This research addresses the pervasive issue of traffic congestion during the Hajj, where approximately 250,000 vehicles substantially exacerbate travel times and road accidents, while also escalating pollution levels, thereby adversely affecting public health. Aimed at bolstering the Kingdom's Vision 2030, the study focuses on the incorporation of artificial intelligence (AI) and advanced communication technologies to optimize traffic management in Mecca. Through the innovative deployment of smart cameras and real-time data analytics, the proposed system seeks to predict, manage, and alleviate traffic congestion by providing alternative routes and facilitating smoother vehicular movement. An exploration into the myriad benefits of this AI-integrated system reveals potentials such as enhanced road safety, improved emergency response efficiencies, and elevated air quality, thereby contributing to the overall wellbeing of the community and environment. In addition, the research anticipates that reducing traffic bottlenecks will indirectly invigorate local businesses and augment tourism revenues, aligning with the objectives of enhancing economic prosperity. By advocating a multi-faceted approach to crowd monitoring and management, this study underscores the indispensable role of AI in revolutionizing traffic management strategies, despite the challenges posed by the complexity of real-time data simulation and the unique intricacies of the Hajj.

A wide range of mathematical constructs, theories, and algorithms have been leveraged by cryptographers to secure valuable and sensitive digital images. Among these, image encryption based on scrambling algorithms has been extensively utilized. These scrambling techniques operate on 1D, 2D, and 3D constructs, relying on pixel-level swapping to enhance security. This research introduces a novel scrambling algorithm that employs a row- and column-based scrambling approach. The encryption process begins with an input image, from which a 2D scrambled image is generated. The columns of pixels from the input image are randomly placed into the columns of the scrambled image. Once all columns are inserted, the process is repeated to intensify the scrambling effect. A similar procedure is then applied to the rows of the image. To further enhance security, a diffusion process is applied using an XOR operation between the scrambled image and a random number stream. These random numbers are generated using the 3D Rössler chaotic system, where two chaotic streams contribute to the scrambling effect, and a third stream is used for diffusion. Both machine simulations and exhaustive security analyses demonstrate that the proposed image cipher is highly resistant to various cryptanalytic attacks, including brute-force attacks, differential attacks, and noise and data cropping attacks. Given these promising results, we recommend the proposed method for real-world applications to fully utilize its security potential.

The Hajj is a religious event that attracts a significant number of Muslims from various countries who perform rituals in Mecca, Saudi Arabia. Despite the high volume of pilgrims that typically participate in the event, the number has been reduced in recent years due to the COVID-19 pandemic. The satisfaction of Hajj pilgrims with the quality of hospitality services provided during the event is a crucial factor that must be studied and understood. To achieve this goal, various psychological theories have been employed to explain the phenomenon. The advancement of big data and artificial intelligence has enabled the development of new analytical methodologies for evaluating psychological theories in the hospitality industry. In this study, we present a novel deep learning model that leverages the expectation-confirmation theory to examine the satisfaction of Hajj pilgrims with hospitality services. The model was trained and tested on data obtained from hotel review posts related to the Hajj. Based on our results, the proposed model achieved a high accuracy of 97 % in predicting the satisfaction of Hajj pilgrims. In addition, the results can be used to improve the quality of services provided to pilgrims and enhance their overall experience during the Hajj.

This study explores green work engagement in response to the global demand for sustainability in businesses and the shift toward green-oriented agendas. Specifically, this study aims to examine how green work engagement (GWE) is affected by artificial intelligence awareness (AIA) through job stress (JS) as a mediator. It also explores the moderating roles of technological self-efficacy (TSE) in the AIA→JS relationship and trust in leadership (TIL) in the GWE→JS relationship. A PLS-SEM analysis was conducted on 392 valid replies from full-time employees of five-star hotels in Egypt using WarpPLS 7.0. The findings indicated that artificial intelligence awareness (AIA) negatively affects employees’ green work engagement (GWE) and positively affects job stress (JS). In addition, GWE is negatively affected by JS. Moreover, TSE negatively moderates the AIA→JS relationship, while TIL negatively moderates the JS→GWE relationship. The study also found a significant mediating effect of JS on the AIA→GWE relationship. The study enhances research on AIA’s impact on JS and GWE, addressing a gap in existing empirical studies on the relationship between these elements in hotels. Overall, the study of green work engagement has the potential to be a valuable contribution to the growing field of sustainable business practices.

For over two decades, e-learning has been recognized as a flexible and faster method compared to the other established methods, especially in enhancing knowledge. Concurrently, the expansion of information technology applications, such as mobile applications and Artificial Intelligence (AI), has provided well-grounded foundations for e-learning to be more reachable. In particular, education can be seen as the most beneficial sector of advancements in e-learning. Machine learning is considered a form of personalized learning that could be used to give each student a specific personal experience through which students are directed to gain their own experience. Web and AI-enabled mobile applications can be recognized as one of the most broadly used platforms for e-learning where machine learning technology can be applied to measure many influences and predictions regarding the quality of e-learning, but we cannot ignore the complexities of use. This study shows the role of machine learning in the user’s ability to make use of the course and its contents to measure ease and clarity. Based on a former study shown previously, this paper attempts to pinpoint realities and complexities associated with web and AI-enabled mobile applications by evaluating user preferences. This paper forms the second phase using two user groups (21–30 years) where data were attained using a survey questionnaire to investigate the user preferences when using an application for e-learning. The analysis shows that the future of e-learning has greater potential in web-based applications, as they have more scope for development and improvements compared to mobile applications. The paper concludes with a conceptual framework that works as a machine that stimulates different information and uses e-learning applications that support artificial intelligence techniques. This research provides a solid underpinning for further research into the future of AI-enabled e-learning education and its implication with respect to cost, quality, and usability.