This study examines the application of agentic artificial intelligence (AI) frameworks within small, medium, and micro-enterprises (SMMEs), highlighting how interconnected autonomous agents improve operational efficiency and adaptability. Using the PRISMA 2020 framework, this study systematically identified, screened, and analyzed 66 studies, including peer-reviewed and credible gray literature, published between 2019 and 2024, to assess agentic AI frameworks in SMMEs. Recognizing the constraints faced by SMMEs, such as limited scalability, high operational demands, and restricted access to advanced technologies, the review synthesizes existing research to highlight the characteristics, implementations, and impacts of agentic AI in task automation, decision-making, and ecosystem-wide collaboration. The results demonstrate the potential of agentic AI to address technological, ethical, and infrastructure barriers while promoting innovation, scalability, and competitiveness. This review contributes to the understanding of agentic AI frameworks by offering practical insights and setting the groundwork for further research into their applications in SMMEs’ dynamic and resource-constrained economic environments.

Vehicle detection has attracted a lot of research attention and has been employed in many areas of life including public safety and security. Accurately monitoring and detecting vehicles on the road in real time is one of the challenging tasks in this area of research. Towards this purpose, computer vision techniques are commonly used, where Convolution Neural Networks (CNN) got special focus. This paper presents an overview of CNN based vehicle detection methods in the context of monocular vision, where a camera is placed at the back of a vehicle to monitor the road situation, as well as an overview of CNN based Deep Convolution Neural Network (DNN) vehicle detection methods in the context of satellite imagery. The recent developments of these CNN based methods are discussed.

In this paper, the secrecy performance of a mixed free space optical (FSO)/radio frequency (RF) integrated satellite-high altitude platform (HAP) relaying networks for terrestrial multiusers with the existence of an eavesdropper is investigated. In this network, FSO is adopted to establish the link between the satellite and HAP for which it experiences Gamma-Gamma distributions under different detection schemes (i.e., heterodyne and intensity modulation direct detection). The transmission between the amplify-and-forward (AF) relaying HAP and terrestrial multiusers is through the RF and is modeled as shadowed-Rician fading distribution. Owning to broadcasting nature of RF link, it is assumed that an eavesdropper attempts to intercept the users’ confidential message, and the eavesdropper link is subjected to Rician distributions. Specifically, the closed-form expression for the system equivalent end-to-end cumulative distribution function is derived by exploiting the Meijer’s G and Fox’s H functions. Based on this expression, the exact closed-form expressions of the system connection outage probability, secrecy outage probability, and strictly positive secrecy capacity are obtained under the different detection schemes at HAP. Moreover, the asymptotic analyze of the system secrecy outage probability is provided to obtain more physical insights. Furthermore, the accuracy of all the derived analytical closed-form expressions is verified through the Monte-Carlo simulations. In addition, the impact of atmospheric turbulence, pointing errors, shadowing severity parameters, and Rician factor are thoroughly evaluated. Under the same system conditions, the results depict that heterodyne detection outperforms the intensity modulation direct detection.