Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

The widespread adoption of digital technologies in tourism has transformed the data privacy landscape, necessitating stronger safeguards. This study examines the evolving research environment of digital privacy in tourism management, focusing on publication trends, collaborative networks, and social contract theory. A mixed-methods approach was employed, combining bibliometric analysis, social contract theory, and qualitative content analysis. Data from 2004 to 2023 were analyzed using network visualization tools to identify key researchers and trends. The study highlights a significant increase in academic attention after 2015, reflecting the industry's growing recognition of digital privacy as crucial. Social contract theory provided a framework emphasizing transparency, consent, and accountability. The study also examined high-impact articles and the role of publishers like Elsevier and Wiley. The findings offer practical insights for policymakers, industry leaders, and researchers, advocating for ongoing collaboration to address privacy challenges in tourism.

Plant growth-promoting rhizobacteria (PGPR) offer eco-friendly alternatives to chemical fertilizers, promoting sustainable agriculture by enhancing soil fertility, reducing pathogens, and aiding in stress resistance. In agriculture, they play a crucial role in plant growth promotion through the production of agroactive compounds and extracellular enzymes to promote plant health and protection against phytopathogens. In the rhizosphere, diverse microbial interactions, including those with bacteria and fungi, influence plant health by production of antimicrobial compounds. The antagonism displayed by rhizobacteria plays a crucial role in shaping microbial communities and has potential applications in developing a natural and environmentally friendly approach to pest control. The rhizospheric microbes showcase their ecological importance and potential for biotechnological applications in the context of plant-microbe interactions. The extracellular enzymes produced by rhizospheric microbes like amylases, chitinases, glucanases, cellulases, proteases, and ACC deaminase contribute to plant processes and stress response emphasizing their importance in sustainable agriculture. Moreover, this review highlights the new paradigm including artificial intelligence (AI) in sustainable horticulture and agriculture as a harmonious interaction between ecological networks for promoting soil health and microbial diversity that leads to a more robust and self-regulating agricultural system for protecting the environment in the future. Overall, this review emphasizes microbial interactions and the role of rhizospheric microbial extracellular enzymes which is crucial for developing eco-friendly approaches to enhance crop production and soil health.