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The metaverse, a burgeoning virtual reality realm, has garnered substantial attention owing to its multifaceted applications. Rapid advancements and widespread acceptance of metaverse technologies have birthed a dynamic and intricate digital landscape. As various platforms, virtual worlds, and social networks within the metaverse increase, there is a growing imperative for a comprehensive analysis of its implications across societal, technological, and business dimensions. Notably, existing review studies have, for the past decade, primarily overlooked a metaverse‐based multidomain approach. A meticulous examination encompassing 207 research studies delves into the technological innovation of the metaverse, elucidating its future trajectory and ethical imperatives. Additionally, the article introduces the term "MetaWarria" to conceptualize potential conflicts arising from metaverse dynamics. The study discerns that healthcare (45%) and education (22%) are pivotal sectors steering metaverse developments, while the entertainment sector (9%) reshapes the corporate landscape. Artificial intelligence (AI) plays a 9% role in enhancing the metaverse's marketing and user experience. Security, privacy, and policy concerns (11%) are addressed due to escalating threats, yielding practical solutions. The analysis underscores the metaverse's profound influence (57%) on the digital realm, a phenomenon accelerated by the COVID‐19 pandemic. The article culminates in contemplating the metaverse's role in future warfare and national security, introducing "MetaWarria" as a conceptual framework for such discussions. This article is categorized under:Application Areas > Health CareApplication Areas > Science and TechnologyFundamental Concepts of Data and Knowledge > Human Centricity and User Interaction [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly to numerous application areas. The stormy expansion of smart devices and their credence for data transfer using wireless mechanics boost their susceptibility to cyberattacks. Consequently, the cybercrime rate is increasing day by day. Hence, the study of IoT security threats and possible corrective measures can benefit researchers in identifying appropriate solutions to deal with various challenges in cybercrime investigation. IoT forensics plays a vital role in cybercrime investigations. This review paper presents an overview of the IoT framework consisting of IoT architecture, protocols, and technologies. Various security issues at each layer and corrective measures are also discussed in detail. This paper also presents the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, and healthcare. The role of advanced technologies like artificial intelligence, machine learning, cloud computing, edge computing, fog computing, and blockchain technology in cybercrime investigation is also discussed. Lastly, various open research challenges in IoT to assist cybercrime investigation are explained to provide a new direction for further research. [ABSTRACT FROM AUTHOR]

An effective machine learning implementation means that artificial intelligence has tremendous potential to help and automate financial threat assessment for commercial firms and credit agencies. The scope of this study is to build a predictive framework to help the credit bureau by modelling/assessing the credit card delinquency risk. Machine learning enables risk assessment by predicting deception in large imbalanced data by classifying the transaction as normal or fraudster. In case of fraud transaction, an alert can be sent to the related financial organization that can suspend the release of payment for particular transaction. Of all the machine learning models such as RUSBoost, decision tree, logistic regression, multilayer perceptron, K-nearest neighbor, random forest, and support vector machine, the overall predictive performance of customized RUSBoost is the most impressive. The evaluation metrics used in the experimentation are sensitivity, specificity, precision, F scores, and area under receiver operating characteristic and precision recall curves. Datasets used for training and testing of the models have been taken from kaggle.com. [ABSTRACT FROM AUTHOR]