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.

The economy of Pakistan has faced many challenges due to COVID-19, leading to numerous systemic failures and leaving it struggling to recover. This research aims to shed light on the specific challenges faced by Pakistani textile companies during the pandemic. Comprehensive data was collected from one hundred fifty-three textile managers in Pakistan. Upon examining the impact of COVID-19 on businesses, it has been found that the most pressing issues revolved around working capital and strategies for generating new sales. Interestingly, many of these businesses were well-prepared in the digital realm, readily embracing digital knowledge and seizing opportunities by pivoting to the production of personal protective equipment (PPE) and N95 masks. This study aims to evaluate the early consequences of COVID-19 on Pakistan’s textile industry. Considering the scarcity of research on these challenges and opportunities, our work contributes to a better understanding of the hurdles the textile sector faces. Furthermore, it sets the groundwork for future research in this domain. It provides valuable insights for textile businesses, enabling them to align their strategies with the ever-evolving digital marketing landscape.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

As autonomous vehicles (AVs) revolutionize the global transportation landscape, their implications for emerging economies like Malaysia remain a subject of significant interest. This study delves into the multifaceted world of AV technology, focusing on Malaysia’s unique transportation challenges and opportunities. Through interviews with key stakeholders and experts, the research uncovers valuable insights into AV technology’s awareness, regulatory landscape, integration hurdles, potential benefits, and inclusivity impact in the Malaysian context. The study finds that while AVs hold the promise of improved road safety, reduced traffic congestion, and enhanced environmental sustainability, addressing challenges related to regulation, infrastructure, and public acceptance is imperative for successful integration. Additionally, AV technology has the potential to significantly enhance inclusivity in transportation, benefiting individuals with disabilities. The study underscores the need for holistic policy and infrastructure development to leverage the benefits of AV technology and pave the way for a sustainable and inclusive transportation future in Malaysia.