the development of suitable nanozyme-based portable water purification systems. It shows their catalytic mechanisms, the class of nanozymes used, and the design characteristics related to their working use, also highlighting the developments that consider the specific needs of rural contexts, provide rapid responses to disaster areas, and offer drinking water with reliable, simple, and sustainable apparatus.