The next step of digital development is the metaverse, which has the potential to drastically alter how people use technology and expand the range of services available beyond conventional systems that can be accessed online. As the efficiency, performance, and quality of service access reach their peak levels, the focus has shifted to the user experience. Due to this, there is an increasing demand for more involved and thorough customer service, and service providers are willing to increase their present standards. Consumers are genuinely asking for tactile and immersive elements in their digital interfaces, but these features can only be made possible by the metaverse's potentially futuristic subfields of virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR). However, the metaverse may not be widely used due to significant security and privacy issues either from underlying technology or produced by the new digital environment. A variety of fundamental problems, such as scalability and interoperability, can arise in terms of ensuring security for the metaverse because of the metaverse's inherent properties, such as immersive realism, sustainability, and heterogeneity. In this survey, we propose a hypothetical meta‐stack framework to understand the various components in the realm of metaverse and then provide wide‐ranging insights on the most recent development in metaverse realm in the context of cutting‐edge technologies, security vulnerabilities and preventive measures specific to the metaverse and the research challenges pertaining to metaverse.

No abstract

Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta‐learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F‐score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate.