Digital currencies represent a new, innovative means of exchange, utilizing the internet to simplify and enhance online transactions, which brings a revolution to the economy. This research investigated the acceptance predictors of digital currency among the people of the United Arab Emirates (UAE). To achieve this goal, I surveyed a sample of 181 UAE residents, aiming to predict the intention to use digital currency. The researcher argues that perceived usefulness, perceived trust, social influence, and perceived ease of use are significant determinants of citizens' intention to use digital currency, and perceived ease of use and perceived usefulness mediate the relationship between awareness and intention to use. Also, this paper reveals the factors affecting digital currency acceptance in the UAE, representing differences and similarities regarding global acceptance. The results extend existing models of digital currency acceptance, providing governments, policymakers, and IT professionals with understanding of how acceptance is developed in this context.

The digital technologies such as internet play a crucial role in the management of operations of organizations in both public and private sectors. Such technologies support the implementation of effective digital business strategies. By reviewing the extant literature, this paper aims to identify factors that influence the intention to use digital technologies in order to develop a theoretical model which is then tested empirically using the PLS-SEM approach. While many studies have focused solely on the importance of social influence, perceived usefulness, perceived ease of use, awareness, perceived trust in technology, perceived trust in government, perceived cost, and perceived risk, this article brings them together to explain their linkage, and quantifies the relationship. This study is the first empirical attempt to explore the factors influence e-government services adoption in the UAE. Most specifically, this article emphasizes the role of social influence, perceived ease to use, and perceived trust in technology as the important determinants of the intention to digital technology adoption. The paper expands the traditional discussion by incorporating six variables, in addition to Davis's (1989) the perceived ease to digital technologies use and perceived usefulness, in a model that acts as facilitator or barrier in the intention to use digital technologies. This article helps practitioners to understand of which factors should be given emphasis in enhancing the intention to use digital technologies. The model developed in this paper is not only a response to the need to understand what causes the variation in the intention to use digital technologies from the operation management perspective, it is also a response to practitioner needs to use an appropriate construct to ensure the effective operation and use of the digital technologies in e-government services. The paper will help to identify the key

The end-users’ acceptance of electronic government applications is crucial for the effective delivery of public services. This study intends to investigate the factors that influence the end-users' acceptance of smart-government services.This paper identifies key determinants of the end-users’ acceptance of smart government services in the UAE to develop a theoretical model which is tested empirically, using the partial least squares structural equation modelling. This study examines the relationship among the factors that influence the adoption of smart government applications, along with the moderation effects of gender, age, and experience of the end-users on this linkage. The paper reveals that performance expectancy is the strongest factor influencing adoption of smart government, followed by trust in government, effort expectancy, and social influence. The Multi Group Analysis is used to test the moderation effects of the gender, age, and smart service use experience of the end-users on the relationship between the factors that influence smart-government service adoption.

Graphical-design-based symptomatic techniques in pandemics perform a quintessential purpose in screening hit causes that comparatively render better outcomes amongst the principal radioscopy mechanisms in recognizing and diagnosing COVID-19 cases. The deep learning paradigm has been applied vastly to investigate radiographic images such as Chest X-Rays (CXR) and CT scan images. These radiographic images are rich in information such as patterns and clusters like structures, which are evident in conformance and detection of COVID-19 like pandemics. This paper aims to comprehensively study and analyze detection methodology based on Deep learning techniques for COVID-19 diagnosis. Deep learning technology is a good, practical, and affordable modality that can be deemed a reliable technique for adequately diagnosing the COVID-19 virus. Furthermore, the research determines the potential to enhance image character through artificial intelligence and distinguishes the most inexpensive and most trustworthy imaging method to anticipate dreadful viruses. This paper further discusses the cost-effectiveness of the surveyed methods for detecting COVID-19, in contrast with the other methods. Several finance-related aspects of COVID-19 detection effectiveness of different methods used for COVID-19 detection have been discussed. Overall, this study presents an overview of COVID-19 detection using deep learning methods and their cost-effectiveness and financial implications from the perspective of insurance claim settlement.

This concept paper addresses specific challenges identified in the UN 2030 Agenda Sustainable Development Goals (SDG) as well as the National Health Policy of India (NHP-India) and the Ministry of Health Policy of UAE (MHP-UAE). This policy calls for a digital health technology ecosystem. SDG Goal 1 and its related objectives are conceptualized which serves as the foundation for Virtual Consultations, Tele-pharmacy, Virtual Storage, and Virtual Community (VCom). SDG Goals 2 and 3 are conceptualized as Data Management & Analytical (DMA) Architecture. Individual researchers and health care professionals in India and the UAE can use DMA to uncover and harness PHC and POC data into practical insights. In addition, the DMA would provide a set of core tools for cross-network initiatives, allowing researchers and other users to compare their data with DMA data. In rural, urban, and remote populations of the UAE and India, the concept augments the PHC system with ICT-based interventions. The ICT-based interventions may improve patient health outcomes. The open and flexible design allows users to access various digital materials. Extendable data/metadata format, scalable architecture for petabyte-scale federated discovery. The modular DMA is designed using existing technology and resources. Public health functions include population health assessment, policy development, and monitoring policy implementation. PHC and POC periodically conduct syndromic surveillance to identify population risk patterns. In addition, the PHC and POC deploy medical and non-medical preventive measures to prevent disease outbreaks. To assess the impact of social and economic factors on health, epidemiologists must first understand diseases. Improved health due to compliance with holistic disease treatment plans and access to scientific health information.