Unveiling the Metaverse: A survey of user perceptions and the impact of usability, social influence and interoperability.

This review explores the Metaverse, focusing on user perceptions and emphasizing the critical aspects of usability, social influence, and interoperability within this emerging digital ecosystem. By integrating various academic perspectives, this analysis highlights the Metaverse's significant impact across various sectors, emphasizing its potential to reshape digital interaction paradigms. The investigation reveals usability as a cornerstone for user engagement, demonstrating how social dynamics profoundly influence user behaviors and choices within virtual environments. Furthermore, the study outlines interoperability as a paramount challenge, advocating for establishing unified protocols and technologies to facilitate seamless experiences across disparate Metaverse platforms. It advocates for the adoption of inclusive, ergonomically oriented designs aimed at enhancing user participation. It addresses the ethical and societal challenges posed by the Metaverse, including concerns related to digital harassment, invasive marketing practices, and breaches of privacy. Additionally, the review identifies existing gaps in the literature, particularly regarding the Metaverse's implications for healthcare, its impact on educational outcomes, and the urgent need for empirical data concerning its long-term effects on user psychology and behavior. By providing a comprehensive synthesis of the current understanding of user experiences and challenges within the Metaverse, this paper contributes to the academic dialogue, laying the groundwork for future research initiatives. It aims to steer the development of the Metaverse towards a trajectory that is ethically sound, socially responsible, inclusive, and aligned with societal expectations, thereby fostering a digital realm that upholds the highest standards of integrity and inclusivity.

Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection.

There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance. Therefore, proper selection of relevant features from the set of available features and the removal of irrelevant features will solve these problems. Therefore, to solve the feature selection problem, an improved version of Dragonfly Algorithm (DA) is proposed by combining it with Simulated Annealing (SA), where the improved algorithm named BDA-SA. To solve the local optima problem of DA and enhance its ability in selecting the best subset of features for classification problems, Simulated Annealing (SA) was applied to the best solution found by Binary Dragonfly algorithm in attempt to improve its accuracy. A set of frequently used data sets from UCI repository was utilized to evaluate the performance of the proposed FS approach. Results show that the proposed hybrid approach, named BDA-SA, has superior performance when compared to wrapper-based FS methods including a feature selection method based on the basic version of Binary Dragonfly Algorithm. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42979-021-00687-5.