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By a thorough bibliometric examination of the area through time, this paper analyses the research landscape of metaverse in education. It is an effort that is focused on the metaverse research trends, academic production and conceptual focus of scientific publications.

The Web of Science (WoS) database was explored for information containing research articles and associated publications that met the requirements. For a thorough analysis of the trend, thematic focus and scientific output in the subject of metaverse in education, a bibliometric technique was used to analyse the data. The bibliometrix package of R software, specifically the biblioshiny interface of R-studio, was used to conduct the analysis.

The analysis of the metaverse in education spanning from 1995 to the beginning of 2023 reveals a dynamic and evolving landscape. Notably, the field has experienced robust annual growth, with a peak of publications in 2022. Citation analysis highlights seminal works, with Dionisio et al. (2013) leading discussions on the transition of virtual worlds into intricate digital cultures. Thematic mapping identifies dominant themes such as “system,” “augmented reality” and “information technology,” indicating a strong technological focus. Surprisingly, China emerges as a leading contributor with significant citation impact, emphasising the global nature of metaverse research. The thematic map suggests ongoing developments in performance and future aspects, emphasising the essential role of emerging technologies like artificial intelligence and virtual reality. Overall, the findings depict a vibrant and multidimensional metaverse in education, poised for continued exploration and innovation.

The study is among the pioneers that provide a comprehensive bibliometric analysis in the area of metaverse in education which will guide the novice researchers to identify the unexplored areas.

Purpose: This chapter aims to review the research literature on the insurance industry and map the emerging research trends in this field through a bibliometric analysis and network visualisation exercise.

Design/methodology/approach: The research literature gathered from the Web of Science (WoS) databases was applied to bibliometric analysis in this article. With the help of Biblioshiny, this research was utilised to identify documents, most prolific institutions, countries, resource titles, and WoS categories in the insurance industry. In addition, bibliometric mapping was used to identify national and institutional collaboration networks.

Findings: The author discovered that the literature had increased drastically in the academic discourse during the last two decades. According to the bibliometric data, developed countries such as the United States and the United Kingdom reign research in this sector. The research highlights the most prominent studies and writers in the insurance field and the evolution of the domain from its inception to the contemporary. It also highlights theoretical disagreements and contradictions between theoretical conceptualisation and empirical measures by presenting the significant concerns in the literature.

Originality/value: This chapter delivers the first comprehensive bibliometric analysis of the insurance sector’s literature production in connection to emerging technology, which will aid researchers and practitioners in better understanding the relationships between themes and outsiders to understand the domain area better. The author makes recommendations for future perspectives study directions and highlights the critical conceptual framework that can build future research. Overall, this research contributes to a better understanding of the insurance industry and offers new perspectives.

Purpose: Health insurance and big data analytics have become increasingly intertwined in recent years, offering both opportunities and challenges for the industry. Thus, the primary aim is to utilize bibliometric analysis for comprehensive literature reviews in health insurance and big data analytics.

Design/methodology/approach: Scopus, chosen for its broad coverage, is utilized to extract 493 manuscripts meeting the inclusion criteria set (year and language) for a 25-year period. The tools employed in the study include VOSViewer and Biblioshiny package (R-programming).

Findings: An emerging trend has been observed in the field of health insurance and big data analytics for 25 years. The US has been observed as the topmost leading country to contribute to the subject under study. The Ministry of Science and Technology of Taiwan is at the top first rank of top leading institutions contributing 20 documents to the field of health insurance and big data analytics. Moreover, thematic mapping and word cloud is done to find the most relevant keywords in the study. Furthermore, co-occurrence analysis revealed the relationship of keywords for health insurance and big data mining.

Implications: The implications of the research extend beyond academic insights and have practical implications for stakeholders involved in healthcare policy, practice, and research.

Originality/Value/Implications: The novelty in the manuscript has been brought in by focusing on one of the many types of insurance, i.e., health. Moreover, big data analytics in relation to health insurance for such a range of time period serves as the original presentation of the work with regards to the matter under study.

The present study aims to undertake an extensive review of scholarly literature by exploring the intersection of the metaverse and education.

The researchers used the relevant documents from the Scopus database to conduct bibliometric analysis. The data were retrieved from 2010 to February 2024. Citation, co-citation and author’s keyword analysis were conducted for bibliometric analysis. The study was performed using VOSviewer and the Biblioshiny app software packages.

The extant literature related to the metaverse and education is presented in the paper. The paper identified four key themes in the literature, i.e. Metaverse and education, Contemporary application of metaverse: a multisectoral perspective, Metaverse: spatial dimensions and concerns and Metaverse: shaping the future of digital interaction. Other information related to the most influential authors, journals and countries concerning metaverse and education is also presented.

The paper studies the gradual evolution of the present research domain over time. The study outlines key areas that have emerged from the literature review, suggesting directions for future research.

Positive experiences with working from home (WFH) during the Corona pandemic (COVID-19) have motivated many employees to continue WFH after the pandemic. However, factors influencing employees' WFH intentions against the backdrop of experiences during pandemic-induced enforced working from home (EWFH) are heterogeneous. This study investigates factors linked to information technology (IT) professionals' WFH intentions.

This mixed-methods study with 92 IT professionals examines the effects of seven predictors for IT professionals' WFH intentions. The predictors are categorized according to the trichotomy of (1) characteristics of the worker, (2) characteristics of the workspace and (3) the work context. Structural equation modeling is used to analyze the quantitative survey data. In addition, IT professionals' responses to six open questions in which they reflect on past experiences and envision future work are examined.

Quantitative results suggest that characteristics of the worker, such as segmentation preference, are influencing WFH intentions stronger than characteristics of the workspace or the work context. Furthermore, perceived productivity during EWFH and gender significantly predict WFH intentions. Contextualizing these quantitative insights, the qualitative data provides a rich yet heterogeneous list of factors why IT professionals prefer (not) to work from home.

Reasons influencing WFH intentions vary due to individual preferences and constraints. Therefore, a differentiated organizational approach is recommended for designing future work arrangements. In addition, the findings suggest that team contracts to formalize working patterns, e.g. to agree on the needed number of physical meetings, can be helpful levers to reduce the complexity of future work that is most likely a mix of WFH and office arrangements.

This study extends literature reflecting on COVID-19-induced changes, specifically the emerging debate about why employees want to continue WFH. It is crucial for researchers and practitioners to understand which factors influence IT professionals' WFH intentions and how they impact the design and implementation of future hybrid work arrangements.

This study aims to empirically examine the impact of perceived risks, optimism and financial literacy on trust and the perceived value of cryptocurrencies. It will also examine the impact of trust on the perceived value of cryptocurrencies.

A quantitative approach is followed. A questionnaire was designed to collect data from 308 respondents in Jordan. The Structural Equation Modeling – Partial Least Squares (SEM-PLS) method was used to evaluate the research model and test hypotheses.

The results of PLS algorithm analysis showed that perceived risks negatively impact the optimism and trust in cryptocurrencies. This study revealed that while financial literacy minimizes the perceived risks, it serves to enhance optimism and improve the perception of the value of cryptocurrencies. Furthermore, the findings of this study show that optimism plays a significant role in trust and perceived value.

This study provides new insights into the literature on cryptocurrencies adoption, blockchain theory, the theory of trust in financial systems, the role of the optimism factor and the perception of the value of cryptocurrencies. It also provides important practical implications for different stakeholders.

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.

The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).

The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.

This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

While COVID-19 temporarily created worldwide immobility, the gradual opening up of borders spurred one of the largest return migration episodes ever, and it continues to this day. Disappearing jobs, decreasing wages, inadequate social protection systems and networks, xenophobia, wage theft and overall uncertainty are among the prominent factors that have influenced this movement. Emigrants from the Gulf-India Migration Corridor were particularly affected by these forces and returned en masse, uncertain of their future. When people come back to their home country after living abroad, particularly due to exogenous shocks, it raises concerns about whether their decision to return was truly voluntary, their ability to adjust to being back home and the long-term effects on their reintegration. Additionally, it is uncertain what kind of impact return migrants have on their home country’s development. In this chapter, the authors examine the recent trend of return migration since the outbreak of COVID-19 and how it affects the Gulf-India corridor. The authors also take a closer look at the state of Kerala through a unique survey conducted by the authors and provide possible future scenarios for emigration in this region, along with recommendations for policy.