Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.
The global expansion of the Visual Internet of Things (VIoT)'s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.
The conventional electron transport layer (ETL) TiO2 has been widely used in perovskite solar cells (PSCs), which have produced exceptional power conversion efficiencies (PCE), allowing the technology to be highly regarded and propitious. Nevertheless, the recent high demand for energy harvesters in wearable electronics, aerospace, and building integration has led to the need for flexible solar cells. However, the conventional TiO2 ETL layer is less preferred, where a crystallization process at a temperature as high as 450 °C is required, which degrades the plastic substrate. Zinc oxide nanorods (ZnO NRs) as a simple and low-cost fabrication material may fulfil the need as an ETL, but they still suffer from low PCE due to atomic defect vacancy. To delve into the issue, several dopants have been reviewed as an additive to passivate or substitute the Zn2+ vacancies, thus enhancing the charge transport mechanism. This work thereby unravels and provides a clear insight into dopant engineering in ZnO NRs ETL for PSC.